Merge branch 'master' of https://github.com/erwincoumans/bullet3

370 files changed, 72064 insertions, 23927 deletions

diff --git a/.style.yapf b/.style.yapf
new file mode 100644
index 000000000..5e7043e0f
--- /dev/null
+++ b/.style.yapf
@@ -0,0 +1,5 @@
+[style]
+based_on_style = google
+column_limit = 99
+indent_width = 2
+
diff --git a/Extras/Serialize/HeaderGenerator/blenderGenerate.py b/Extras/Serialize/HeaderGenerator/blenderGenerate.py
index e615fcc28..63c41804e 100644
--- a/Extras/Serialize/HeaderGenerator/blenderGenerate.py
+++ b/Extras/Serialize/HeaderGenerator/blenderGenerate.py
@@ -1,6 +1,5 @@
 import dump
 
-
 header = """/* Copyright (C) 2006 Charlie C
 *
 * This software is provided 'as-is', without any express or implied
@@ -27,27 +26,30 @@ dtList = dump.DataTypeList
 out = "../BlenderSerialize/autogenerated/"
 spaces = 4
 
+
 def addSpaces(file, space):
-	for i in range(0, space):
-		file.write(" ")
+  for i in range(0, space):
+    file.write(" ")
+
 
 def write(file, spaces, string):
-	addSpaces(file, spaces)
-	file.write(string)
+  addSpaces(file, spaces)
+  file.write(string)
+
 
 ###################################################################################
-blender = open(out+"blender.h", 'w')
+blender = open(out + "blender.h", 'w')
 blender.write(header)
 blender.write("#ifndef __BLENDER_H__\n")
 blender.write("#define __BLENDER_H__\n")
 for dt in dtList:
-	blender.write("#include \"%s.h\"\n"%dt.filename)
+  blender.write("#include \"%s.h\"\n" % dt.filename)
 
 blender.write("#endif//__BLENDER_H__")
 blender.close()
 
 ###################################################################################
-blenderC = open(out+"blender_Common.h", 'w')
+blenderC = open(out + "blender_Common.h", 'w')
 blenderC.write(header)
 blenderC.write("#ifndef __BLENDERCOMMON_H__\n")
 blenderC.write("#define __BLENDERCOMMON_H__\n")
@@ -63,49 +65,43 @@ blenderC.write(strUnRes)
 
 blenderC.write("namespace Blender {\n")
 for dt in dtList:
-	write(blenderC, 4, "class %s;\n"%dt.name)
+  write(blenderC, 4, "class %s;\n" % dt.name)
 
 blenderC.write("}\n")
 blenderC.write("#endif//__BLENDERCOMMON_H__")
 blenderC.close()
 
-
 for dt in dtList:
-	fp = open(out+dt.filename+".h", 'w')
-	
-	fp.write(header)
-	strUpper = dt.filename.upper()
-	
-	fp.write("#ifndef __%s__H__\n"%strUpper)
-	fp.write("#define __%s__H__\n"%strUpper)
-	fp.write("\n\n")
-	
-	
-	fp.write("// -------------------------------------------------- //\n")
-	fp.write("#include \"blender_Common.h\"\n")
+  fp = open(out + dt.filename + ".h", 'w')
 
-	for i in dt.includes:
-		fp.write("#include \"%s\"\n"%i)
+  fp.write(header)
+  strUpper = dt.filename.upper()
 
-	fp.write("\nnamespace Blender {\n")
-	fp.write("\n\n")
+  fp.write("#ifndef __%s__H__\n" % strUpper)
+  fp.write("#define __%s__H__\n" % strUpper)
+  fp.write("\n\n")
 
-	addSpaces(fp,4)
-	fp.write("// ---------------------------------------------- //\n")
+  fp.write("// -------------------------------------------------- //\n")
+  fp.write("#include \"blender_Common.h\"\n")
 
+  for i in dt.includes:
+    fp.write("#include \"%s\"\n" % i)
 
-	write(fp, 4, "class %s\n"%dt.name)
+  fp.write("\nnamespace Blender {\n")
+  fp.write("\n\n")
 
-	write(fp, 4, "{\n")
-	write(fp, 4, "public:\n")
-	for i in dt.dataTypes:
-		write(fp, 8, i+";\n")
+  addSpaces(fp, 4)
+  fp.write("// ---------------------------------------------- //\n")
 
+  write(fp, 4, "class %s\n" % dt.name)
 
-	write(fp, 4, "};\n")
-	fp.write("}\n")
-	fp.write("\n\n")
-	fp.write("#endif//__%s__H__\n"%strUpper)
-	fp.close()
+  write(fp, 4, "{\n")
+  write(fp, 4, "public:\n")
+  for i in dt.dataTypes:
+    write(fp, 8, i + ";\n")
 
-	
\ No newline at end of file
+  write(fp, 4, "};\n")
+  fp.write("}\n")
+  fp.write("\n\n")
+  fp.write("#endif//__%s__H__\n" % strUpper)
+  fp.close()
diff --git a/Extras/Serialize/HeaderGenerator/bulletGenerate.py b/Extras/Serialize/HeaderGenerator/bulletGenerate.py
index 90d616c56..5f1515d72 100644
--- a/Extras/Serialize/HeaderGenerator/bulletGenerate.py
+++ b/Extras/Serialize/HeaderGenerator/bulletGenerate.py
@@ -2,8 +2,6 @@ import sys
 sys.path.append(".")
 import dump
 
-
-
 header = """/* Copyright (C) 2011 Erwin Coumans & Charlie C
 *
 * This software is provided 'as-is', without any express or implied
@@ -30,23 +28,25 @@ dtList = dump.DataTypeList
 out = "autogenerated/"
 spaces = 4
 
+
 def addSpaces(file, space):
-	for i in range(0, space):
-		file.write(" ")
+  for i in range(0, space):
+    file.write(" ")
+
 
 def write(file, spaces, string):
-	addSpaces(file, spaces)
-	file.write(string)
+  addSpaces(file, spaces)
+  file.write(string)
+
 
 ###################################################################################
-blender = open(out+"bullet.h", 'w')
+blender = open(out + "bullet.h", 'w')
 blender.write(header)
 blender.write("#ifndef __BULLET_H__\n")
 blender.write("#define __BULLET_H__\n")
 #for dt in dtList:
 #	blender.write("struct %s;\n"%dt.filename)
 
-
 ###################################################################################
 
 blender.write("namespace Bullet {\n")
@@ -61,26 +61,25 @@ typedef struct bInvalidHandle {
 blender.write(strUnRes)
 
 for dt in dtList:
-	write(blender, 4, "class %s;\n"%dt.name)
-
+  write(blender, 4, "class %s;\n" % dt.name)
 
 for dt in dtList:
-		
-	strUpper = dt.filename.upper()
-	
-	blender.write("// -------------------------------------------------- //\n")
-
-	write(blender, 4, "class %s\n"%dt.name)
-
-	write(blender, 4, "{\n")
-	write(blender, 4, "public:\n")
-	for i in dt.dataTypes:
-		write(blender, 8, i+";\n")
-
-	write(blender, 4, "};\n")
-	
-	blender.write("\n\n")
-	
+
+  strUpper = dt.filename.upper()
+
+  blender.write("// -------------------------------------------------- //\n")
+
+  write(blender, 4, "class %s\n" % dt.name)
+
+  write(blender, 4, "{\n")
+  write(blender, 4, "public:\n")
+  for i in dt.dataTypes:
+    write(blender, 8, i + ";\n")
+
+  write(blender, 4, "};\n")
+
+  blender.write("\n\n")
+
 blender.write("}\n")
 blender.write("#endif//__BULLET_H__")
 blender.close()
diff --git a/build3/cmake/FindLibPython.py b/build3/cmake/FindLibPython.py
index d4384da4e..580feee3a 100644
--- a/build3/cmake/FindLibPython.py
+++ b/build3/cmake/FindLibPython.py
@@ -15,11 +15,11 @@ print("exec_prefix:%s" % sys.exec_prefix)
 print("major_version:%s" % str(sys.version_info[0]))
 print("minor_version:%s" % str(sys.version_info[1]))
 print("patch_version:%s" % str(sys.version_info[2]))
-print("short_version:%s" % '.'.join(map(lambda x:str(x), sys.version_info[0:2])))
-print("long_version:%s" % '.'.join(map(lambda x:str(x), sys.version_info[0:3])))
+print("short_version:%s" % '.'.join(map(lambda x: str(x), sys.version_info[0:2])))
+print("long_version:%s" % '.'.join(map(lambda x: str(x), sys.version_info[0:3])))
 print("py_inc_dir:%s" % distutils.sysconfig.get_python_inc())
 print("site_packages_dir:%s" % distutils.sysconfig.get_python_lib(plat_specific=1))
-for e in distutils.sysconfig.get_config_vars ('LIBDIR'):
-    if e != None:
-        print("py_lib_dir:%s" % e)
-        break
+for e in distutils.sysconfig.get_config_vars('LIBDIR'):
+  if e != None:
+    print("py_lib_dir:%s" % e)
+    break
diff --git a/data/dinnerware/generate.py b/data/dinnerware/generate.py
index d828dc2ec..5ebdae768 100644
--- a/data/dinnerware/generate.py
+++ b/data/dinnerware/generate.py
@@ -1,252 +1,253 @@
 from __future__ import print_function
 import numpy as np
 
+
 class Obj:
-	def __init__(self, fn):
-		self.ind_v  = 0
-		self.ind_vt = 0
-		self.ind_vn = 0
-		self.fn = fn
-		self.out = open(fn + ".tmp", "w")
-		self.out.write("mtllib dinnerware.mtl\n")
-	def __del__(self):
-		self.out.close()
-		import shutil
-		shutil.move(self.fn + ".tmp", self.fn)
-	def push_v(self, v):
-		self.out.write("v %f %f %f\n" % (v[0],v[1],v[2]))
-		self.ind_v += 1
-		return self.ind_v
-	def push_vt(self, vt):
-		self.out.write("vt %f %f\n" % (vt[0],vt[1]))
-		self.ind_vt += 1
-		return self.ind_vt
-	def push_vn(self, vn):
-		vn /= np.linalg.norm(vn)
-		self.out.write("vn %f %f %f\n" % (vn[0],vn[1],vn[2]))
-		self.ind_vn += 1
-		return self.ind_vn
+
+  def __init__(self, fn):
+    self.ind_v = 0
+    self.ind_vt = 0
+    self.ind_vn = 0
+    self.fn = fn
+    self.out = open(fn + ".tmp", "w")
+    self.out.write("mtllib dinnerware.mtl\n")
+
+  def __del__(self):
+    self.out.close()
+    import shutil
+    shutil.move(self.fn + ".tmp", self.fn)
+
+  def push_v(self, v):
+    self.out.write("v %f %f %f\n" % (v[0], v[1], v[2]))
+    self.ind_v += 1
+    return self.ind_v
+
+  def push_vt(self, vt):
+    self.out.write("vt %f %f\n" % (vt[0], vt[1]))
+    self.ind_vt += 1
+    return self.ind_vt
+
+  def push_vn(self, vn):
+    vn /= np.linalg.norm(vn)
+    self.out.write("vn %f %f %f\n" % (vn[0], vn[1], vn[2]))
+    self.ind_vn += 1
+    return self.ind_vn
 
 
 def convex_hull(points, vind, nind, tind, obj):
-	"super ineffective"
-	cnt = len(points)
-	for a in range(cnt):
-		for b in range(a+1,cnt):
-			for c in range(b+1,cnt):
-				vec1 = points[a] - points[b]
-				vec2 = points[a] - points[c]
-				n  = np.cross(vec1, vec2)
-				n /= np.linalg.norm(n)
-				C = np.dot(n, points[a])
-				inner = np.inner(n, points)
-				pos = (inner <= C+0.0001).all()
-				neg = (inner >= C-0.0001).all()
-				if not pos and not neg: continue
-				obj.out.write("f %i//%i %i//%i %i//%i\n" % ( 
-					(vind[a], nind[a], vind[b], nind[b], vind[c], nind[c])
-					if (inner - C).sum() < 0 else
-					(vind[a], nind[a], vind[c], nind[c], vind[b], nind[b]) ) )
-				#obj.out.write("f %i/%i/%i %i/%i/%i %i/%i/%i\n" % ( 
-				#	(vind[a], tind[a], nind[a], vind[b], tind[b], nind[b], vind[c], tind[c], nind[c])
-				#	if (inner - C).sum() < 0 else
-				#	(vind[a], tind[a], nind[a], vind[c], tind[c], nind[c], vind[b], tind[b], nind[b]) ) )
+  "super ineffective"
+  cnt = len(points)
+  for a in range(cnt):
+    for b in range(a + 1, cnt):
+      for c in range(b + 1, cnt):
+        vec1 = points[a] - points[b]
+        vec2 = points[a] - points[c]
+        n = np.cross(vec1, vec2)
+        n /= np.linalg.norm(n)
+        C = np.dot(n, points[a])
+        inner = np.inner(n, points)
+        pos = (inner <= C + 0.0001).all()
+        neg = (inner >= C - 0.0001).all()
+        if not pos and not neg: continue
+        obj.out.write("f %i//%i %i//%i %i//%i\n" %
+                      ((vind[a], nind[a], vind[b], nind[b], vind[c], nind[c]) if
+                       (inner - C).sum() < 0 else
+                       (vind[a], nind[a], vind[c], nind[c], vind[b], nind[b])))
+        #obj.out.write("f %i/%i/%i %i/%i/%i %i/%i/%i\n" % (
+        #	(vind[a], tind[a], nind[a], vind[b], tind[b], nind[b], vind[c], tind[c], nind[c])
+        #	if (inner - C).sum() < 0 else
+        #	(vind[a], tind[a], nind[a], vind[c], tind[c], nind[c], vind[b], tind[b], nind[b]) ) )
+
 
 def test_convex_hull():
-	obj = Obj("convex_test.obj")
-	vlist = np.random.uniform( low=-0.1, high=+0.1, size=(100,3) )
-	nlist = vlist.copy()
-	tlist = np.random.uniform( low=0, high=+1, size=(100,2) )
-	vind = [obj.push_v(xyz) for xyz in vlist]
-	nind = [obj.push_vn(xyz) for xyz in nlist]
-	tind = [obj.push_vt(uv) for uv in tlist]
-	convex_hull(vlist, vind, nind, tind, obj)
+  obj = Obj("convex_test.obj")
+  vlist = np.random.uniform(low=-0.1, high=+0.1, size=(100, 3))
+  nlist = vlist.copy()
+  tlist = np.random.uniform(low=0, high=+1, size=(100, 2))
+  vind = [obj.push_v(xyz) for xyz in vlist]
+  nind = [obj.push_vn(xyz) for xyz in nlist]
+  tind = [obj.push_vt(uv) for uv in tlist]
+  convex_hull(vlist, vind, nind, tind, obj)
+
 
 class Contour:
-	def __init__(self):
-		self.vprev_vind = None
-
-	def f(self, obj, vlist_vind, vlist_tind, vlist_nind):
-		cnt = len(vlist_vind)
-		for i1 in range(cnt):
-			i2 = i1-1
-			obj.out.write("f %i/%i/%i %i/%i/%i %i/%i/%i\n" % ( 
-				vlist_vind[i2], vlist_tind[i2], vlist_nind[i2],
-				vlist_vind[i1], vlist_tind[i1], vlist_nind[i1],
-				self.vprev_vind[i1], self.vprev_tind[i1], self.vprev_nind[i1],
-				) )
-			obj.out.write("f %i/%i/%i %i/%i/%i %i/%i/%i\n" % ( 
-				vlist_vind[i2], vlist_tind[i2], vlist_nind[i2],
-				self.vprev_vind[i1], self.vprev_tind[i1], self.vprev_nind[i1],
-				self.vprev_vind[i2], self.vprev_tind[i2], self.vprev_nind[i2],
-				) )
-
-	def belt(self, obj, vlist, nlist, tlist):
-		vlist_vind = [obj.push_v(xyz)  for xyz in vlist]
-		vlist_tind = [obj.push_vt(xyz) for xyz in tlist]
-		vlist_nind = [obj.push_vn(xyz) for xyz in nlist]
-		if self.vprev_vind:
-			self.f(obj, vlist_vind, vlist_tind, vlist_nind)
-		else:
-			self.first_vind = vlist_vind
-			self.first_tind = vlist_tind
-			self.first_nind = vlist_nind
-		self.vprev_vind = vlist_vind
-		self.vprev_tind = vlist_tind
-		self.vprev_nind = vlist_nind
-
-	def finish(self, obj):
-		self.f(obj, self.first_vind, self.first_tind, self.first_nind)
+
+  def __init__(self):
+    self.vprev_vind = None
+
+  def f(self, obj, vlist_vind, vlist_tind, vlist_nind):
+    cnt = len(vlist_vind)
+    for i1 in range(cnt):
+      i2 = i1 - 1
+      obj.out.write("f %i/%i/%i %i/%i/%i %i/%i/%i\n" % (
+          vlist_vind[i2],
+          vlist_tind[i2],
+          vlist_nind[i2],
+          vlist_vind[i1],
+          vlist_tind[i1],
+          vlist_nind[i1],
+          self.vprev_vind[i1],
+          self.vprev_tind[i1],
+          self.vprev_nind[i1],
+      ))
+      obj.out.write("f %i/%i/%i %i/%i/%i %i/%i/%i\n" % (
+          vlist_vind[i2],
+          vlist_tind[i2],
+          vlist_nind[i2],
+          self.vprev_vind[i1],
+          self.vprev_tind[i1],
+          self.vprev_nind[i1],
+          self.vprev_vind[i2],
+          self.vprev_tind[i2],
+          self.vprev_nind[i2],
+      ))
+
+  def belt(self, obj, vlist, nlist, tlist):
+    vlist_vind = [obj.push_v(xyz) for xyz in vlist]
+    vlist_tind = [obj.push_vt(xyz) for xyz in tlist]
+    vlist_nind = [obj.push_vn(xyz) for xyz in nlist]
+    if self.vprev_vind:
+      self.f(obj, vlist_vind, vlist_tind, vlist_nind)
+    else:
+      self.first_vind = vlist_vind
+      self.first_tind = vlist_tind
+      self.first_nind = vlist_nind
+    self.vprev_vind = vlist_vind
+    self.vprev_tind = vlist_tind
+    self.vprev_nind = vlist_nind
+
+  def finish(self, obj):
+    self.f(obj, self.first_vind, self.first_tind, self.first_nind)
+
 
 def test_contour():
-	RAD1 = 2.0
-	RAD2 = 1.5
-	obj = Obj("torus.obj")
-	obj.out.write("usemtl porcelain\n")
-	contour = Contour()
-	for step in range(100):
-		angle = step/100.0*2*np.pi
-		belt_v = []
-		belt_n = []
-		belt_t = []
-		for b in range(50):
-			beta = b/50.0*2*np.pi
-			r = RAD2*np.cos(beta) + RAD1
-			z = RAD2*np.sin(beta)
-			belt_v.append( np.array( [
-				np.cos(angle)*r,
-				np.sin(angle)*r,
-				z] ) )
-			belt_n.append( np.array( [
-				np.cos(angle)*np.cos(beta),
-				np.sin(angle)*np.cos(beta),
-				np.sin(beta)] ) )
-			belt_t.append( (0,0) )
-		contour.belt(obj, belt_v, belt_n, belt_t)
-	contour.finish(obj)
+  RAD1 = 2.0
+  RAD2 = 1.5
+  obj = Obj("torus.obj")
+  obj.out.write("usemtl porcelain\n")
+  contour = Contour()
+  for step in range(100):
+    angle = step / 100.0 * 2 * np.pi
+    belt_v = []
+    belt_n = []
+    belt_t = []
+    for b in range(50):
+      beta = b / 50.0 * 2 * np.pi
+      r = RAD2 * np.cos(beta) + RAD1
+      z = RAD2 * np.sin(beta)
+      belt_v.append(np.array([np.cos(angle) * r, np.sin(angle) * r, z]))
+      belt_n.append(
+          np.array([np.cos(angle) * np.cos(beta),
+                    np.sin(angle) * np.cos(beta),
+                    np.sin(beta)]))
+      belt_t.append((0, 0))
+    contour.belt(obj, belt_v, belt_n, belt_t)
+  contour.finish(obj)
+
 
 #test_convex_hull()
 #test_contour()
 
+
 class RotationFigureParams:
-	pass
+  pass
+
 
 def generate_plate(p, obj, collision_prefix):
-	contour = Contour()
-	belt_vlist_3d_prev = None
-
-	for step in range(p.N_VIZ+1):
-		angle = step/float(p.N_VIZ)*2*np.pi
-
-		if step % p.COLLISION_EVERY == 0:
-			vlist_3d = []
-			for x,y in p.belt_simple:
-				vlist_3d.append( [
-					np.cos(angle)*x*1.06,
-					np.sin(angle)*x*1.06,
-					y
-					] )
-			if belt_vlist_3d_prev:
-				obj2 = Obj(collision_prefix % (step / p.COLLISION_EVERY))
-				obj2.out.write("usemtl pan_tefal\n")
-				vlist = np.array( vlist_3d + belt_vlist_3d_prev )
-				vlist[len(vlist_3d):] *= 1.01  # break points on one plane
-				vlist[0,0:2] += 0.01*vlist[len(vlist_3d),0:2]
-				vlist[len(vlist_3d),0:2] += 0.01*vlist[0,0:2]
-				nlist = np.random.uniform( low=-1, high=+1, size=vlist.shape )
-				tlist = np.random.uniform( low=0, high=+1, size=(len(vlist),2) )
-				vind = [obj2.push_v(xyz) for xyz in vlist]
-				nind = [obj2.push_vn(xyz) for xyz in nlist]
-				convex_hull(vlist, vind, nind, None, obj2)
-			belt_vlist_3d_prev = vlist_3d
-		if step==p.N_VIZ: break
-
-		belt_v = []
-		belt_n = []
-		belt_t = []
-		for x,y,nx,ny in p.belt:
-			belt_v.append( np.array( [
-				np.cos(angle)*x,
-				np.sin(angle)*x,
-				y
-				] ) )
-			belt_n.append( np.array( [
-				np.cos(angle)*nx,
-				np.sin(angle)*nx,
-				ny
-				] ) )
-			if ny-nx >= 0:
-				belt_t.append( (
-					127.0/512 + np.cos(angle)*x/p.RAD_HIGH*105/512,
-					(512-135.0)/512 + np.sin(angle)*x/p.RAD_HIGH*105/512) )
-			else:
-				belt_t.append( (
-					382.0/512 + np.cos(angle)*x/p.RAD_HIGH*125/512,
-					(512-380.0)/512 + np.sin(angle)*x/p.RAD_HIGH*125/512) )
-		contour.belt(obj, belt_v, belt_n, belt_t)
-
-	contour.finish(obj)
+  contour = Contour()
+  belt_vlist_3d_prev = None
+
+  for step in range(p.N_VIZ + 1):
+    angle = step / float(p.N_VIZ) * 2 * np.pi
+
+    if step % p.COLLISION_EVERY == 0:
+      vlist_3d = []
+      for x, y in p.belt_simple:
+        vlist_3d.append([np.cos(angle) * x * 1.06, np.sin(angle) * x * 1.06, y])
+      if belt_vlist_3d_prev:
+        obj2 = Obj(collision_prefix % (step / p.COLLISION_EVERY))
+        obj2.out.write("usemtl pan_tefal\n")
+        vlist = np.array(vlist_3d + belt_vlist_3d_prev)
+        vlist[len(vlist_3d):] *= 1.01  # break points on one plane
+        vlist[0, 0:2] += 0.01 * vlist[len(vlist_3d), 0:2]
+        vlist[len(vlist_3d), 0:2] += 0.01 * vlist[0, 0:2]
+        nlist = np.random.uniform(low=-1, high=+1, size=vlist.shape)
+        tlist = np.random.uniform(low=0, high=+1, size=(len(vlist), 2))
+        vind = [obj2.push_v(xyz) for xyz in vlist]
+        nind = [obj2.push_vn(xyz) for xyz in nlist]
+        convex_hull(vlist, vind, nind, None, obj2)
+      belt_vlist_3d_prev = vlist_3d
+    if step == p.N_VIZ: break
+
+    belt_v = []
+    belt_n = []
+    belt_t = []
+    for x, y, nx, ny in p.belt:
+      belt_v.append(np.array([np.cos(angle) * x, np.sin(angle) * x, y]))
+      belt_n.append(np.array([np.cos(angle) * nx, np.sin(angle) * nx, ny]))
+      if ny - nx >= 0:
+        belt_t.append((127.0 / 512 + np.cos(angle) * x / p.RAD_HIGH * 105 / 512,
+                       (512 - 135.0) / 512 + np.sin(angle) * x / p.RAD_HIGH * 105 / 512))
+      else:
+        belt_t.append((382.0 / 512 + np.cos(angle) * x / p.RAD_HIGH * 125 / 512,
+                       (512 - 380.0) / 512 + np.sin(angle) * x / p.RAD_HIGH * 125 / 512))
+    contour.belt(obj, belt_v, belt_n, belt_t)
+
+  contour.finish(obj)
+
 
 def tefal():
-	p = RotationFigureParams()
-	p.RAD_LOW  = 0.240/2
-	p.RAD_HIGH = 0.255/2
-	p.H   = 0.075
-	p.THICK = 0.005
-	p.N_VIZ = 30
-	p.COLLISION_EVERY = 5
-	p.belt = [
-		(p.RAD_HIGH-p.THICK, p.H,     -1,0),  # x y norm
-		(p.RAD_HIGH      , p.H,      0,1),
-		(p.RAD_HIGH+p.THICK, p.H,     +1,0),
-		(p.RAD_LOW+p.THICK, p.THICK,   +1,0),
-		(p.RAD_LOW      , 0,       0,-1),
-		(            0, 0,       0,-1),
-		(            0, p.THICK,   0,1),
-		(p.RAD_LOW-p.THICK, p.THICK,   0,1),
-		(p.RAD_LOW-p.THICK, 3*p.THICK,-1,0),
-		]
-	p.belt.reverse()
-	p.belt_simple = [
-		(p.RAD_HIGH-p.THICK, p.H),
-		(p.RAD_HIGH+p.THICK, p.H),
-		(p.RAD_LOW       , 0),
-		(p.RAD_LOW-p.THICK , 0)
-		]
-	obj = Obj("pan_tefal.obj")
-	obj.out.write("usemtl pan_tefal\n")
-	generate_plate(p, obj, "pan_tefal-collision%02i.obj")
+  p = RotationFigureParams()
+  p.RAD_LOW = 0.240 / 2
+  p.RAD_HIGH = 0.255 / 2
+  p.H = 0.075
+  p.THICK = 0.005
+  p.N_VIZ = 30
+  p.COLLISION_EVERY = 5
+  p.belt = [
+      (p.RAD_HIGH - p.THICK, p.H, -1, 0),  # x y norm
+      (p.RAD_HIGH, p.H, 0, 1),
+      (p.RAD_HIGH + p.THICK, p.H, +1, 0),
+      (p.RAD_LOW + p.THICK, p.THICK, +1, 0),
+      (p.RAD_LOW, 0, 0, -1),
+      (0, 0, 0, -1),
+      (0, p.THICK, 0, 1),
+      (p.RAD_LOW - p.THICK, p.THICK, 0, 1),
+      (p.RAD_LOW - p.THICK, 3 * p.THICK, -1, 0),
+  ]
+  p.belt.reverse()
+  p.belt_simple = [(p.RAD_HIGH - p.THICK, p.H), (p.RAD_HIGH + p.THICK, p.H), (p.RAD_LOW, 0),
+                   (p.RAD_LOW - p.THICK, 0)]
+  obj = Obj("pan_tefal.obj")
+  obj.out.write("usemtl pan_tefal\n")
+  generate_plate(p, obj, "pan_tefal-collision%02i.obj")
 
-def plate():
-	p = RotationFigureParams()
-	p.RAD_LOW  = 0.110/2
-	p.RAD_HIGH = 0.190/2
-	p.H   = 0.060
-	p.THICK = 0.003
-	p.N_VIZ = 30
-	p.COLLISION_EVERY = 5
-	p.belt = [
-		(p.RAD_HIGH-p.THICK, p.H,     -0.9,0.5),  # x y norm
-		(p.RAD_HIGH      , p.H,      0,1),
-		(p.RAD_HIGH+p.THICK, p.H,     +1,0),
-		(p.RAD_LOW+p.THICK, p.THICK,   +1,0),
-		(p.RAD_LOW      , 0,       0,-1),
-		(            0, 0,       0,-1),
-		(            0, p.THICK,   0,1),
-		(p.RAD_LOW-3*p.THICK, p.THICK,   0,1),
-		(p.RAD_LOW-p.THICK, 3*p.THICK,-0.5,1.0),
-		]
-	p.belt.reverse()
-	p.belt_simple = [
-		(p.RAD_HIGH-p.THICK, p.H),
-		(p.RAD_HIGH+p.THICK, p.H),
-		(p.RAD_LOW       , 0),
-		(p.RAD_LOW-p.THICK , 0)
-		]
-	obj = Obj("plate.obj")
-	obj.out.write("usemtl solid_color\n")
-	generate_plate(p, obj, "plate-collision%02i.obj")
 
-plate()
+def plate():
+  p = RotationFigureParams()
+  p.RAD_LOW = 0.110 / 2
+  p.RAD_HIGH = 0.190 / 2
+  p.H = 0.060
+  p.THICK = 0.003
+  p.N_VIZ = 30
+  p.COLLISION_EVERY = 5
+  p.belt = [
+      (p.RAD_HIGH - p.THICK, p.H, -0.9, 0.5),  # x y norm
+      (p.RAD_HIGH, p.H, 0, 1),
+      (p.RAD_HIGH + p.THICK, p.H, +1, 0),
+      (p.RAD_LOW + p.THICK, p.THICK, +1, 0),
+      (p.RAD_LOW, 0, 0, -1),
+      (0, 0, 0, -1),
+      (0, p.THICK, 0, 1),
+      (p.RAD_LOW - 3 * p.THICK, p.THICK, 0, 1),
+      (p.RAD_LOW - p.THICK, 3 * p.THICK, -0.5, 1.0),
+  ]
+  p.belt.reverse()
+  p.belt_simple = [(p.RAD_HIGH - p.THICK, p.H), (p.RAD_HIGH + p.THICK, p.H), (p.RAD_LOW, 0),
+                   (p.RAD_LOW - p.THICK, 0)]
+  obj = Obj("plate.obj")
+  obj.out.write("usemtl solid_color\n")
+  generate_plate(p, obj, "plate-collision%02i.obj")
 
 
+plate()
diff --git a/data/quadruped/vision/vision.py b/data/quadruped/vision/vision.py
index 0901d7dfc..359955c08 100644
--- a/data/quadruped/vision/vision.py
+++ b/data/quadruped/vision/vision.py
@@ -3,51 +3,44 @@ import time
 import math
 
 pi = 3.14159264
-limitVal = 2*pi
-legpos = 3./4.*pi
+limitVal = 2 * pi
+legpos = 3. / 4. * pi
 legposS = 0
-legposSright = 0#-0.3
-legposSleft = 0#0.3
+legposSright = 0  #-0.3
+legposSleft = 0  #0.3
 
-defaultERP=0.4
+defaultERP = 0.4
 maxMotorForce = 5000
 maxGearForce = 10000
 jointFriction = 0.1
 
-
 p.connect(p.GUI)
 
+amplitudeId = p.addUserDebugParameter("amplitude", 0, 3.14, 0.5)
+timeScaleId = p.addUserDebugParameter("timeScale", 0, 10, 1)
 
-amplitudeId= p.addUserDebugParameter("amplitude",0,3.14,0.5)
-timeScaleId = p.addUserDebugParameter("timeScale",0,10,1)
-
-
-jointTypeNames={}
-jointTypeNames[p.JOINT_REVOLUTE]="JOINT_REVOLUTE"
-jointTypeNames[p.JOINT_FIXED]="JOINT_FIXED"
+jointTypeNames = {}
+jointTypeNames[p.JOINT_REVOLUTE] = "JOINT_REVOLUTE"
+jointTypeNames[p.JOINT_FIXED] = "JOINT_FIXED"
 p.setPhysicsEngineParameter(numSolverIterations=100)
 p.loadURDF("plane_transparent.urdf", useMaximalCoordinates=True)
 #disable rendering during creation.
 #p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
-p.configureDebugVisualizer(p.COV_ENABLE_PLANAR_REFLECTION,1)
-
-
-jointNamesToIndex={}
+p.configureDebugVisualizer(p.COV_ENABLE_PLANAR_REFLECTION, 1)
 
+jointNamesToIndex = {}
 
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)
-vision = p.loadURDF("vision60.urdf",[0,0,0.4],useFixedBase=False)
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
-
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
+vision = p.loadURDF("vision60.urdf", [0, 0, 0.4], useFixedBase=False)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
 
 for j in range(p.getNumJoints(vision)):
-	jointInfo = p.getJointInfo(vision,j)
-	jointInfoName = jointInfo[1].decode("utf-8") 
-	print("joint ",j," = ",jointInfoName, "type=",jointTypeNames[jointInfo[2]])
-	jointNamesToIndex[jointInfoName ]=j
-	#print("jointNamesToIndex[..]=",jointNamesToIndex[jointInfoName])
-	p.setJointMotorControl2(vision,j,p.VELOCITY_CONTROL,targetVelocity=0, force=jointFriction)
-
+  jointInfo = p.getJointInfo(vision, j)
+  jointInfoName = jointInfo[1].decode("utf-8")
+  print("joint ", j, " = ", jointInfoName, "type=", jointTypeNames[jointInfo[2]])
+  jointNamesToIndex[jointInfoName] = j
+  #print("jointNamesToIndex[..]=",jointNamesToIndex[jointInfoName])
+  p.setJointMotorControl2(vision, j, p.VELOCITY_CONTROL, targetVelocity=0, force=jointFriction)
 
 chassis_right_center = jointNamesToIndex['chassis_right_center']
 motor_front_rightR_joint = jointNamesToIndex['motor_front_rightR_joint']
@@ -72,149 +65,277 @@ knee_back_leftL_joint = jointNamesToIndex['knee_back_leftL_joint']
 motor_back_leftR_joint = jointNamesToIndex['motor_back_leftR_joint']
 motor_back_leftS_joint = jointNamesToIndex['motor_back_leftS_joint']
 
-motA_rf_Id= p.addUserDebugParameter("motA_rf",-limitVal,limitVal,legpos)
-motB_rf_Id= p.addUserDebugParameter("motB_rf",-limitVal,limitVal,legpos)
-motC_rf_Id= p.addUserDebugParameter("motC_rf",-limitVal,limitVal,legposSright)
-erp_rf_Id= p.addUserDebugParameter("erp_rf",0,1,defaultERP)
-relPosTarget_rf_Id= p.addUserDebugParameter("relPosTarget_rf",-limitVal,limitVal,0)
-
+motA_rf_Id = p.addUserDebugParameter("motA_rf", -limitVal, limitVal, legpos)
+motB_rf_Id = p.addUserDebugParameter("motB_rf", -limitVal, limitVal, legpos)
+motC_rf_Id = p.addUserDebugParameter("motC_rf", -limitVal, limitVal, legposSright)
+erp_rf_Id = p.addUserDebugParameter("erp_rf", 0, 1, defaultERP)
+relPosTarget_rf_Id = p.addUserDebugParameter("relPosTarget_rf", -limitVal, limitVal, 0)
 
-motA_lf_Id= p.addUserDebugParameter("motA_lf",-limitVal,limitVal,-legpos)
-motB_lf_Id= p.addUserDebugParameter("motB_lf",-limitVal,limitVal,-legpos)
-motC_lf_Id= p.addUserDebugParameter("motC_lf",-limitVal,limitVal,legposSleft)
+motA_lf_Id = p.addUserDebugParameter("motA_lf", -limitVal, limitVal, -legpos)
+motB_lf_Id = p.addUserDebugParameter("motB_lf", -limitVal, limitVal, -legpos)
+motC_lf_Id = p.addUserDebugParameter("motC_lf", -limitVal, limitVal, legposSleft)
 
-erp_lf_Id= p.addUserDebugParameter("erp_lf",0,1,defaultERP)
-relPosTarget_lf_Id= p.addUserDebugParameter("relPosTarget_lf",-limitVal,limitVal,0)
+erp_lf_Id = p.addUserDebugParameter("erp_lf", 0, 1, defaultERP)
+relPosTarget_lf_Id = p.addUserDebugParameter("relPosTarget_lf", -limitVal, limitVal, 0)
 
-motA_rb_Id= p.addUserDebugParameter("motA_rb",-limitVal,limitVal,legpos)
-motB_rb_Id= p.addUserDebugParameter("motB_rb",-limitVal,limitVal,legpos)
-motC_rb_Id= p.addUserDebugParameter("motC_rb",-limitVal,limitVal,legposSright)
+motA_rb_Id = p.addUserDebugParameter("motA_rb", -limitVal, limitVal, legpos)
+motB_rb_Id = p.addUserDebugParameter("motB_rb", -limitVal, limitVal, legpos)
+motC_rb_Id = p.addUserDebugParameter("motC_rb", -limitVal, limitVal, legposSright)
 
-erp_rb_Id= p.addUserDebugParameter("erp_rb",0,1,defaultERP)
-relPosTarget_rb_Id= p.addUserDebugParameter("relPosTarget_rb",-limitVal,limitVal,0)
+erp_rb_Id = p.addUserDebugParameter("erp_rb", 0, 1, defaultERP)
+relPosTarget_rb_Id = p.addUserDebugParameter("relPosTarget_rb", -limitVal, limitVal, 0)
 
+motA_lb_Id = p.addUserDebugParameter("motA_lb", -limitVal, limitVal, -legpos)
+motB_lb_Id = p.addUserDebugParameter("motB_lb", -limitVal, limitVal, -legpos)
+motC_lb_Id = p.addUserDebugParameter("motC_lb", -limitVal, limitVal, legposSleft)
 
-motA_lb_Id= p.addUserDebugParameter("motA_lb",-limitVal,limitVal,-legpos)
-motB_lb_Id= p.addUserDebugParameter("motB_lb",-limitVal,limitVal,-legpos)
-motC_lb_Id= p.addUserDebugParameter("motC_lb",-limitVal,limitVal,legposSleft)
+erp_lb_Id = p.addUserDebugParameter("erp_lb", 0, 1, defaultERP)
+relPosTarget_lb_Id = p.addUserDebugParameter("relPosTarget_lb", -limitVal, limitVal, 0)
 
-erp_lb_Id= p.addUserDebugParameter("erp_lb",0,1,defaultERP)
-relPosTarget_lb_Id= p.addUserDebugParameter("relPosTarget_lb",-limitVal,limitVal,0)
-
-camTargetPos=[0.25,0.62,-0.15]
+camTargetPos = [0.25, 0.62, -0.15]
 camDist = 2
 camYaw = -2
-camPitch=-16
+camPitch = -16
 p.resetDebugVisualizerCamera(camDist, camYaw, camPitch, camTargetPos)
 
-
-
-
-
-c_rf = p.createConstraint(vision,knee_front_rightR_joint,vision,motor_front_rightL_joint,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c_rf,gearRatio=-1, gearAuxLink = motor_front_rightR_joint,maxForce=maxGearForce)
-
-c_lf = p.createConstraint(vision,knee_front_leftL_joint,vision,motor_front_leftR_joint,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c_lf,gearRatio=-1, gearAuxLink = motor_front_leftL_joint,maxForce=maxGearForce)
-
-c_rb = p.createConstraint(vision,knee_back_rightR_joint,vision,motor_back_rightL_joint,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c_rb,gearRatio=-1, gearAuxLink = motor_back_rightR_joint,maxForce=maxGearForce)
-
-c_lb = p.createConstraint(vision,knee_back_leftL_joint,vision,motor_back_leftR_joint,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c_lb,gearRatio=-1, gearAuxLink = motor_back_leftL_joint,maxForce=maxGearForce)
-
-
-
+c_rf = p.createConstraint(vision,
+                          knee_front_rightR_joint,
+                          vision,
+                          motor_front_rightL_joint,
+                          jointType=p.JOINT_GEAR,
+                          jointAxis=[0, 1, 0],
+                          parentFramePosition=[0, 0, 0],
+                          childFramePosition=[0, 0, 0])
+p.changeConstraint(c_rf, gearRatio=-1, gearAuxLink=motor_front_rightR_joint, maxForce=maxGearForce)
+
+c_lf = p.createConstraint(vision,
+                          knee_front_leftL_joint,
+                          vision,
+                          motor_front_leftR_joint,
+                          jointType=p.JOINT_GEAR,
+                          jointAxis=[0, 1, 0],
+                          parentFramePosition=[0, 0, 0],
+                          childFramePosition=[0, 0, 0])
+p.changeConstraint(c_lf, gearRatio=-1, gearAuxLink=motor_front_leftL_joint, maxForce=maxGearForce)
+
+c_rb = p.createConstraint(vision,
+                          knee_back_rightR_joint,
+                          vision,
+                          motor_back_rightL_joint,
+                          jointType=p.JOINT_GEAR,
+                          jointAxis=[0, 1, 0],
+                          parentFramePosition=[0, 0, 0],
+                          childFramePosition=[0, 0, 0])
+p.changeConstraint(c_rb, gearRatio=-1, gearAuxLink=motor_back_rightR_joint, maxForce=maxGearForce)
+
+c_lb = p.createConstraint(vision,
+                          knee_back_leftL_joint,
+                          vision,
+                          motor_back_leftR_joint,
+                          jointType=p.JOINT_GEAR,
+                          jointAxis=[0, 1, 0],
+                          parentFramePosition=[0, 0, 0],
+                          childFramePosition=[0, 0, 0])
+p.changeConstraint(c_lb, gearRatio=-1, gearAuxLink=motor_back_leftL_joint, maxForce=maxGearForce)
 
 p.setRealTimeSimulation(1)
-for i in range (1):
-#while (1):
-	motA_rf = p.readUserDebugParameter(motA_rf_Id)
-	motB_rf = p.readUserDebugParameter(motB_rf_Id)
-	motC_rf = p.readUserDebugParameter(motC_rf_Id)
-	erp_rf = p.readUserDebugParameter(erp_rf_Id)
-	relPosTarget_rf = p.readUserDebugParameter(relPosTarget_rf_Id)
-	#motC_rf
-	p.setJointMotorControl2(vision,motor_front_rightR_joint,p.POSITION_CONTROL,targetPosition=motA_rf,force=maxMotorForce)
-	p.setJointMotorControl2(vision,motor_front_rightL_joint,p.POSITION_CONTROL,targetPosition=motB_rf,force=maxMotorForce)
-	p.setJointMotorControl2(vision,motor_front_rightS_joint,p.POSITION_CONTROL,targetPosition=motC_rf,force=maxMotorForce)
-	p.changeConstraint(c_rf,gearRatio=-1, gearAuxLink = motor_front_rightR_joint,erp=erp_rf, relativePositionTarget=relPosTarget_rf,maxForce=maxGearForce)
-
-	motA_lf = p.readUserDebugParameter(motA_lf_Id)
-	motB_lf = p.readUserDebugParameter(motB_lf_Id)
-	motC_lf = p.readUserDebugParameter(motC_lf_Id)
-	erp_lf = p.readUserDebugParameter(erp_lf_Id)
-	relPosTarget_lf = p.readUserDebugParameter(relPosTarget_lf_Id)
-	p.setJointMotorControl2(vision,motor_front_leftL_joint,p.POSITION_CONTROL,targetPosition=motA_lf,force=maxMotorForce)
-	p.setJointMotorControl2(vision,motor_front_leftR_joint,p.POSITION_CONTROL,targetPosition=motB_lf,force=maxMotorForce)
-	p.setJointMotorControl2(vision,motor_front_leftS_joint,p.POSITION_CONTROL,targetPosition=motC_lf,force=maxMotorForce)
-	p.changeConstraint(c_lf,gearRatio=-1, gearAuxLink = motor_front_leftL_joint,erp=erp_lf, relativePositionTarget=relPosTarget_lf,maxForce=maxGearForce)
-
-
-	motA_rb = p.readUserDebugParameter(motA_rb_Id)
-	motB_rb = p.readUserDebugParameter(motB_rb_Id)
-	motC_rb = p.readUserDebugParameter(motC_rb_Id)
-	erp_rb = p.readUserDebugParameter(erp_rb_Id)
-	relPosTarget_rb = p.readUserDebugParameter(relPosTarget_rb_Id)
-	p.setJointMotorControl2(vision,motor_back_rightR_joint,p.POSITION_CONTROL,targetPosition=motA_rb,force=maxMotorForce)
-	p.setJointMotorControl2(vision,motor_back_rightL_joint,p.POSITION_CONTROL,targetPosition=motB_rb,force=maxMotorForce)
-	p.setJointMotorControl2(vision,motor_back_rightS_joint,p.POSITION_CONTROL,targetPosition=motC_rb,force=maxMotorForce)
-	p.changeConstraint(c_rb,gearRatio=-1, gearAuxLink = motor_back_rightR_joint,erp=erp_rb, relativePositionTarget=relPosTarget_rb,maxForce=maxGearForce)
-
-	motA_lb = p.readUserDebugParameter(motA_lb_Id)
-	motB_lb = p.readUserDebugParameter(motB_lb_Id)
-	motC_lb = p.readUserDebugParameter(motC_lb_Id)
-	erp_lb = p.readUserDebugParameter(erp_lb_Id)
-	relPosTarget_lb = p.readUserDebugParameter(relPosTarget_lb_Id)
-	p.setJointMotorControl2(vision,motor_back_leftL_joint,p.POSITION_CONTROL,targetPosition=motA_lb,force=maxMotorForce)
-	p.setJointMotorControl2(vision,motor_back_leftR_joint,p.POSITION_CONTROL,targetPosition=motB_lb,force=maxMotorForce)
-	p.setJointMotorControl2(vision,motor_back_leftS_joint,p.POSITION_CONTROL,targetPosition=motC_lb,force=maxMotorForce)
-	p.changeConstraint(c_lb,gearRatio=-1, gearAuxLink = motor_back_leftL_joint,erp=erp_lb, relativePositionTarget=relPosTarget_lb,maxForce=maxGearForce)
-
-
-
-	p.setGravity(0,0,-10)
-	time.sleep(1./240.)
+for i in range(1):
+  #while (1):
+  motA_rf = p.readUserDebugParameter(motA_rf_Id)
+  motB_rf = p.readUserDebugParameter(motB_rf_Id)
+  motC_rf = p.readUserDebugParameter(motC_rf_Id)
+  erp_rf = p.readUserDebugParameter(erp_rf_Id)
+  relPosTarget_rf = p.readUserDebugParameter(relPosTarget_rf_Id)
+  #motC_rf
+  p.setJointMotorControl2(vision,
+                          motor_front_rightR_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motA_rf,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_front_rightL_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motB_rf,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_front_rightS_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motC_rf,
+                          force=maxMotorForce)
+  p.changeConstraint(c_rf,
+                     gearRatio=-1,
+                     gearAuxLink=motor_front_rightR_joint,
+                     erp=erp_rf,
+                     relativePositionTarget=relPosTarget_rf,
+                     maxForce=maxGearForce)
+
+  motA_lf = p.readUserDebugParameter(motA_lf_Id)
+  motB_lf = p.readUserDebugParameter(motB_lf_Id)
+  motC_lf = p.readUserDebugParameter(motC_lf_Id)
+  erp_lf = p.readUserDebugParameter(erp_lf_Id)
+  relPosTarget_lf = p.readUserDebugParameter(relPosTarget_lf_Id)
+  p.setJointMotorControl2(vision,
+                          motor_front_leftL_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motA_lf,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_front_leftR_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motB_lf,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_front_leftS_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motC_lf,
+                          force=maxMotorForce)
+  p.changeConstraint(c_lf,
+                     gearRatio=-1,
+                     gearAuxLink=motor_front_leftL_joint,
+                     erp=erp_lf,
+                     relativePositionTarget=relPosTarget_lf,
+                     maxForce=maxGearForce)
+
+  motA_rb = p.readUserDebugParameter(motA_rb_Id)
+  motB_rb = p.readUserDebugParameter(motB_rb_Id)
+  motC_rb = p.readUserDebugParameter(motC_rb_Id)
+  erp_rb = p.readUserDebugParameter(erp_rb_Id)
+  relPosTarget_rb = p.readUserDebugParameter(relPosTarget_rb_Id)
+  p.setJointMotorControl2(vision,
+                          motor_back_rightR_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motA_rb,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_back_rightL_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motB_rb,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_back_rightS_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motC_rb,
+                          force=maxMotorForce)
+  p.changeConstraint(c_rb,
+                     gearRatio=-1,
+                     gearAuxLink=motor_back_rightR_joint,
+                     erp=erp_rb,
+                     relativePositionTarget=relPosTarget_rb,
+                     maxForce=maxGearForce)
+
+  motA_lb = p.readUserDebugParameter(motA_lb_Id)
+  motB_lb = p.readUserDebugParameter(motB_lb_Id)
+  motC_lb = p.readUserDebugParameter(motC_lb_Id)
+  erp_lb = p.readUserDebugParameter(erp_lb_Id)
+  relPosTarget_lb = p.readUserDebugParameter(relPosTarget_lb_Id)
+  p.setJointMotorControl2(vision,
+                          motor_back_leftL_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motA_lb,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_back_leftR_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motB_lb,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_back_leftS_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motC_lb,
+                          force=maxMotorForce)
+  p.changeConstraint(c_lb,
+                     gearRatio=-1,
+                     gearAuxLink=motor_back_leftL_joint,
+                     erp=erp_lb,
+                     relativePositionTarget=relPosTarget_lb,
+                     maxForce=maxGearForce)
+
+  p.setGravity(0, 0, -10)
+  time.sleep(1. / 240.)
 t = 0
 prevTime = time.time()
 while (1):
-        timeScale = p.readUserDebugParameter(timeScaleId)
-        amplitude = p.readUserDebugParameter(amplitudeId)
-        newTime = time.time()
-        dt = (newTime-prevTime)*timeScale
-        t = t+dt
-        prevTime = newTime
-
-        amp=amplitude
-        motA_rf = math.sin(t)*amp+legpos
-        motA_rb = math.sin(t)*amp+legpos
-        motA_lf = -(math.sin(t)*amp+legpos)
-        motA_lb = -(math.sin(t)*amp+legpos)
-
-        motB_rf = math.sin(t)*amp+legpos
-        motB_rb = math.sin(t)*amp+legpos
-        motB_lf = -(math.sin(t)*amp+legpos)
-        motB_lb = -(math.sin(t)*amp+legpos)
-	
-        p.setJointMotorControl2(vision,motor_front_rightR_joint,p.POSITION_CONTROL,targetPosition=motA_rf,force=maxMotorForce)
-        p.setJointMotorControl2(vision,motor_front_rightL_joint,p.POSITION_CONTROL,targetPosition=motB_rf,force=maxMotorForce)
-        p.setJointMotorControl2(vision,motor_front_rightS_joint,p.POSITION_CONTROL,targetPosition=motC_rf,force=maxMotorForce)
-      
-
-        p.setJointMotorControl2(vision,motor_front_leftL_joint,p.POSITION_CONTROL,targetPosition=motA_lf,force=maxMotorForce)
-        p.setJointMotorControl2(vision,motor_front_leftR_joint,p.POSITION_CONTROL,targetPosition=motB_lf,force=maxMotorForce)
-        p.setJointMotorControl2(vision,motor_front_leftS_joint,p.POSITION_CONTROL,targetPosition=motC_lf,force=maxMotorForce)
-
-
-        p.setJointMotorControl2(vision,motor_back_rightR_joint,p.POSITION_CONTROL,targetPosition=motA_rb,force=maxMotorForce)
-        p.setJointMotorControl2(vision,motor_back_rightL_joint,p.POSITION_CONTROL,targetPosition=motB_rb,force=maxMotorForce)
-        p.setJointMotorControl2(vision,motor_back_rightS_joint,p.POSITION_CONTROL,targetPosition=motC_rb,force=maxMotorForce)
-
-
-        p.setJointMotorControl2(vision,motor_back_leftL_joint,p.POSITION_CONTROL,targetPosition=motA_lb,force=maxMotorForce)
-        p.setJointMotorControl2(vision,motor_back_leftR_joint,p.POSITION_CONTROL,targetPosition=motB_lb,force=maxMotorForce)
-        p.setJointMotorControl2(vision,motor_back_leftS_joint,p.POSITION_CONTROL,targetPosition=motC_lb,force=maxMotorForce)
-
-        p.setGravity(0,0,-10)
-        time.sleep(1./240.)
-
+  timeScale = p.readUserDebugParameter(timeScaleId)
+  amplitude = p.readUserDebugParameter(amplitudeId)
+  newTime = time.time()
+  dt = (newTime - prevTime) * timeScale
+  t = t + dt
+  prevTime = newTime
+
+  amp = amplitude
+  motA_rf = math.sin(t) * amp + legpos
+  motA_rb = math.sin(t) * amp + legpos
+  motA_lf = -(math.sin(t) * amp + legpos)
+  motA_lb = -(math.sin(t) * amp + legpos)
+
+  motB_rf = math.sin(t) * amp + legpos
+  motB_rb = math.sin(t) * amp + legpos
+  motB_lf = -(math.sin(t) * amp + legpos)
+  motB_lb = -(math.sin(t) * amp + legpos)
+
+  p.setJointMotorControl2(vision,
+                          motor_front_rightR_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motA_rf,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_front_rightL_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motB_rf,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_front_rightS_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motC_rf,
+                          force=maxMotorForce)
+
+  p.setJointMotorControl2(vision,
+                          motor_front_leftL_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motA_lf,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_front_leftR_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motB_lf,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_front_leftS_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motC_lf,
+                          force=maxMotorForce)
+
+  p.setJointMotorControl2(vision,
+                          motor_back_rightR_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motA_rb,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_back_rightL_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motB_rb,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_back_rightS_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motC_rb,
+                          force=maxMotorForce)
+
+  p.setJointMotorControl2(vision,
+                          motor_back_leftL_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motA_lb,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_back_leftR_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motB_lb,
+                          force=maxMotorForce)
+  p.setJointMotorControl2(vision,
+                          motor_back_leftS_joint,
+                          p.POSITION_CONTROL,
+                          targetPosition=motC_lb,
+                          force=maxMotorForce)
+
+  p.setGravity(0, 0, -10)
+  time.sleep(1. / 240.)
diff --git a/data/terrain.py b/data/terrain.py
index 53a357a3f..b6f5751c3 100644
--- a/data/terrain.py
+++ b/data/terrain.py
@@ -2,58 +2,57 @@ import math
 
 NUM_VERTS_X = 30
 NUM_VERTS_Y = 30
-totalVerts = NUM_VERTS_X*NUM_VERTS_Y
-totalTriangles = 2*(NUM_VERTS_X-1)*(NUM_VERTS_Y-1)
+totalVerts = NUM_VERTS_X * NUM_VERTS_Y
+totalTriangles = 2 * (NUM_VERTS_X - 1) * (NUM_VERTS_Y - 1)
 offset = -50.0
 TRIANGLE_SIZE = 1.
-waveheight=0.1
-gGroundVertices = [None] * totalVerts*3
-gGroundIndices = [None] * totalTriangles*3
-
-i=0
-
-for i in range (NUM_VERTS_X):		
-	for j in range (NUM_VERTS_Y):
-		gGroundVertices[(i+j*NUM_VERTS_X)*3+0] = (i-NUM_VERTS_X*0.5)*TRIANGLE_SIZE
-		gGroundVertices[(i+j*NUM_VERTS_X)*3+1] = (j-NUM_VERTS_Y*0.5)*TRIANGLE_SIZE
-		gGroundVertices[(i+j*NUM_VERTS_X)*3+2] = waveheight*math.sin(float(i))*math.cos(float(j)+offset)
-		                         
-index=0
-for i in range (NUM_VERTS_X-1):
-	for j in range (NUM_VERTS_Y-1):
-		gGroundIndices[index] = 1+j*NUM_VERTS_X+i
-		index+=1
-		gGroundIndices[index] = 1+j*NUM_VERTS_X+i+1
-		index+=1
-		gGroundIndices[index] = 1+(j+1)*NUM_VERTS_X+i+1
-		index+=1
-		gGroundIndices[index] = 1+j*NUM_VERTS_X+i
-		index+=1
-		gGroundIndices[index] = 1+(j+1)*NUM_VERTS_X+i+1
-		index+=1
-		gGroundIndices[index] = 1+(j+1)*NUM_VERTS_X+i
-		index+=1
+waveheight = 0.1
+gGroundVertices = [None] * totalVerts * 3
+gGroundIndices = [None] * totalTriangles * 3
+
+i = 0
+
+for i in range(NUM_VERTS_X):
+  for j in range(NUM_VERTS_Y):
+    gGroundVertices[(i + j * NUM_VERTS_X) * 3 + 0] = (i - NUM_VERTS_X * 0.5) * TRIANGLE_SIZE
+    gGroundVertices[(i + j * NUM_VERTS_X) * 3 + 1] = (j - NUM_VERTS_Y * 0.5) * TRIANGLE_SIZE
+    gGroundVertices[(i + j * NUM_VERTS_X) * 3 +
+                    2] = waveheight * math.sin(float(i)) * math.cos(float(j) + offset)
+
+index = 0
+for i in range(NUM_VERTS_X - 1):
+  for j in range(NUM_VERTS_Y - 1):
+    gGroundIndices[index] = 1 + j * NUM_VERTS_X + i
+    index += 1
+    gGroundIndices[index] = 1 + j * NUM_VERTS_X + i + 1
+    index += 1
+    gGroundIndices[index] = 1 + (j + 1) * NUM_VERTS_X + i + 1
+    index += 1
+    gGroundIndices[index] = 1 + j * NUM_VERTS_X + i
+    index += 1
+    gGroundIndices[index] = 1 + (j + 1) * NUM_VERTS_X + i + 1
+    index += 1
+    gGroundIndices[index] = 1 + (j + 1) * NUM_VERTS_X + i
+    index += 1
 
 #print(gGroundVertices)
 #print(gGroundIndices)
 
 print("o Terrain")
 
-for i in range (totalVerts):
-	print("v "),
-	print(gGroundVertices[i*3+0]),
-	print(" "),
-	print(gGroundVertices[i*3+1]),
-	print(" "),
-	print(gGroundVertices[i*3+2])
-		
-for i in range (totalTriangles):
-	print("f "),
-	print(gGroundIndices[i*3+0]),
-	print(" "),
-	print(gGroundIndices[i*3+1]),
-	print(" "),
-	print(gGroundIndices[i*3+2]),
-	print(" ")
-	
+for i in range(totalVerts):
+  print("v "),
+  print(gGroundVertices[i * 3 + 0]),
+  print(" "),
+  print(gGroundVertices[i * 3 + 1]),
+  print(" "),
+  print(gGroundVertices[i * 3 + 2])
 
+for i in range(totalTriangles):
+  print("f "),
+  print(gGroundIndices[i * 3 + 0]),
+  print(" "),
+  print(gGroundIndices[i * 3 + 1]),
+  print(" "),
+  print(gGroundIndices[i * 3 + 2]),
+  print(" ")
diff --git a/data/threecubes/threecubes.py b/data/threecubes/threecubes.py
index 48efea514..4069cc3e9 100644
--- a/data/threecubes/threecubes.py
+++ b/data/threecubes/threecubes.py
@@ -3,6 +3,5 @@ p.connect(p.DIRECT)
 p.loadPlugin("eglRendererPlugin")
 p.loadSDF("newsdf.sdf")
 while (1):
-	p.getCameraImage(320,240, flags=p.ER_NO_SEGMENTATION_MASK)
-	p.stepSimulation()
-	
\ No newline at end of file
+  p.getCameraImage(320, 240, flags=p.ER_NO_SEGMENTATION_MASK)
+  p.stepSimulation()
diff --git a/data/threecubes/threecubes_org.py b/data/threecubes/threecubes_org.py
index 3b47f082f..65c931c9c 100644
--- a/data/threecubes/threecubes_org.py
+++ b/data/threecubes/threecubes_org.py
@@ -3,20 +3,30 @@ useEGL = False
 useEGLGUI = False
 
 if useEGL:
-	if useEGLGUI:
-		p.connect(p.GUI, "window_backend=2")
-	else:	
-		p.connect(p.DIRECT)
-		p.loadPlugin("eglRendererPlugin")
+  if useEGLGUI:
+    p.connect(p.GUI, "window_backend=2")
+  else:
+    p.connect(p.DIRECT)
+    p.loadPlugin("eglRendererPlugin")
 else:
-	p.connect(p.GUI)
+  p.connect(p.GUI)
 
 p.loadURDF("threecubes.urdf", flags=p.URDF_USE_MATERIAL_COLORS_FROM_MTL)
 while (1):
-	
-	viewmat= [0.642787516117096, -0.4393851161003113, 0.6275069713592529, 0.0, 0.766044557094574, 0.36868777871131897, -0.5265407562255859, 0.0, -0.0, 0.8191521167755127, 0.5735764503479004, 0.0, 2.384185791015625e-07, 2.384185791015625e-07, -5.000000476837158, 1.0]
-	projmat= [0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0]
 
-	p.getCameraImage(64,64, viewMatrix=viewmat, projectionMatrix=projmat, flags=p.ER_NO_SEGMENTATION_MASK )
-	p.stepSimulation()
-	
\ No newline at end of file
+  viewmat = [
+      0.642787516117096, -0.4393851161003113, 0.6275069713592529, 0.0, 0.766044557094574,
+      0.36868777871131897, -0.5265407562255859, 0.0, -0.0, 0.8191521167755127, 0.5735764503479004,
+      0.0, 2.384185791015625e-07, 2.384185791015625e-07, -5.000000476837158, 1.0
+  ]
+  projmat = [
+      0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0,
+      0.0, 0.0, -0.02000020071864128, 0.0
+  ]
+
+  p.getCameraImage(64,
+                   64,
+                   viewMatrix=viewmat,
+                   projectionMatrix=projmat,
+                   flags=p.ER_NO_SEGMENTATION_MASK)
+  p.stepSimulation()
diff --git a/data/xacro_standalone.py b/data/xacro_standalone.py
index a1e77f6f9..384c91bf6 100755
--- a/data/xacro_standalone.py
+++ b/data/xacro_standalone.py
@@ -44,157 +44,162 @@ import xml
 from xml.dom.minidom import parse
 
 try:
-    _basestr = basestring
+  _basestr = basestring
 except NameError:
-    _basestr = str
+  _basestr = str
 
 # Dictionary of subtitution args
 substitution_args_context = {}
 
 
 class XacroException(Exception):
-    pass
+  pass
 
 
 def isnumber(x):
-    return hasattr(x, '__int__')
+  return hasattr(x, '__int__')
 
 
 # Better pretty printing of xml
 # Taken from http://ronrothman.com/public/leftbraned/xml-dom-minidom-toprettyxml-and-silly-whitespace/
 def fixed_writexml(self, writer, indent="", addindent="", newl=""):
-    # indent = current indentation
-    # addindent = indentation to add to higher levels
-    # newl = newline string
-    writer.write(indent + "<" + self.tagName)
-
-    attrs = self._get_attributes()
-    a_names = list(attrs.keys())
-    a_names.sort()
-
-    for a_name in a_names:
-        writer.write(" %s=\"" % a_name)
-        xml.dom.minidom._write_data(writer, attrs[a_name].value)
-        writer.write("\"")
-    if self.childNodes:
-        if len(self.childNodes) == 1 \
-           and self.childNodes[0].nodeType == xml.dom.minidom.Node.TEXT_NODE:
-            writer.write(">")
-            self.childNodes[0].writexml(writer, "", "", "")
-            writer.write("</%s>%s" % (self.tagName, newl))
-            return
-        writer.write(">%s" % (newl))
-        for node in self.childNodes:
-            # skip whitespace-only text nodes
-            if node.nodeType == xml.dom.minidom.Node.TEXT_NODE and \
-               not node.data.strip():
-                continue
-            node.writexml(writer, indent + addindent, addindent, newl)
-        writer.write("%s</%s>%s" % (indent, self.tagName, newl))
-    else:
-        writer.write("/>%s" % (newl))
+  # indent = current indentation
+  # addindent = indentation to add to higher levels
+  # newl = newline string
+  writer.write(indent + "<" + self.tagName)
+
+  attrs = self._get_attributes()
+  a_names = list(attrs.keys())
+  a_names.sort()
+
+  for a_name in a_names:
+    writer.write(" %s=\"" % a_name)
+    xml.dom.minidom._write_data(writer, attrs[a_name].value)
+    writer.write("\"")
+  if self.childNodes:
+    if len(self.childNodes) == 1 \
+       and self.childNodes[0].nodeType == xml.dom.minidom.Node.TEXT_NODE:
+      writer.write(">")
+      self.childNodes[0].writexml(writer, "", "", "")
+      writer.write("</%s>%s" % (self.tagName, newl))
+      return
+    writer.write(">%s" % (newl))
+    for node in self.childNodes:
+      # skip whitespace-only text nodes
+      if node.nodeType == xml.dom.minidom.Node.TEXT_NODE and \
+         not node.data.strip():
+        continue
+      node.writexml(writer, indent + addindent, addindent, newl)
+    writer.write("%s</%s>%s" % (indent, self.tagName, newl))
+  else:
+    writer.write("/>%s" % (newl))
+
+
 # replace minidom's function with ours
 xml.dom.minidom.Element.writexml = fixed_writexml
 
 
 class Table:
-    def __init__(self, parent=None):
-        self.parent = parent
-        self.table = {}
-
-    def __getitem__(self, key):
-        if key in self.table:
-            return self.table[key]
-        elif self.parent:
-            return self.parent[key]
-        else:
-            raise KeyError(key)
 
-    def __setitem__(self, key, value):
-        self.table[key] = value
+  def __init__(self, parent=None):
+    self.parent = parent
+    self.table = {}
 
-    def __contains__(self, key):
-        return \
-            key in self.table or \
-            (self.parent and key in self.parent)
+  def __getitem__(self, key):
+    if key in self.table:
+      return self.table[key]
+    elif self.parent:
+      return self.parent[key]
+    else:
+      raise KeyError(key)
+
+  def __setitem__(self, key, value):
+    self.table[key] = value
+
+  def __contains__(self, key):
+    return \
+        key in self.table or \
+        (self.parent and key in self.parent)
 
 
 class QuickLexer(object):
-    def __init__(self, **res):
-        self.str = ""
-        self.top = None
-        self.res = []
-        for k, v in res.items():
-            self.__setattr__(k, len(self.res))
-            self.res.append(v)
-
-    def lex(self, str):
-        self.str = str
-        self.top = None
-        self.next()
-
-    def peek(self):
-        return self.top
-
-    def next(self):
-        result = self.top
-        self.top = None
-        for i in range(len(self.res)):
-            m = re.match(self.res[i], self.str)
-            if m:
-                self.top = (i, m.group(0))
-                self.str = self.str[m.end():]
-                break
-        return result
+
+  def __init__(self, **res):
+    self.str = ""
+    self.top = None
+    self.res = []
+    for k, v in res.items():
+      self.__setattr__(k, len(self.res))
+      self.res.append(v)
+
+  def lex(self, str):
+    self.str = str
+    self.top = None
+    self.next()
+
+  def peek(self):
+    return self.top
+
+  def next(self):
+    result = self.top
+    self.top = None
+    for i in range(len(self.res)):
+      m = re.match(self.res[i], self.str)
+      if m:
+        self.top = (i, m.group(0))
+        self.str = self.str[m.end():]
+        break
+    return result
 
 
 def first_child_element(elt):
-    c = elt.firstChild
-    while c:
-        if c.nodeType == xml.dom.Node.ELEMENT_NODE:
-            return c
-        c = c.nextSibling
-    return None
+  c = elt.firstChild
+  while c:
+    if c.nodeType == xml.dom.Node.ELEMENT_NODE:
+      return c
+    c = c.nextSibling
+  return None
 
 
 def next_sibling_element(elt):
-    c = elt.nextSibling
-    while c:
-        if c.nodeType == xml.dom.Node.ELEMENT_NODE:
-            return c
-        c = c.nextSibling
-    return None
+  c = elt.nextSibling
+  while c:
+    if c.nodeType == xml.dom.Node.ELEMENT_NODE:
+      return c
+    c = c.nextSibling
+  return None
 
 
 # Pre-order traversal of the elements
 def next_element(elt):
-    child = first_child_element(elt)
-    if child:
-        return child
-    while elt and elt.nodeType == xml.dom.Node.ELEMENT_NODE:
-        next = next_sibling_element(elt)
-        if next:
-            return next
-        elt = elt.parentNode
-    return None
+  child = first_child_element(elt)
+  if child:
+    return child
+  while elt and elt.nodeType == xml.dom.Node.ELEMENT_NODE:
+    next = next_sibling_element(elt)
+    if next:
+      return next
+    elt = elt.parentNode
+  return None
 
 
 # Pre-order traversal of all the nodes
 def next_node(node):
-    if node.firstChild:
-        return node.firstChild
-    while node:
-        if node.nextSibling:
-            return node.nextSibling
-        node = node.parentNode
-    return None
+  if node.firstChild:
+    return node.firstChild
+  while node:
+    if node.nextSibling:
+      return node.nextSibling
+    node = node.parentNode
+  return None
 
 
 def child_nodes(elt):
-    c = elt.firstChild
-    while c:
-        yield c
-        c = c.nextSibling
+  c = elt.firstChild
+  while c:
+    yield c
+    c = c.nextSibling
+
 
 all_includes = []
 
@@ -210,496 +215,506 @@ include_no_matches_msg = """Include tag filename spec \"{}\" matched no files.""
 
 ## @throws XacroException if a parsing error occurs with an included document
 def process_includes(doc, base_dir):
-    namespaces = {}
-    previous = doc.documentElement
-    elt = next_element(previous)
-    while elt:
-        # Xacro should not use plain 'include' tags but only namespaced ones. Causes conflicts with
-        # other XML elements including Gazebo's <gazebo> extensions
-        is_include = False
-        if elt.tagName == 'xacro:include' or elt.tagName == 'include':
-
-            is_include = True
-            # Temporary fix for ROS Hydro and the xacro include scope problem
-            if elt.tagName == 'include':
-
-                # check if there is any element within the <include> tag. mostly we are concerned
-                # with Gazebo's <uri> element, but it could be anything. also, make sure the child
-                # nodes aren't just a single Text node, which is still considered a deprecated
-                # instance
-                if elt.childNodes and not (len(elt.childNodes) == 1 and
-                                           elt.childNodes[0].nodeType == elt.TEXT_NODE):
-                    # this is not intended to be a xacro element, so we can ignore it
-                    is_include = False
-                else:
-                    # throw a deprecated warning
-                    print(deprecated_include_msg, file=sys.stderr)
-
-        # Process current element depending on previous conditions
-        if is_include:
-            filename_spec = eval_text(elt.getAttribute('filename'), {})
-            if not os.path.isabs(filename_spec):
-                filename_spec = os.path.join(base_dir, filename_spec)
-
-            if re.search('[*[?]+', filename_spec):
-                # Globbing behaviour
-                filenames = sorted(glob.glob(filename_spec))
-                if len(filenames) == 0:
-                    print(include_no_matches_msg.format(filename_spec), file=sys.stderr)
-            else:
-                # Default behaviour
-                filenames = [filename_spec]
-
-            for filename in filenames:
-                global all_includes
-                all_includes.append(filename)
-                try:
-                    with open(filename) as f:
-                        try:
-                            included = parse(f)
-                        except Exception as e:
-                            raise XacroException(
-                                "included file \"%s\" generated an error during XML parsing: %s"
-                                % (filename, str(e)))
-                except IOError as e:
-                    raise XacroException("included file \"%s\" could not be opened: %s" % (filename, str(e)))
-
-                # Replaces the include tag with the elements of the included file
-                for c in child_nodes(included.documentElement):
-                    elt.parentNode.insertBefore(c.cloneNode(deep=True), elt)
-
-                # Grabs all the declared namespaces of the included document
-                for name, value in included.documentElement.attributes.items():
-                    if name.startswith('xmlns:'):
-                        namespaces[name] = value
-
-            elt.parentNode.removeChild(elt)
-            elt = None
+  namespaces = {}
+  previous = doc.documentElement
+  elt = next_element(previous)
+  while elt:
+    # Xacro should not use plain 'include' tags but only namespaced ones. Causes conflicts with
+    # other XML elements including Gazebo's <gazebo> extensions
+    is_include = False
+    if elt.tagName == 'xacro:include' or elt.tagName == 'include':
+
+      is_include = True
+      # Temporary fix for ROS Hydro and the xacro include scope problem
+      if elt.tagName == 'include':
+
+        # check if there is any element within the <include> tag. mostly we are concerned
+        # with Gazebo's <uri> element, but it could be anything. also, make sure the child
+        # nodes aren't just a single Text node, which is still considered a deprecated
+        # instance
+        if elt.childNodes and not (len(elt.childNodes) == 1 and
+                                   elt.childNodes[0].nodeType == elt.TEXT_NODE):
+          # this is not intended to be a xacro element, so we can ignore it
+          is_include = False
         else:
-            previous = elt
+          # throw a deprecated warning
+          print(deprecated_include_msg, file=sys.stderr)
+
+    # Process current element depending on previous conditions
+    if is_include:
+      filename_spec = eval_text(elt.getAttribute('filename'), {})
+      if not os.path.isabs(filename_spec):
+        filename_spec = os.path.join(base_dir, filename_spec)
+
+      if re.search('[*[?]+', filename_spec):
+        # Globbing behaviour
+        filenames = sorted(glob.glob(filename_spec))
+        if len(filenames) == 0:
+          print(include_no_matches_msg.format(filename_spec), file=sys.stderr)
+      else:
+        # Default behaviour
+        filenames = [filename_spec]
+
+      for filename in filenames:
+        global all_includes
+        all_includes.append(filename)
+        try:
+          with open(filename) as f:
+            try:
+              included = parse(f)
+            except Exception as e:
+              raise XacroException(
+                  "included file \"%s\" generated an error during XML parsing: %s" %
+                  (filename, str(e)))
+        except IOError as e:
+          raise XacroException("included file \"%s\" could not be opened: %s" % (filename, str(e)))
+
+        # Replaces the include tag with the elements of the included file
+        for c in child_nodes(included.documentElement):
+          elt.parentNode.insertBefore(c.cloneNode(deep=True), elt)
+
+        # Grabs all the declared namespaces of the included document
+        for name, value in included.documentElement.attributes.items():
+          if name.startswith('xmlns:'):
+            namespaces[name] = value
+
+      elt.parentNode.removeChild(elt)
+      elt = None
+    else:
+      previous = elt
 
-        elt = next_element(previous)
+    elt = next_element(previous)
 
-    # Makes sure the final document declares all the namespaces of the included documents.
-    for k, v in namespaces.items():
-        doc.documentElement.setAttribute(k, v)
+  # Makes sure the final document declares all the namespaces of the included documents.
+  for k, v in namespaces.items():
+    doc.documentElement.setAttribute(k, v)
 
 
 # Returns a dictionary: { macro_name => macro_xml_block }
 def grab_macros(doc):
-    macros = {}
+  macros = {}
 
-    previous = doc.documentElement
-    elt = next_element(previous)
-    while elt:
-        if elt.tagName == 'macro' or elt.tagName == 'xacro:macro':
-            name = elt.getAttribute('name')
+  previous = doc.documentElement
+  elt = next_element(previous)
+  while elt:
+    if elt.tagName == 'macro' or elt.tagName == 'xacro:macro':
+      name = elt.getAttribute('name')
 
-            macros[name] = elt
-            macros['xacro:' + name] = elt
+      macros[name] = elt
+      macros['xacro:' + name] = elt
 
-            elt.parentNode.removeChild(elt)
-            elt = None
-        else:
-            previous = elt
+      elt.parentNode.removeChild(elt)
+      elt = None
+    else:
+      previous = elt
 
-        elt = next_element(previous)
-    return macros
+    elt = next_element(previous)
+  return macros
 
 
 # Returns a Table of the properties
 def grab_properties(doc):
-    table = Table()
+  table = Table()
+
+  previous = doc.documentElement
+  elt = next_element(previous)
+  while elt:
+    if elt.tagName == 'property' or elt.tagName == 'xacro:property':
+      name = elt.getAttribute('name')
+      value = None
+
+      if elt.hasAttribute('value'):
+        value = elt.getAttribute('value')
+      else:
+        name = '**' + name
+        value = elt  # debug
+
+      bad = string.whitespace + "${}"
+      has_bad = False
+      for b in bad:
+        if b in name:
+          has_bad = True
+          break
+
+      if has_bad:
+        sys.stderr.write('Property names may not have whitespace, ' + '"{", "}", or "$" : "' +
+                         name + '"')
+      else:
+        table[name] = value
+
+      elt.parentNode.removeChild(elt)
+      elt = None
+    else:
+      previous = elt
 
-    previous = doc.documentElement
     elt = next_element(previous)
-    while elt:
-        if elt.tagName == 'property' or elt.tagName == 'xacro:property':
-            name = elt.getAttribute('name')
-            value = None
-
-            if elt.hasAttribute('value'):
-                value = elt.getAttribute('value')
-            else:
-                name = '**' + name
-                value = elt  # debug
-
-            bad = string.whitespace + "${}"
-            has_bad = False
-            for b in bad:
-                if b in name:
-                    has_bad = True
-                    break
-
-            if has_bad:
-                sys.stderr.write('Property names may not have whitespace, ' +
-                                 '"{", "}", or "$" : "' + name + '"')
-            else:
-                table[name] = value
-
-            elt.parentNode.removeChild(elt)
-            elt = None
-        else:
-            previous = elt
-
-        elt = next_element(previous)
-    return table
+  return table
 
 
 def eat_ignore(lex):
-    while lex.peek() and lex.peek()[0] == lex.IGNORE:
-        lex.next()
+  while lex.peek() and lex.peek()[0] == lex.IGNORE:
+    lex.next()
 
 
 def eval_lit(lex, symbols):
-    eat_ignore(lex)
-    if lex.peek()[0] == lex.NUMBER:
-        return float(lex.next()[1])
-    if lex.peek()[0] == lex.SYMBOL:
-        try:
-            key = lex.next()[1]
-            value = symbols[key]
-        except KeyError as ex:
-            raise XacroException("Property wasn't defined: %s" % str(ex))
-        if not (isnumber(value) or isinstance(value, _basestr)):
-            if value is None:
-                raise XacroException("Property %s recursively used" % key)
-            raise XacroException("WTF2")
-        try:
-            return int(value)
-        except:
-            try:
-                return float(value)
-            except:
-                # prevent infinite recursion
-                symbols[key] = None
-                result = eval_text(value, symbols)
-                # restore old entry
-                symbols[key] = value
-                return result
-    raise XacroException("Bad literal")
+  eat_ignore(lex)
+  if lex.peek()[0] == lex.NUMBER:
+    return float(lex.next()[1])
+  if lex.peek()[0] == lex.SYMBOL:
+    try:
+      key = lex.next()[1]
+      value = symbols[key]
+    except KeyError as ex:
+      raise XacroException("Property wasn't defined: %s" % str(ex))
+    if not (isnumber(value) or isinstance(value, _basestr)):
+      if value is None:
+        raise XacroException("Property %s recursively used" % key)
+      raise XacroException("WTF2")
+    try:
+      return int(value)
+    except:
+      try:
+        return float(value)
+      except:
+        # prevent infinite recursion
+        symbols[key] = None
+        result = eval_text(value, symbols)
+        # restore old entry
+        symbols[key] = value
+        return result
+  raise XacroException("Bad literal")
 
 
 def eval_factor(lex, symbols):
-    eat_ignore(lex)
+  eat_ignore(lex)
 
-    neg = 1
-    if lex.peek()[1] == '-':
-        lex.next()
-        neg = -1
+  neg = 1
+  if lex.peek()[1] == '-':
+    lex.next()
+    neg = -1
 
-    if lex.peek()[0] in [lex.NUMBER, lex.SYMBOL]:
-        return neg * eval_lit(lex, symbols)
-    if lex.peek()[0] == lex.LPAREN:
-        lex.next()
-        eat_ignore(lex)
-        result = eval_expr(lex, symbols)
-        eat_ignore(lex)
-        if lex.next()[0] != lex.RPAREN:
-            raise XacroException("Unmatched left paren")
-        eat_ignore(lex)
-        return neg * result
+  if lex.peek()[0] in [lex.NUMBER, lex.SYMBOL]:
+    return neg * eval_lit(lex, symbols)
+  if lex.peek()[0] == lex.LPAREN:
+    lex.next()
+    eat_ignore(lex)
+    result = eval_expr(lex, symbols)
+    eat_ignore(lex)
+    if lex.next()[0] != lex.RPAREN:
+      raise XacroException("Unmatched left paren")
+    eat_ignore(lex)
+    return neg * result
 
-    raise XacroException("Misplaced operator")
+  raise XacroException("Misplaced operator")
 
 
 def eval_term(lex, symbols):
+  eat_ignore(lex)
+
+  result = 0
+  if lex.peek()[0] in [lex.NUMBER, lex.SYMBOL, lex.LPAREN] \
+          or lex.peek()[1] == '-':
+    result = eval_factor(lex, symbols)
+
+  eat_ignore(lex)
+  while lex.peek() and lex.peek()[1] in ['*', '/']:
+    op = lex.next()[1]
+    n = eval_factor(lex, symbols)
+
+    if op == '*':
+      result = float(result) * float(n)
+    elif op == '/':
+      result = float(result) / float(n)
+    else:
+      raise XacroException("WTF")
     eat_ignore(lex)
+  return result
 
-    result = 0
-    if lex.peek()[0] in [lex.NUMBER, lex.SYMBOL, lex.LPAREN] \
-            or lex.peek()[1] == '-':
-        result = eval_factor(lex, symbols)
 
-    eat_ignore(lex)
-    while lex.peek() and lex.peek()[1] in ['*', '/']:
-        op = lex.next()[1]
-        n = eval_factor(lex, symbols)
-
-        if op == '*':
-            result = float(result) * float(n)
-        elif op == '/':
-            result = float(result) / float(n)
-        else:
-            raise XacroException("WTF")
-        eat_ignore(lex)
-    return result
+def eval_expr(lex, symbols):
+  eat_ignore(lex)
 
+  op = None
+  if lex.peek()[0] == lex.OP:
+    op = lex.next()[1]
+    if not op in ['+', '-']:
+      raise XacroException("Invalid operation. Must be '+' or '-'")
 
-def eval_expr(lex, symbols):
-    eat_ignore(lex)
+  result = eval_term(lex, symbols)
+  if op == '-':
+    result = -float(result)
 
-    op = None
-    if lex.peek()[0] == lex.OP:
-        op = lex.next()[1]
-        if not op in ['+', '-']:
-            raise XacroException("Invalid operation. Must be '+' or '-'")
+  eat_ignore(lex)
+  while lex.peek() and lex.peek()[1] in ['+', '-']:
+    op = lex.next()[1]
+    n = eval_term(lex, symbols)
 
-    result = eval_term(lex, symbols)
+    if op == '+':
+      result = float(result) + float(n)
     if op == '-':
-        result = -float(result)
-
+      result = float(result) - float(n)
     eat_ignore(lex)
-    while lex.peek() and lex.peek()[1] in ['+', '-']:
-        op = lex.next()[1]
-        n = eval_term(lex, symbols)
-
-        if op == '+':
-            result = float(result) + float(n)
-        if op == '-':
-            result = float(result) - float(n)
-        eat_ignore(lex)
-    return result
+  return result
 
 
 def eval_text(text, symbols):
-    def handle_expr(s):
-        lex = QuickLexer(IGNORE=r"\s+",
-                         NUMBER=r"(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?",
-                         SYMBOL=r"[a-zA-Z_]\w*",
-                         OP=r"[\+\-\*/^]",
-                         LPAREN=r"\(",
-                         RPAREN=r"\)")
-        lex.lex(s)
-        return eval_expr(lex, symbols)
-
-    def handle_extension(s):
-        return ("$(%s)" % s)
-
-    results = []
-    lex = QuickLexer(DOLLAR_DOLLAR_BRACE=r"\$\$+\{",
-                     EXPR=r"\$\{[^\}]*\}",
-                     EXTENSION=r"\$\([^\)]*\)",
-                     TEXT=r"([^\$]|\$[^{(]|\$$)+")
-    lex.lex(text)
-    while lex.peek():
-        if lex.peek()[0] == lex.EXPR:
-            results.append(handle_expr(lex.next()[1][2:-1]))
-        elif lex.peek()[0] == lex.EXTENSION:
-            results.append(handle_extension(lex.next()[1][2:-1]))
-        elif lex.peek()[0] == lex.TEXT:
-            results.append(lex.next()[1])
-        elif lex.peek()[0] == lex.DOLLAR_DOLLAR_BRACE:
-            results.append(lex.next()[1][1:])
-    return ''.join(map(str, results))
+
+  def handle_expr(s):
+    lex = QuickLexer(IGNORE=r"\s+",
+                     NUMBER=r"(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?",
+                     SYMBOL=r"[a-zA-Z_]\w*",
+                     OP=r"[\+\-\*/^]",
+                     LPAREN=r"\(",
+                     RPAREN=r"\)")
+    lex.lex(s)
+    return eval_expr(lex, symbols)
+
+  def handle_extension(s):
+    return ("$(%s)" % s)
+
+  results = []
+  lex = QuickLexer(DOLLAR_DOLLAR_BRACE=r"\$\$+\{",
+                   EXPR=r"\$\{[^\}]*\}",
+                   EXTENSION=r"\$\([^\)]*\)",
+                   TEXT=r"([^\$]|\$[^{(]|\$$)+")
+  lex.lex(text)
+  while lex.peek():
+    if lex.peek()[0] == lex.EXPR:
+      results.append(handle_expr(lex.next()[1][2:-1]))
+    elif lex.peek()[0] == lex.EXTENSION:
+      results.append(handle_extension(lex.next()[1][2:-1]))
+    elif lex.peek()[0] == lex.TEXT:
+      results.append(lex.next()[1])
+    elif lex.peek()[0] == lex.DOLLAR_DOLLAR_BRACE:
+      results.append(lex.next()[1][1:])
+  return ''.join(map(str, results))
 
 
 # Expands macros, replaces properties, and evaluates expressions
 def eval_all(root, macros, symbols):
-    # Evaluates the attributes for the root node
-    for at in root.attributes.items():
-        result = eval_text(at[1], symbols)
-        root.setAttribute(at[0], result)
-
-    previous = root
-    node = next_node(previous)
-    while node:
-        if node.nodeType == xml.dom.Node.ELEMENT_NODE:
-            if node.tagName in macros:
-                body = macros[node.tagName].cloneNode(deep=True)
-                params = body.getAttribute('params').split()
-
-                # Parse default values for any parameters
-                defaultmap = {}
-                for param in params[:]:
-                    splitParam = param.split(':=')
-
-                    if len(splitParam) == 2:
-                        defaultmap[splitParam[0]] = splitParam[1]
-                        params.remove(param)
-                        params.append(splitParam[0])
-                        
-                    elif len(splitParam) != 1:
-                        raise XacroException("Invalid parameter definition")
-
-                # Expands the macro
-                scoped = Table(symbols)
-                for name, value in node.attributes.items():
-                    if not name in params:
-                        raise XacroException("Invalid parameter \"%s\" while expanding macro \"%s\"" %
-                                             (str(name), str(node.tagName)))
-                    params.remove(name)
-                    scoped[name] = eval_text(value, symbols)
-
-                # Pulls out the block arguments, in order
-                cloned = node.cloneNode(deep=True)
-                eval_all(cloned, macros, symbols)
-                block = cloned.firstChild
-                for param in params[:]:
-                    if param[0] == '*':
-                        while block and block.nodeType != xml.dom.Node.ELEMENT_NODE:
-                            block = block.nextSibling
-                        if not block:
-                            raise XacroException("Not enough blocks while evaluating macro %s" % str(node.tagName))
-                        params.remove(param)
-                        scoped[param] = block
-                        block = block.nextSibling
-
-                # Try to load defaults for any remaining non-block parameters
-                for param in params[:]:
-                    if param[0] != '*' and param in defaultmap:
-                        scoped[param] = defaultmap[param]
-                        params.remove(param)
-
-                if params:
-                    raise XacroException("Parameters [%s] were not set for macro %s" %
-                                         (",".join(params), str(node.tagName)))
-                eval_all(body, macros, scoped)
-
-                # Replaces the macro node with the expansion
-                for e in list(child_nodes(body)):  # Ew
-                    node.parentNode.insertBefore(e, node)
-                node.parentNode.removeChild(node)
-
-                node = None
-            elif node.tagName == 'arg' or node.tagName == 'xacro:arg':
-                name = node.getAttribute('name')
-                if not name:
-                    raise XacroException("Argument name missing")
-                default = node.getAttribute('default')
-                if default and name not in substitution_args_context['arg']:
-                    substitution_args_context['arg'][name] = default
-                
-                node.parentNode.removeChild(node)
-                node = None
-
-            elif node.tagName == 'insert_block' or node.tagName == 'xacro:insert_block':
-                name = node.getAttribute('name')
-
-                if ("**" + name) in symbols:
-                    # Multi-block
-                    block = symbols['**' + name]
-
-                    for e in list(child_nodes(block)):
-                        node.parentNode.insertBefore(e.cloneNode(deep=True), node)
-                    node.parentNode.removeChild(node)
-                elif ("*" + name) in symbols:
-                    # Single block
-                    block = symbols['*' + name]
-
-                    node.parentNode.insertBefore(block.cloneNode(deep=True), node)
-                    node.parentNode.removeChild(node)
-                else:
-                    raise XacroException("Block \"%s\" was never declared" % name)
-
-                node = None
-            elif node.tagName in ['if', 'xacro:if', 'unless', 'xacro:unless']:
-                value = eval_text(node.getAttribute('value'), symbols)
-                try: 
-                    if value == 'true': keep = True
-                    elif value == 'false': keep = False
-                    else: keep = float(value)
-                except ValueError:
-                    raise XacroException("Xacro conditional evaluated to \"%s\". Acceptable evaluations are one of [\"1\",\"true\",\"0\",\"false\"]" % value)
-                if node.tagName in ['unless', 'xacro:unless']: keep = not keep
-                if keep:
-                    for e in list(child_nodes(node)):
-                        node.parentNode.insertBefore(e.cloneNode(deep=True), node)
-
-                node.parentNode.removeChild(node)
-            else:
-                # Evals the attributes
-                for at in node.attributes.items():
-                    result = eval_text(at[1], symbols)
-                    node.setAttribute(at[0], result)
-                previous = node
-        elif node.nodeType == xml.dom.Node.TEXT_NODE:
-            node.data = eval_text(node.data, symbols)
-            previous = node
+  # Evaluates the attributes for the root node
+  for at in root.attributes.items():
+    result = eval_text(at[1], symbols)
+    root.setAttribute(at[0], result)
+
+  previous = root
+  node = next_node(previous)
+  while node:
+    if node.nodeType == xml.dom.Node.ELEMENT_NODE:
+      if node.tagName in macros:
+        body = macros[node.tagName].cloneNode(deep=True)
+        params = body.getAttribute('params').split()
+
+        # Parse default values for any parameters
+        defaultmap = {}
+        for param in params[:]:
+          splitParam = param.split(':=')
+
+          if len(splitParam) == 2:
+            defaultmap[splitParam[0]] = splitParam[1]
+            params.remove(param)
+            params.append(splitParam[0])
+
+          elif len(splitParam) != 1:
+            raise XacroException("Invalid parameter definition")
+
+        # Expands the macro
+        scoped = Table(symbols)
+        for name, value in node.attributes.items():
+          if not name in params:
+            raise XacroException("Invalid parameter \"%s\" while expanding macro \"%s\"" %
+                                 (str(name), str(node.tagName)))
+          params.remove(name)
+          scoped[name] = eval_text(value, symbols)
+
+        # Pulls out the block arguments, in order
+        cloned = node.cloneNode(deep=True)
+        eval_all(cloned, macros, symbols)
+        block = cloned.firstChild
+        for param in params[:]:
+          if param[0] == '*':
+            while block and block.nodeType != xml.dom.Node.ELEMENT_NODE:
+              block = block.nextSibling
+            if not block:
+              raise XacroException("Not enough blocks while evaluating macro %s" %
+                                   str(node.tagName))
+            params.remove(param)
+            scoped[param] = block
+            block = block.nextSibling
+
+        # Try to load defaults for any remaining non-block parameters
+        for param in params[:]:
+          if param[0] != '*' and param in defaultmap:
+            scoped[param] = defaultmap[param]
+            params.remove(param)
+
+        if params:
+          raise XacroException("Parameters [%s] were not set for macro %s" %
+                               (",".join(params), str(node.tagName)))
+        eval_all(body, macros, scoped)
+
+        # Replaces the macro node with the expansion
+        for e in list(child_nodes(body)):  # Ew
+          node.parentNode.insertBefore(e, node)
+        node.parentNode.removeChild(node)
+
+        node = None
+      elif node.tagName == 'arg' or node.tagName == 'xacro:arg':
+        name = node.getAttribute('name')
+        if not name:
+          raise XacroException("Argument name missing")
+        default = node.getAttribute('default')
+        if default and name not in substitution_args_context['arg']:
+          substitution_args_context['arg'][name] = default
+
+        node.parentNode.removeChild(node)
+        node = None
+
+      elif node.tagName == 'insert_block' or node.tagName == 'xacro:insert_block':
+        name = node.getAttribute('name')
+
+        if ("**" + name) in symbols:
+          # Multi-block
+          block = symbols['**' + name]
+
+          for e in list(child_nodes(block)):
+            node.parentNode.insertBefore(e.cloneNode(deep=True), node)
+          node.parentNode.removeChild(node)
+        elif ("*" + name) in symbols:
+          # Single block
+          block = symbols['*' + name]
+
+          node.parentNode.insertBefore(block.cloneNode(deep=True), node)
+          node.parentNode.removeChild(node)
         else:
-            previous = node
+          raise XacroException("Block \"%s\" was never declared" % name)
 
-        node = next_node(previous)
-    return macros
+        node = None
+      elif node.tagName in ['if', 'xacro:if', 'unless', 'xacro:unless']:
+        value = eval_text(node.getAttribute('value'), symbols)
+        try:
+          if value == 'true': keep = True
+          elif value == 'false': keep = False
+          else: keep = float(value)
+        except ValueError:
+          raise XacroException(
+              "Xacro conditional evaluated to \"%s\". Acceptable evaluations are one of [\"1\",\"true\",\"0\",\"false\"]"
+              % value)
+        if node.tagName in ['unless', 'xacro:unless']: keep = not keep
+        if keep:
+          for e in list(child_nodes(node)):
+            node.parentNode.insertBefore(e.cloneNode(deep=True), node)
+
+        node.parentNode.removeChild(node)
+      else:
+        # Evals the attributes
+        for at in node.attributes.items():
+          result = eval_text(at[1], symbols)
+          node.setAttribute(at[0], result)
+        previous = node
+    elif node.nodeType == xml.dom.Node.TEXT_NODE:
+      node.data = eval_text(node.data, symbols)
+      previous = node
+    else:
+      previous = node
+
+    node = next_node(previous)
+  return macros
 
 
 # Expands everything except includes
 def eval_self_contained(doc):
-    macros = grab_macros(doc)
-    symbols = grab_properties(doc)
-    eval_all(doc.documentElement, macros, symbols)
+  macros = grab_macros(doc)
+  symbols = grab_properties(doc)
+  eval_all(doc.documentElement, macros, symbols)
 
 
 def print_usage(exit_code=0):
-    print("Usage: %s [-o <output>] <input>" % 'xacro.py')
-    print("       %s --deps       Prints dependencies" % 'xacro.py')
-    print("       %s --includes   Only evalutes includes" % 'xacro.py')
-    sys.exit(exit_code)
+  print("Usage: %s [-o <output>] <input>" % 'xacro.py')
+  print("       %s --deps       Prints dependencies" % 'xacro.py')
+  print("       %s --includes   Only evalutes includes" % 'xacro.py')
+  sys.exit(exit_code)
 
 
 def set_substitution_args_context(context={}):
-    substitution_args_context['arg'] = context
+  substitution_args_context['arg'] = context
+
 
 def open_output(output_filename):
-    if output_filename is None:
-        return sys.stdout
-    else:
-        return open(output_filename, 'w') 
+  if output_filename is None:
+    return sys.stdout
+  else:
+    return open(output_filename, 'w')
+
 
 def main():
-    try:
-        opts, args = getopt.gnu_getopt(sys.argv[1:], "ho:", ['deps', 'includes'])
-    except getopt.GetoptError as err:
-        print(str(err))
-        print_usage(2)
-
-    just_deps = False
-    just_includes = False
-
-    output_filename = None
-    for o, a in opts:
-        if o == '-h':
-            print_usage(0)
-        elif o == '-o':
-            output_filename = a 
-        elif o == '--deps':
-            just_deps = True
-        elif o == '--includes':
-            just_includes = True
-
-    if len(args) < 1:
-        print("No input given")
-        print_usage(2)
-
-    # Process substitution args
-    # set_substitution_args_context(load_mappings(sys.argv))
-    set_substitution_args_context((sys.argv))
-
-    f = open(args[0])
-    doc = None
-    try:
-        doc = parse(f)
-    except xml.parsers.expat.ExpatError:
-        sys.stderr.write("Expat parsing error.  Check that:\n")
-        sys.stderr.write(" - Your XML is correctly formed\n")
-        sys.stderr.write(" - You have the xacro xmlns declaration: " +
-                         "xmlns:xacro=\"http://www.ros.org/wiki/xacro\"\n")
-        sys.stderr.write("\n")
-        raise
-    finally:
-        f.close()
-
-    process_includes(doc, os.path.dirname(args[0]))
-    if just_deps:
-        for inc in all_includes:
-            sys.stdout.write(inc + " ")
-        sys.stdout.write("\n")
-    elif just_includes:
-        doc.writexml(open_output(output_filename))
-        print()
-    else:
-        eval_self_contained(doc)
-        banner = [xml.dom.minidom.Comment(c) for c in
-                  [" %s " % ('=' * 83),
-                   " |    This document was autogenerated by xacro from %-30s | " % args[0],
-                   " |    EDITING THIS FILE BY HAND IS NOT RECOMMENDED  %-30s | " % "",
-                   " %s " % ('=' * 83)]]
-        first = doc.firstChild
-        for comment in banner:
-            doc.insertBefore(comment, first)
-
-        open_output(output_filename).write(doc.toprettyxml(indent='  '))
-        print()
+  try:
+    opts, args = getopt.gnu_getopt(sys.argv[1:], "ho:", ['deps', 'includes'])
+  except getopt.GetoptError as err:
+    print(str(err))
+    print_usage(2)
+
+  just_deps = False
+  just_includes = False
+
+  output_filename = None
+  for o, a in opts:
+    if o == '-h':
+      print_usage(0)
+    elif o == '-o':
+      output_filename = a
+    elif o == '--deps':
+      just_deps = True
+    elif o == '--includes':
+      just_includes = True
+
+  if len(args) < 1:
+    print("No input given")
+    print_usage(2)
+
+  # Process substitution args
+  # set_substitution_args_context(load_mappings(sys.argv))
+  set_substitution_args_context((sys.argv))
+
+  f = open(args[0])
+  doc = None
+  try:
+    doc = parse(f)
+  except xml.parsers.expat.ExpatError:
+    sys.stderr.write("Expat parsing error.  Check that:\n")
+    sys.stderr.write(" - Your XML is correctly formed\n")
+    sys.stderr.write(" - You have the xacro xmlns declaration: " +
+                     "xmlns:xacro=\"http://www.ros.org/wiki/xacro\"\n")
+    sys.stderr.write("\n")
+    raise
+  finally:
+    f.close()
+
+  process_includes(doc, os.path.dirname(args[0]))
+  if just_deps:
+    for inc in all_includes:
+      sys.stdout.write(inc + " ")
+    sys.stdout.write("\n")
+  elif just_includes:
+    doc.writexml(open_output(output_filename))
+    print()
+  else:
+    eval_self_contained(doc)
+    banner = [
+        xml.dom.minidom.Comment(c) for c in [
+            " %s " % ('=' * 83),
+            " |    This document was autogenerated by xacro from %-30s | " % args[0],
+            " |    EDITING THIS FILE BY HAND IS NOT RECOMMENDED  %-30s | " % "",
+            " %s " % ('=' * 83)
+        ]
+    ]
+    first = doc.firstChild
+    for comment in banner:
+      doc.insertBefore(comment, first)
+
+    open_output(output_filename).write(doc.toprettyxml(indent='  '))
+    print()
+
 
 if __name__ == '__main__':
-    main()
+  main()
diff --git a/docs/pybullet_quickstart_guide/PlainPreview/BuildMarkdeepUtility.py b/docs/pybullet_quickstart_guide/PlainPreview/BuildMarkdeepUtility.py
index 16793346e..5bf40e1a8 100644
--- a/docs/pybullet_quickstart_guide/PlainPreview/BuildMarkdeepUtility.py
+++ b/docs/pybullet_quickstart_guide/PlainPreview/BuildMarkdeepUtility.py
@@ -1,43 +1,44 @@
 import re

 

-if(__name__=="__main__"):

-    # Assemble the script which embeds the Markdeep page into the preview blog

-    PreviewBlogPage=open("PreviewBlogPage.htm","rb").read().decode("utf-8");

-    HeadMatch=re.search("<head(.*?)>(.*?)</head>",PreviewBlogPage,re.DOTALL);

-    HeadAttributes=HeadMatch.group(1);

-    FullDocumentHead=HeadMatch.group(2);

-    BodyMatch=re.search("<body(.*?)>(.*?)</body>",PreviewBlogPage,re.DOTALL);

-    BodyAttributes=BodyMatch.group(1);

-    FullPreviewBody=BodyMatch.group(2);

-    ArticleHTMLCodeMacro="$(ARTICLE_HTML_CODE)";

-    iArticleHTMLCodeMacro=FullPreviewBody.find(ArticleHTMLCodeMacro);

-    DocumentBodyPrefix=FullPreviewBody[0:iArticleHTMLCodeMacro];

-    DocumentBodySuffix=FullPreviewBody[iArticleHTMLCodeMacro+len(ArticleHTMLCodeMacro):];

-    FullPrepareHTMLCode=open("PrepareHTML.js","rb").read().decode("utf-8");

-    ReplacementList=[

-        ("$(FULL_DOCUMENT_HEAD)",FullDocumentHead),

-        ("$(DOCUMENT_BODY_PREFIX)",DocumentBodyPrefix),

-        ("$(DOCUMENT_BODY_SUFFIX)",DocumentBodySuffix)

-    ];

-    for Macro,Replacement in ReplacementList:

-        FullPrepareHTMLCode=FullPrepareHTMLCode.replace(Macro,Replacement.replace("\r\n","\\r\\n\\\r\n").replace("'","\\'"));

-    # Generate code which sets body and head attributes appropriately

-    for Element,AttributeCode in [("head",HeadAttributes),("body",BodyAttributes)]:

-        FullPrepareHTMLCode+="\r\n// Setting "+Element+" attributes\r\n";

-        for Match in re.finditer("(\\w+)=\\\"(.*?)\\\"",AttributeCode):

-            FullPrepareHTMLCode+="document."+Element+".setAttribute(\""+Match.group(1)+"\",\""+Match.group(2)+"\");\r\n";

-    open("PrepareHTML.full.js","wb").write(FullPrepareHTMLCode.encode("utf-8"));

+if (__name__ == "__main__"):

+  # Assemble the script which embeds the Markdeep page into the preview blog

+  PreviewBlogPage = open("PreviewBlogPage.htm", "rb").read().decode("utf-8")

+  HeadMatch = re.search("<head(.*?)>(.*?)</head>", PreviewBlogPage, re.DOTALL)

+  HeadAttributes = HeadMatch.group(1)

+  FullDocumentHead = HeadMatch.group(2)

+  BodyMatch = re.search("<body(.*?)>(.*?)</body>", PreviewBlogPage, re.DOTALL)

+  BodyAttributes = BodyMatch.group(1)

+  FullPreviewBody = BodyMatch.group(2)

+  ArticleHTMLCodeMacro = "$(ARTICLE_HTML_CODE)"

+  iArticleHTMLCodeMacro = FullPreviewBody.find(ArticleHTMLCodeMacro)

+  DocumentBodyPrefix = FullPreviewBody[0:iArticleHTMLCodeMacro]

+  DocumentBodySuffix = FullPreviewBody[iArticleHTMLCodeMacro + len(ArticleHTMLCodeMacro):]

+  FullPrepareHTMLCode = open("PrepareHTML.js", "rb").read().decode("utf-8")

+  ReplacementList = [("$(FULL_DOCUMENT_HEAD)", FullDocumentHead),

+                     ("$(DOCUMENT_BODY_PREFIX)", DocumentBodyPrefix),

+                     ("$(DOCUMENT_BODY_SUFFIX)", DocumentBodySuffix)]

+  for Macro, Replacement in ReplacementList:

+    FullPrepareHTMLCode = FullPrepareHTMLCode.replace(

+        Macro,

+        Replacement.replace("\r\n", "\\r\\n\\\r\n").replace("'", "\\'"))

+  # Generate code which sets body and head attributes appropriately

+  for Element, AttributeCode in [("head", HeadAttributes), ("body", BodyAttributes)]:

+    FullPrepareHTMLCode += "\r\n// Setting " + Element + " attributes\r\n"

+    for Match in re.finditer("(\\w+)=\\\"(.*?)\\\"", AttributeCode):

+      FullPrepareHTMLCode += "document." + Element + ".setAttribute(\"" + Match.group(

+          1) + "\",\"" + Match.group(2) + "\");\r\n"

+  open("PrepareHTML.full.js", "wb").write(FullPrepareHTMLCode.encode("utf-8"))

 

-    # Concatenate all the scripts together

-    SourceFileList=[

-                    "PrepareHTML.full.js",

-                    "SetMarkdeepMode.js",

-                    "markdeep.min.js",

-                    "DisplayMarkdeepOutput.js",

-                    "InvokeMathJax.js"

-    ];

-    OutputCode="\r\n\r\n".join(["// "+SourceFile+"\r\n\r\n"+open(SourceFile,"rb").read().decode("utf-8") for SourceFile in SourceFileList]);

-    OutputFile=open("MarkdeepUtility.js","wb");

-    OutputFile.write(OutputCode.encode("utf-8"));

-    OutputFile.close();

-    print("Done.");

+  # Concatenate all the scripts together

+  SourceFileList = [

+      "PrepareHTML.full.js", "SetMarkdeepMode.js", "markdeep.min.js", "DisplayMarkdeepOutput.js",

+      "InvokeMathJax.js"

+  ]

+  OutputCode = "\r\n\r\n".join([

+      "// " + SourceFile + "\r\n\r\n" + open(SourceFile, "rb").read().decode("utf-8")

+      for SourceFile in SourceFileList

+  ])

+  OutputFile = open("MarkdeepUtility.js", "wb")

+  OutputFile.write(OutputCode.encode("utf-8"))

+  OutputFile.close()

+  print("Done.")

diff --git a/docs/pybullet_quickstart_guide/WordpressPreview/BuildMarkdeepUtility.py b/docs/pybullet_quickstart_guide/WordpressPreview/BuildMarkdeepUtility.py
index 16793346e..5bf40e1a8 100644
--- a/docs/pybullet_quickstart_guide/WordpressPreview/BuildMarkdeepUtility.py
+++ b/docs/pybullet_quickstart_guide/WordpressPreview/BuildMarkdeepUtility.py
@@ -1,43 +1,44 @@
 import re

 

-if(__name__=="__main__"):

-    # Assemble the script which embeds the Markdeep page into the preview blog

-    PreviewBlogPage=open("PreviewBlogPage.htm","rb").read().decode("utf-8");

-    HeadMatch=re.search("<head(.*?)>(.*?)</head>",PreviewBlogPage,re.DOTALL);

-    HeadAttributes=HeadMatch.group(1);

-    FullDocumentHead=HeadMatch.group(2);

-    BodyMatch=re.search("<body(.*?)>(.*?)</body>",PreviewBlogPage,re.DOTALL);

-    BodyAttributes=BodyMatch.group(1);

-    FullPreviewBody=BodyMatch.group(2);

-    ArticleHTMLCodeMacro="$(ARTICLE_HTML_CODE)";

-    iArticleHTMLCodeMacro=FullPreviewBody.find(ArticleHTMLCodeMacro);

-    DocumentBodyPrefix=FullPreviewBody[0:iArticleHTMLCodeMacro];

-    DocumentBodySuffix=FullPreviewBody[iArticleHTMLCodeMacro+len(ArticleHTMLCodeMacro):];

-    FullPrepareHTMLCode=open("PrepareHTML.js","rb").read().decode("utf-8");

-    ReplacementList=[

-        ("$(FULL_DOCUMENT_HEAD)",FullDocumentHead),

-        ("$(DOCUMENT_BODY_PREFIX)",DocumentBodyPrefix),

-        ("$(DOCUMENT_BODY_SUFFIX)",DocumentBodySuffix)

-    ];

-    for Macro,Replacement in ReplacementList:

-        FullPrepareHTMLCode=FullPrepareHTMLCode.replace(Macro,Replacement.replace("\r\n","\\r\\n\\\r\n").replace("'","\\'"));

-    # Generate code which sets body and head attributes appropriately

-    for Element,AttributeCode in [("head",HeadAttributes),("body",BodyAttributes)]:

-        FullPrepareHTMLCode+="\r\n// Setting "+Element+" attributes\r\n";

-        for Match in re.finditer("(\\w+)=\\\"(.*?)\\\"",AttributeCode):

-            FullPrepareHTMLCode+="document."+Element+".setAttribute(\""+Match.group(1)+"\",\""+Match.group(2)+"\");\r\n";

-    open("PrepareHTML.full.js","wb").write(FullPrepareHTMLCode.encode("utf-8"));

+if (__name__ == "__main__"):

+  # Assemble the script which embeds the Markdeep page into the preview blog

+  PreviewBlogPage = open("PreviewBlogPage.htm", "rb").read().decode("utf-8")

+  HeadMatch = re.search("<head(.*?)>(.*?)</head>", PreviewBlogPage, re.DOTALL)

+  HeadAttributes = HeadMatch.group(1)

+  FullDocumentHead = HeadMatch.group(2)

+  BodyMatch = re.search("<body(.*?)>(.*?)</body>", PreviewBlogPage, re.DOTALL)

+  BodyAttributes = BodyMatch.group(1)

+  FullPreviewBody = BodyMatch.group(2)

+  ArticleHTMLCodeMacro = "$(ARTICLE_HTML_CODE)"

+  iArticleHTMLCodeMacro = FullPreviewBody.find(ArticleHTMLCodeMacro)

+  DocumentBodyPrefix = FullPreviewBody[0:iArticleHTMLCodeMacro]

+  DocumentBodySuffix = FullPreviewBody[iArticleHTMLCodeMacro + len(ArticleHTMLCodeMacro):]

+  FullPrepareHTMLCode = open("PrepareHTML.js", "rb").read().decode("utf-8")

+  ReplacementList = [("$(FULL_DOCUMENT_HEAD)", FullDocumentHead),

+                     ("$(DOCUMENT_BODY_PREFIX)", DocumentBodyPrefix),

+                     ("$(DOCUMENT_BODY_SUFFIX)", DocumentBodySuffix)]

+  for Macro, Replacement in ReplacementList:

+    FullPrepareHTMLCode = FullPrepareHTMLCode.replace(

+        Macro,

+        Replacement.replace("\r\n", "\\r\\n\\\r\n").replace("'", "\\'"))

+  # Generate code which sets body and head attributes appropriately

+  for Element, AttributeCode in [("head", HeadAttributes), ("body", BodyAttributes)]:

+    FullPrepareHTMLCode += "\r\n// Setting " + Element + " attributes\r\n"

+    for Match in re.finditer("(\\w+)=\\\"(.*?)\\\"", AttributeCode):

+      FullPrepareHTMLCode += "document." + Element + ".setAttribute(\"" + Match.group(

+          1) + "\",\"" + Match.group(2) + "\");\r\n"

+  open("PrepareHTML.full.js", "wb").write(FullPrepareHTMLCode.encode("utf-8"))

 

-    # Concatenate all the scripts together

-    SourceFileList=[

-                    "PrepareHTML.full.js",

-                    "SetMarkdeepMode.js",

-                    "markdeep.min.js",

-                    "DisplayMarkdeepOutput.js",

-                    "InvokeMathJax.js"

-    ];

-    OutputCode="\r\n\r\n".join(["// "+SourceFile+"\r\n\r\n"+open(SourceFile,"rb").read().decode("utf-8") for SourceFile in SourceFileList]);

-    OutputFile=open("MarkdeepUtility.js","wb");

-    OutputFile.write(OutputCode.encode("utf-8"));

-    OutputFile.close();

-    print("Done.");

+  # Concatenate all the scripts together

+  SourceFileList = [

+      "PrepareHTML.full.js", "SetMarkdeepMode.js", "markdeep.min.js", "DisplayMarkdeepOutput.js",

+      "InvokeMathJax.js"

+  ]

+  OutputCode = "\r\n\r\n".join([

+      "// " + SourceFile + "\r\n\r\n" + open(SourceFile, "rb").read().decode("utf-8")

+      for SourceFile in SourceFileList

+  ])

+  OutputFile = open("MarkdeepUtility.js", "wb")

+  OutputFile.write(OutputCode.encode("utf-8"))

+  OutputFile.close()

+  print("Done.")

diff --git a/examples/BlockSolver/BlockSolverExample.cpp b/examples/BlockSolver/BlockSolverExample.cpp
index ba699ee64..61685079a 100644
--- a/examples/BlockSolver/BlockSolverExample.cpp
+++ b/examples/BlockSolver/BlockSolverExample.cpp
@@ -140,7 +140,7 @@ void BlockSolverExample::createMultiBodyStack()
 		tr.setOrigin(btVector3(0, 0, 0.1 + i * 0.2));
 		btMultiBody* body = createMultiBody(mass, tr, boxShape);
 	}
-	if (0)
+	if (/* DISABLES CODE */ (0))
 	{
 		btMultiBody* mb = loadRobot("cube_small.urdf");
 		btTransform tr;
diff --git a/examples/ExampleBrowser/CMakeLists.txt b/examples/ExampleBrowser/CMakeLists.txt
index 89531eb77..d17ed4210 100644
--- a/examples/ExampleBrowser/CMakeLists.txt
+++ b/examples/ExampleBrowser/CMakeLists.txt
@@ -94,6 +94,9 @@ LINK_LIBRARIES(
         BulletExampleBrowserLib Bullet3Common BulletSoftBody BulletDynamics BulletCollision BulletInverseDynamicsUtils BulletInverseDynamics LinearMath OpenGLWindow gwen BussIK
 )
 
+
+add_definitions(-DINCLUDE_CLOTH_DEMOS)
+
 IF (WIN32)
 	INCLUDE_DIRECTORIES(
 		${BULLET_PHYSICS_SOURCE_DIR}/examples/ThirdPartyLibs/glad
@@ -346,6 +349,8 @@ SET(BulletExampleBrowser_SRCS
   ../MultiBody/InvertedPendulumPDControl.cpp
   ../MultiBody/InvertedPendulumPDControl.h
  ../MultiBody/MultiBodyConstraintFeedback.cpp
+  ../SoftDemo/SoftDemo.cpp
+  ../SoftDemo/SoftDemo.h
   ../MultiBody/MultiDofDemo.cpp
   ../MultiBody/MultiDofDemo.h
   ../RigidBody/RigidBodySoftContact.cpp
diff --git a/examples/RoboticsLearning/GripperGraspExample.cpp b/examples/RoboticsLearning/GripperGraspExample.cpp
index a7c80c1f4..8f5ec1a8e 100644
--- a/examples/RoboticsLearning/GripperGraspExample.cpp
+++ b/examples/RoboticsLearning/GripperGraspExample.cpp
@@ -326,7 +326,10 @@ public:
 				m_robotSim.loadURDF("plane.urdf", args);
 			}
 			m_robotSim.setGravity(btVector3(0, 0, -10));
-			m_robotSim.loadSoftBody("bunny.obj", 0.1, 0.1, 0.02);
+			b3RobotSimulatorLoadSoftBodyArgs args(0.1, 1, 0.02);
+			args.m_startPosition.setValue(0, 0, 5);
+			args.m_startOrientation.setValue(1, 0, 0, 1);
+			m_robotSim.loadSoftBody("bunny.obj", args);
 
 			b3JointInfo revoluteJoint1;
 			revoluteJoint1.m_parentFrame[0] = -0.055;
@@ -402,7 +405,8 @@ public:
 				m_robotSim.loadURDF("plane.urdf", args);
 			}
 			m_robotSim.setGravity(btVector3(0, 0, -10));
-			m_robotSim.loadSoftBody("bunny.obj", 0.3, 10.0, 0.1);
+			b3RobotSimulatorLoadSoftBodyArgs args(0.3, 10, 0.1);
+			m_robotSim.loadSoftBody("bunny.obj", args);
 		}
 	}
 	virtual void exitPhysics()
diff --git a/examples/SharedMemory/PhysicsClientC_API.cpp b/examples/SharedMemory/PhysicsClientC_API.cpp
index 920a463b7..c316cbeb2 100644
--- a/examples/SharedMemory/PhysicsClientC_API.cpp
+++ b/examples/SharedMemory/PhysicsClientC_API.cpp
@@ -314,6 +314,30 @@ B3_SHARED_API int b3LoadSoftBodySetCollisionMargin(b3SharedMemoryCommandHandle c
 	return 0;
 }
 
+
+B3_SHARED_API int b3LoadSoftBodySetStartPosition(b3SharedMemoryCommandHandle commandHandle, double startPosX, double startPosY, double startPosZ)
+{
+	struct SharedMemoryCommand* command = (struct SharedMemoryCommand*)commandHandle;
+	b3Assert(command->m_type == CMD_LOAD_SOFT_BODY);
+	command->m_loadSoftBodyArguments.m_initialPosition[0] = startPosX;
+	command->m_loadSoftBodyArguments.m_initialPosition[1] = startPosY;
+	command->m_loadSoftBodyArguments.m_initialPosition[2] = startPosZ;
+	command->m_updateFlags |= LOAD_SOFT_BODY_INITIAL_POSITION;
+	return 0;
+}
+
+B3_SHARED_API int b3LoadSoftBodySetStartOrientation(b3SharedMemoryCommandHandle commandHandle, double startOrnX, double startOrnY, double startOrnZ, double startOrnW)
+{
+	struct SharedMemoryCommand* command = (struct SharedMemoryCommand*)commandHandle;
+	b3Assert(command->m_type == CMD_LOAD_SOFT_BODY);
+        command->m_loadSoftBodyArguments.m_initialOrientation[0] = startOrnX;
+	command->m_loadSoftBodyArguments.m_initialOrientation[1] = startOrnY;
+	command->m_loadSoftBodyArguments.m_initialOrientation[2] = startOrnZ;
+	command->m_loadSoftBodyArguments.m_initialOrientation[3] = startOrnW;
+	command->m_updateFlags |= LOAD_SOFT_BODY_INITIAL_ORIENTATION;
+	return 0;
+}
+
 B3_SHARED_API b3SharedMemoryCommandHandle b3LoadUrdfCommandInit(b3PhysicsClientHandle physClient, const char* urdfFileName)
 {
 	PhysicsClient* cl = (PhysicsClient*)physClient;
diff --git a/examples/SharedMemory/PhysicsClientC_API.h b/examples/SharedMemory/PhysicsClientC_API.h
index 290c8b36b..9ba2dc37c 100644
--- a/examples/SharedMemory/PhysicsClientC_API.h
+++ b/examples/SharedMemory/PhysicsClientC_API.h
@@ -614,6 +614,8 @@ extern "C"
 	B3_SHARED_API int b3LoadSoftBodySetScale(b3SharedMemoryCommandHandle commandHandle, double scale);
 	B3_SHARED_API int b3LoadSoftBodySetMass(b3SharedMemoryCommandHandle commandHandle, double mass);
 	B3_SHARED_API int b3LoadSoftBodySetCollisionMargin(b3SharedMemoryCommandHandle commandHandle, double collisionMargin);
+	B3_SHARED_API int b3LoadSoftBodySetStartPosition(b3SharedMemoryCommandHandle commandHandle, double startPosX, double startPosY, double startPosZ);
+	B3_SHARED_API int b3LoadSoftBodySetStartOrientation(b3SharedMemoryCommandHandle commandHandle, double startOrnX, double startOrnY, double startOrnZ, double startOrnW);
 
 	B3_SHARED_API b3SharedMemoryCommandHandle b3RequestVREventsCommandInit(b3PhysicsClientHandle physClient);
 	B3_SHARED_API void b3VREventsSetDeviceTypeFilter(b3SharedMemoryCommandHandle commandHandle, int deviceTypeFilter);
diff --git a/examples/SharedMemory/PhysicsClientSharedMemory.cpp b/examples/SharedMemory/PhysicsClientSharedMemory.cpp
index f6cd23d1c..f0b742623 100644
--- a/examples/SharedMemory/PhysicsClientSharedMemory.cpp
+++ b/examples/SharedMemory/PhysicsClientSharedMemory.cpp
@@ -1398,12 +1398,51 @@ const SharedMemoryStatus* PhysicsClientSharedMemory::processServerStatus()
 			}
 			case CMD_LOAD_SOFT_BODY_FAILED:
 			{
-				b3Warning("loadSoftBody failed");
-				break;
+                            B3_PROFILE("CMD_LOAD_SOFT_BODY_FAILED");
+
+                            if (m_data->m_verboseOutput)
+                            {
+                                b3Printf("Server failed loading the SoftBody...\n");
+                            }
+                            break;
 			}
 			case CMD_LOAD_SOFT_BODY_COMPLETED:
 			{
-				break;
+                          B3_PROFILE("CMD_LOAD_SOFT_BODY_COMPLETED");
+
+                          if (m_data->m_verboseOutput)
+                          {
+                            b3Printf("Server loading the SoftBody OK\n");
+                          }
+                          
+                          b3Assert(serverCmd.m_numDataStreamBytes);
+                          if (serverCmd.m_numDataStreamBytes > 0)
+                          {
+			       bParse::btBulletFile bf(
+                                   this->m_data->m_testBlock1->m_bulletStreamDataServerToClientRefactor,
+                                   serverCmd.m_numDataStreamBytes);
+                               bf.setFileDNAisMemoryDNA();
+                               bf.parse(false);
+                               int bodyUniqueId = serverCmd.m_dataStreamArguments.m_bodyUniqueId;
+                               
+                               BodyJointInfoCache* bodyJoints = new BodyJointInfoCache;
+                               m_data->m_bodyJointMap.insert(bodyUniqueId, bodyJoints);
+                               bodyJoints->m_bodyName = serverCmd.m_dataStreamArguments.m_bodyName;
+                               bodyJoints->m_baseName = "baseLink";
+                                        
+                               if (bf.ok())
+                               {
+                                 if (m_data->m_verboseOutput)
+                                 {
+                                   b3Printf("Received robot description ok!\n");
+                                 }
+                               }
+                               else
+                               {
+                                 b3Warning("Robot description not received when loading soft body!");
+                               }
+                          }
+                          break;
 			}
 			case CMD_SYNC_USER_DATA_FAILED:
 			{
diff --git a/examples/SharedMemory/PhysicsDirect.cpp b/examples/SharedMemory/PhysicsDirect.cpp
index dd5bbfb7f..5bc538151 100644
--- a/examples/SharedMemory/PhysicsDirect.cpp
+++ b/examples/SharedMemory/PhysicsDirect.cpp
@@ -1079,7 +1079,12 @@ void PhysicsDirect::postProcessStatus(const struct SharedMemoryStatus& serverCmd
 		}
 		case CMD_LOAD_SOFT_BODY_COMPLETED:
 		{
-			break;
+                       int bodyUniqueId = serverCmd.m_loadSoftBodyResultArguments.m_objectUniqueId;
+                       BodyJointInfoCache2* bodyJoints = new BodyJointInfoCache2;
+                       m_data->m_bodyJointMap.insert(bodyUniqueId, bodyJoints);
+                       bodyJoints->m_bodyName = serverCmd.m_dataStreamArguments.m_bodyName;
+                       bodyJoints->m_baseName = "baseLink";
+                       break;
 		}
 		case CMD_SYNC_USER_DATA_FAILED:
 		{
@@ -1541,7 +1546,7 @@ int PhysicsDirect::getNumUserData(int bodyUniqueId) const
 void PhysicsDirect::getUserDataInfo(int bodyUniqueId, int userDataIndex, const char** keyOut, int* userDataIdOut, int* linkIndexOut, int* visualShapeIndexOut) const
 {
 	BodyJointInfoCache2** bodyJointsPtr = m_data->m_bodyJointMap[bodyUniqueId];
-	if (!bodyJointsPtr || !(*bodyJointsPtr) || userDataIndex <= 0 || userDataIndex > (*bodyJointsPtr)->m_userDataIds.size())
+	if (!bodyJointsPtr || !(*bodyJointsPtr) || userDataIndex < 0 || userDataIndex > (*bodyJointsPtr)->m_userDataIds.size())
 	{
 		*keyOut = 0;
 		*userDataIdOut = -1;
diff --git a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp
index b0c6697ec..87420a2ea 100644
--- a/examples/SharedMemory/PhysicsServerCommandProcessor.cpp
+++ b/examples/SharedMemory/PhysicsServerCommandProcessor.cpp
@@ -122,24 +122,6 @@ btScalar gRhsClamp = 1.f;
 
 #include "../CommonInterfaces/CommonFileIOInterface.h"
 
-
-
-
-struct UrdfLinkNameMapUtil
-{
-	btMultiBody* m_mb;
-	btAlignedObjectArray<btGeneric6DofSpring2Constraint*> m_rigidBodyJoints;
-
-	btDefaultSerializer* m_memSerializer;
-
-	UrdfLinkNameMapUtil() : m_mb(0), m_memSerializer(0)
-	{
-	}
-	virtual ~UrdfLinkNameMapUtil()
-	{
-	}
-};
-
 class b3ThreadPool
 {
 public:
@@ -2861,7 +2843,7 @@ void PhysicsServerCommandProcessor::deleteDynamicsWorld()
 
 bool PhysicsServerCommandProcessor::supportsJointMotor(btMultiBody* mb, int mbLinkIndex)
 {
-	bool canHaveMotor = (mb->getLink(mbLinkIndex).m_jointType == btMultibodyLink::eRevolute 
+	bool canHaveMotor = (mb->getLink(mbLinkIndex).m_jointType == btMultibodyLink::eRevolute
 		|| mb->getLink(mbLinkIndex).m_jointType == btMultibodyLink::ePrismatic);
 	return canHaveMotor;
 }
@@ -3074,7 +3056,7 @@ bool PhysicsServerCommandProcessor::processImportedObjects(const char* fileName,
 			std::string* baseName = new std::string(u2b.getLinkName(u2b.getRootLinkIndex()));
 			m_data->m_strings.push_back(baseName);
 			mb->setBaseName(baseName->c_str());
-			
+
 #if 0
 			btAlignedObjectArray<char> urdf;
 			mb->dumpUrdf(urdf);
@@ -3085,7 +3067,7 @@ bool PhysicsServerCommandProcessor::processImportedObjects(const char* fileName,
 				fclose(f);
 			}
 #endif
-			
+
 		}
 		else
 		{
@@ -3357,73 +3339,77 @@ int PhysicsServerCommandProcessor::createBodyInfoStream(int bodyUniqueId, char*
 	//serialize the btMultiBody and send the data to the client. This is one way to get the link/joint names across the (shared memory) wire
 
 	InternalBodyHandle* bodyHandle = m_data->m_bodyHandles.getHandle(bodyUniqueId);
-	btMultiBody* mb = bodyHandle ? bodyHandle->m_multiBody : 0;
-	if (mb)
+  if(!bodyHandle) return 0;
+	if (bodyHandle->m_multiBody)
 	{
-		UrdfLinkNameMapUtil utilBla;
-		UrdfLinkNameMapUtil* util = &utilBla;
+    btMultiBody* mb = bodyHandle->m_multiBody;
 		btDefaultSerializer ser(bufferSizeInBytes, (unsigned char*)bufferServerToClient);
 
-		util->m_mb = mb;
-		util->m_memSerializer = &ser;
-		util->m_memSerializer->startSerialization();
+		ser.startSerialization();
 
 		//disable serialization of the collision objects (they are too big, and the client likely doesn't need them);
-		util->m_memSerializer->m_skipPointers.insert(mb->getBaseCollider(), 0);
+		ser.m_skipPointers.insert(mb->getBaseCollider(), 0);
 		if (mb->getBaseName())
 		{
-			util->m_memSerializer->registerNameForPointer(mb->getBaseName(), mb->getBaseName());
+			ser.registerNameForPointer(mb->getBaseName(), mb->getBaseName());
 		}
 
 		bodyHandle->m_linkLocalInertialFrames.reserve(mb->getNumLinks());
 		for (int i = 0; i < mb->getNumLinks(); i++)
 		{
 			//disable serialization of the collision objects
-			util->m_memSerializer->m_skipPointers.insert(mb->getLink(i).m_collider, 0);
-			util->m_memSerializer->registerNameForPointer(mb->getLink(i).m_linkName, mb->getLink(i).m_linkName);
-			util->m_memSerializer->registerNameForPointer(mb->getLink(i).m_jointName, mb->getLink(i).m_jointName);
+			ser.m_skipPointers.insert(mb->getLink(i).m_collider, 0);
+			ser.registerNameForPointer(mb->getLink(i).m_linkName, mb->getLink(i).m_linkName);
+			ser.registerNameForPointer(mb->getLink(i).m_jointName, mb->getLink(i).m_jointName);
 		}
 
-		util->m_memSerializer->registerNameForPointer(mb->getBaseName(), mb->getBaseName());
+		ser.registerNameForPointer(mb->getBaseName(), mb->getBaseName());
 
 		int len = mb->calculateSerializeBufferSize();
-		btChunk* chunk = util->m_memSerializer->allocate(len, 1);
-		const char* structType = mb->serialize(chunk->m_oldPtr, util->m_memSerializer);
-		util->m_memSerializer->finalizeChunk(chunk, structType, BT_MULTIBODY_CODE, mb);
-		streamSizeInBytes = util->m_memSerializer->getCurrentBufferSize();
+		btChunk* chunk = ser.allocate(len, 1);
+		const char* structType = mb->serialize(chunk->m_oldPtr, &ser);
+		ser.finalizeChunk(chunk, structType, BT_MULTIBODY_CODE, mb);
+		streamSizeInBytes = ser.getCurrentBufferSize();
 	}
-	else
-	{
-		btRigidBody* rb = bodyHandle ? bodyHandle->m_rigidBody : 0;
-		if (rb)
-		{
-			UrdfLinkNameMapUtil utilBla;
-			UrdfLinkNameMapUtil* util = &utilBla;
+	else if(bodyHandle->m_rigidBody){
+	    btRigidBody* rb = bodyHandle->m_rigidBody;
 			btDefaultSerializer ser(bufferSizeInBytes, (unsigned char*)bufferServerToClient);
 
-			util->m_memSerializer = &ser;
-			util->m_memSerializer->startSerialization();
-			util->m_memSerializer->registerNameForPointer(bodyHandle->m_rigidBody, bodyHandle->m_bodyName.c_str());
+			ser.startSerialization();
+			ser.registerNameForPointer(bodyHandle->m_rigidBody, bodyHandle->m_bodyName.c_str());
 			//disable serialization of the collision objects (they are too big, and the client likely doesn't need them);
 			for (int i = 0; i < bodyHandle->m_rigidBodyJoints.size(); i++)
 			{
 				const btGeneric6DofSpring2Constraint* con = bodyHandle->m_rigidBodyJoints.at(i);
 
-				util->m_memSerializer->registerNameForPointer(con, bodyHandle->m_rigidBodyJointNames[i].c_str());
-				util->m_memSerializer->registerNameForPointer(&con->getRigidBodyB(), bodyHandle->m_rigidBodyLinkNames[i].c_str());
+				ser.registerNameForPointer(con, bodyHandle->m_rigidBodyJointNames[i].c_str());
+				ser.registerNameForPointer(&con->getRigidBodyB(), bodyHandle->m_rigidBodyLinkNames[i].c_str());
 
 				const btRigidBody& bodyA = con->getRigidBodyA();
 
 				int len = con->calculateSerializeBufferSize();
-				btChunk* chunk = util->m_memSerializer->allocate(len, 1);
-				const char* structType = con->serialize(chunk->m_oldPtr, util->m_memSerializer);
-				util->m_memSerializer->finalizeChunk(chunk, structType, BT_CONSTRAINT_CODE, (void*)con);
+				btChunk* chunk = ser.allocate(len, 1);
+				const char* structType = con->serialize(chunk->m_oldPtr, &ser);
+				ser.finalizeChunk(chunk, structType, BT_CONSTRAINT_CODE, (void*)con);
 			}
-
-			streamSizeInBytes = util->m_memSerializer->getCurrentBufferSize();
+			streamSizeInBytes = ser.getCurrentBufferSize();
 		}
-	}
+#ifndef SKIP_SOFT_BODY_MULTI_BODY_DYNAMICS_WORLD
+    else if(bodyHandle->m_softBody){
+    //minimum serialization, registerNameForPointer
+    btSoftBody* sb = bodyHandle->m_softBody;
+		btDefaultSerializer ser(bufferSizeInBytes, (unsigned char*)bufferServerToClient);
+
+		ser.startSerialization();
+		ser.registerNameForPointer(sb, bodyHandle->m_bodyName.c_str());
 
+		int len = sb->calculateSerializeBufferSize();
+		btChunk* chunk = ser.allocate(len, 1);
+		const char* structType = sb->serialize(chunk->m_oldPtr, &ser);
+		ser.finalizeChunk(chunk, structType, BT_SOFTBODY_CODE, sb);
+		streamSizeInBytes = ser.getCurrentBufferSize();
+	}
+#endif
 	return streamSizeInBytes;
 }
 
@@ -5530,7 +5516,11 @@ bool PhysicsServerCommandProcessor::processSyncBodyInfoCommand(const struct Shar
 	{
 		int usedHandle = usedHandles[i];
 		InternalBodyData* body = m_data->m_bodyHandles.getHandle(usedHandle);
+#ifndef SKIP_SOFT_BODY_MULTI_BODY_DYNAMICS_WORLD
+		if (body && (body->m_multiBody || body->m_rigidBody || body->m_softBody))
+#else
 		if (body && (body->m_multiBody || body->m_rigidBody))
+#endif
 		{
 			serverStatusOut.m_sdfLoadedArgs.m_bodyUniqueIds[actualNumBodies++] = usedHandle;
 		}
@@ -6330,7 +6320,7 @@ bool PhysicsServerCommandProcessor::processRequestActualStateCommand(const struc
 
 	//we store the state details in the shared memory block, to reduce status size
 	SendActualStateSharedMemoryStorage* stateDetails = (SendActualStateSharedMemoryStorage*)bufferServerToClient;
-	
+
 	//make sure the storage fits, otherwise
 	btAssert(sizeof(SendActualStateSharedMemoryStorage) < bufferSizeInBytes);
 	if (sizeof(SendActualStateSharedMemoryStorage) > bufferSizeInBytes)
@@ -6342,11 +6332,12 @@ bool PhysicsServerCommandProcessor::processRequestActualStateCommand(const struc
 	{
 		btMultiBody* mb = body->m_multiBody;
 		SharedMemoryStatus& serverCmd = serverStatusOut;
-		
+
 		serverStatusOut.m_type = CMD_ACTUAL_STATE_UPDATE_COMPLETED;
 
 		serverCmd.m_sendActualStateArgs.m_bodyUniqueId = bodyUniqueId;
 		serverCmd.m_sendActualStateArgs.m_numLinks = body->m_multiBody->getNumLinks();
+		serverCmd.m_numDataStreamBytes = sizeof(SendActualStateSharedMemoryStorage);
 		serverCmd.m_sendActualStateArgs.m_stateDetails = 0;
 		int totalDegreeOfFreedomQ = 0;
 		int totalDegreeOfFreedomU = 0;
@@ -6382,24 +6373,24 @@ bool PhysicsServerCommandProcessor::processRequestActualStateCommand(const struc
 			serverCmd.m_sendActualStateArgs.m_rootLocalInertialFrame[6] =
 				body->m_rootLocalInertialFrame.getRotation()[3];
 
-			//base position in world space, carthesian
+			//base position in world space, cartesian
 			stateDetails->m_actualStateQ[0] = tr.getOrigin()[0];
 			stateDetails->m_actualStateQ[1] = tr.getOrigin()[1];
 			stateDetails->m_actualStateQ[2] = tr.getOrigin()[2];
 
-			//base orientation, quaternion x,y,z,w, in world space, carthesian
+			//base orientation, quaternion x,y,z,w, in world space, cartesian
 			stateDetails->m_actualStateQ[3] = tr.getRotation()[0];
 			stateDetails->m_actualStateQ[4] = tr.getRotation()[1];
 			stateDetails->m_actualStateQ[5] = tr.getRotation()[2];
 			stateDetails->m_actualStateQ[6] = tr.getRotation()[3];
 			totalDegreeOfFreedomQ += 7;  //pos + quaternion
 
-			//base linear velocity (in world space, carthesian)
+			//base linear velocity (in world space, cartesian)
 			stateDetails->m_actualStateQdot[0] = mb->getBaseVel()[0];
 			stateDetails->m_actualStateQdot[1] = mb->getBaseVel()[1];
 			stateDetails->m_actualStateQdot[2] = mb->getBaseVel()[2];
 
-			//base angular velocity (in world space, carthesian)
+			//base angular velocity (in world space, cartesian)
 			stateDetails->m_actualStateQdot[3] = mb->getBaseOmega()[0];
 			stateDetails->m_actualStateQdot[4] = mb->getBaseOmega()[1];
 			stateDetails->m_actualStateQdot[5] = mb->getBaseOmega()[2];
@@ -6551,59 +6542,104 @@ bool PhysicsServerCommandProcessor::processRequestActualStateCommand(const struc
 
 		hasStatus = true;
 	}
-	else
-	{
-		if (body && body->m_rigidBody)
-		{
-			btRigidBody* rb = body->m_rigidBody;
-			SharedMemoryStatus& serverCmd = serverStatusOut;
-			serverCmd.m_type = CMD_ACTUAL_STATE_UPDATE_COMPLETED;
-			
-			serverCmd.m_sendActualStateArgs.m_bodyUniqueId = bodyUniqueId;
-			serverCmd.m_sendActualStateArgs.m_numLinks = 0;
-			serverCmd.m_sendActualStateArgs.m_stateDetails = 0;
-
-			int totalDegreeOfFreedomQ = 0;
-			int totalDegreeOfFreedomU = 0;
-
-			btTransform tr = rb->getWorldTransform();
-			//base position in world space, carthesian
-			stateDetails->m_actualStateQ[0] = tr.getOrigin()[0];
-			stateDetails->m_actualStateQ[1] = tr.getOrigin()[1];
-			stateDetails->m_actualStateQ[2] = tr.getOrigin()[2];
-
-			//base orientation, quaternion x,y,z,w, in world space, carthesian
-			stateDetails->m_actualStateQ[3] = tr.getRotation()[0];
-			stateDetails->m_actualStateQ[4] = tr.getRotation()[1];
-			stateDetails->m_actualStateQ[5] = tr.getRotation()[2];
-			stateDetails->m_actualStateQ[6] = tr.getRotation()[3];
-			totalDegreeOfFreedomQ += 7;  //pos + quaternion
-
-			//base linear velocity (in world space, carthesian)
-			stateDetails->m_actualStateQdot[0] = rb->getLinearVelocity()[0];
-			stateDetails->m_actualStateQdot[1] = rb->getLinearVelocity()[1];
-			stateDetails->m_actualStateQdot[2] = rb->getLinearVelocity()[2];
-
-			//base angular velocity (in world space, carthesian)
-			stateDetails->m_actualStateQdot[3] = rb->getAngularVelocity()[0];
-			stateDetails->m_actualStateQdot[4] = rb->getAngularVelocity()[1];
-			stateDetails->m_actualStateQdot[5] = rb->getAngularVelocity()[2];
-			totalDegreeOfFreedomU += 6;  //3 linear and 3 angular DOF
-
-			serverCmd.m_sendActualStateArgs.m_numDegreeOfFreedomQ = totalDegreeOfFreedomQ;
-			serverCmd.m_sendActualStateArgs.m_numDegreeOfFreedomU = totalDegreeOfFreedomU;
-
-			hasStatus = true;
-		}
-		else
-		{
-			//b3Warning("Request state but no multibody or rigid body available");
-			SharedMemoryStatus& serverCmd = serverStatusOut;
-			serverCmd.m_type = CMD_ACTUAL_STATE_UPDATE_FAILED;
-			hasStatus = true;
-		}
-	}
-	return hasStatus;
+	else if (body && body->m_rigidBody){
+          btRigidBody* rb = body->m_rigidBody;
+          SharedMemoryStatus& serverCmd = serverStatusOut;
+          serverCmd.m_type = CMD_ACTUAL_STATE_UPDATE_COMPLETED;
+
+          serverCmd.m_sendActualStateArgs.m_bodyUniqueId = bodyUniqueId;
+          serverCmd.m_sendActualStateArgs.m_numLinks = 0;
+					serverCmd.m_numDataStreamBytes = sizeof(SendActualStateSharedMemoryStorage);
+          serverCmd.m_sendActualStateArgs.m_stateDetails = 0;
+
+          int totalDegreeOfFreedomQ = 0;
+          int totalDegreeOfFreedomU = 0;
+
+          btTransform tr = rb->getWorldTransform();
+          //base position in world space, cartesian
+          stateDetails->m_actualStateQ[0] = tr.getOrigin()[0];
+          stateDetails->m_actualStateQ[1] = tr.getOrigin()[1];
+          stateDetails->m_actualStateQ[2] = tr.getOrigin()[2];
+
+          //base orientation, quaternion x,y,z,w, in world space, cartesian
+          stateDetails->m_actualStateQ[3] = tr.getRotation()[0];
+          stateDetails->m_actualStateQ[4] = tr.getRotation()[1];
+          stateDetails->m_actualStateQ[5] = tr.getRotation()[2];
+          stateDetails->m_actualStateQ[6] = tr.getRotation()[3];
+          totalDegreeOfFreedomQ += 7;  //pos + quaternion
+
+          //base linear velocity (in world space, cartesian)
+          stateDetails->m_actualStateQdot[0] = rb->getLinearVelocity()[0];
+          stateDetails->m_actualStateQdot[1] = rb->getLinearVelocity()[1];
+          stateDetails->m_actualStateQdot[2] = rb->getLinearVelocity()[2];
+
+          //base angular velocity (in world space, cartesian)
+          stateDetails->m_actualStateQdot[3] = rb->getAngularVelocity()[0];
+          stateDetails->m_actualStateQdot[4] = rb->getAngularVelocity()[1];
+          stateDetails->m_actualStateQdot[5] = rb->getAngularVelocity()[2];
+          totalDegreeOfFreedomU += 6;  //3 linear and 3 angular DOF
+
+          serverCmd.m_sendActualStateArgs.m_numDegreeOfFreedomQ = totalDegreeOfFreedomQ;
+          serverCmd.m_sendActualStateArgs.m_numDegreeOfFreedomU = totalDegreeOfFreedomU;
+
+          hasStatus = true;
+        }
+#ifndef SKIP_SOFT_BODY_MULTI_BODY_DYNAMICS_WORLD
+        else if (body && body->m_softBody)
+        {
+          btSoftBody* sb = body->m_softBody;
+          SharedMemoryStatus& serverCmd = serverStatusOut;
+          serverCmd.m_type = CMD_ACTUAL_STATE_UPDATE_COMPLETED;
+          serverCmd.m_sendActualStateArgs.m_bodyUniqueId = bodyUniqueId;
+          serverCmd.m_sendActualStateArgs.m_numLinks = 0;
+					serverCmd.m_numDataStreamBytes = sizeof(SendActualStateSharedMemoryStorage);
+          serverCmd.m_sendActualStateArgs.m_stateDetails = 0;
+
+          serverCmd.m_sendActualStateArgs.m_rootLocalInertialFrame[0] =
+              body->m_rootLocalInertialFrame.getOrigin()[0];
+          serverCmd.m_sendActualStateArgs.m_rootLocalInertialFrame[1] =
+              body->m_rootLocalInertialFrame.getOrigin()[1];
+          serverCmd.m_sendActualStateArgs.m_rootLocalInertialFrame[2] =
+              body->m_rootLocalInertialFrame.getOrigin()[2];
+
+          serverCmd.m_sendActualStateArgs.m_rootLocalInertialFrame[3] =
+              body->m_rootLocalInertialFrame.getRotation()[0];
+          serverCmd.m_sendActualStateArgs.m_rootLocalInertialFrame[4] =
+              body->m_rootLocalInertialFrame.getRotation()[1];
+          serverCmd.m_sendActualStateArgs.m_rootLocalInertialFrame[5] =
+              body->m_rootLocalInertialFrame.getRotation()[2];
+          serverCmd.m_sendActualStateArgs.m_rootLocalInertialFrame[6] =
+              body->m_rootLocalInertialFrame.getRotation()[3];
+
+          btTransform tr = sb->getWorldTransform();
+          //base position in world space, cartesian
+          stateDetails->m_actualStateQ[0] = tr.getOrigin()[0];
+          stateDetails->m_actualStateQ[1] = tr.getOrigin()[1];
+          stateDetails->m_actualStateQ[2] = tr.getOrigin()[2];
+
+          //base orientation, quaternion x,y,z,w, in world space, cartesian
+          stateDetails->m_actualStateQ[3] = tr.getRotation()[0];
+          stateDetails->m_actualStateQ[4] = tr.getRotation()[1];
+          stateDetails->m_actualStateQ[5] = tr.getRotation()[2];
+          stateDetails->m_actualStateQ[6] = tr.getRotation()[3];
+
+          int totalDegreeOfFreedomQ = 7;  //pos + quaternion
+          int totalDegreeOfFreedomU = 6;  //3 linear and 3 angular DOF
+
+          serverCmd.m_sendActualStateArgs.m_numDegreeOfFreedomQ = totalDegreeOfFreedomQ;
+          serverCmd.m_sendActualStateArgs.m_numDegreeOfFreedomU = totalDegreeOfFreedomU;
+
+          hasStatus = true;
+        }
+#endif
+        else
+        {
+          //b3Warning("Request state but no multibody or rigid body available");
+          SharedMemoryStatus& serverCmd = serverStatusOut;
+          serverCmd.m_type = CMD_ACTUAL_STATE_UPDATE_FAILED;
+          hasStatus = true;
+        }
+        return hasStatus;
 }
 
 bool PhysicsServerCommandProcessor::processRequestContactpointInformationCommand(const struct SharedMemoryCommand& clientCmd, struct SharedMemoryStatus& serverStatusOut, char* bufferServerToClient, int bufferSizeInBytes)
@@ -7171,7 +7207,7 @@ bool PhysicsServerCommandProcessor::processCreateMultiBodyCommandSingle(const st
 			m_data->m_sdfRecentLoadedBodies.clear();
 			if (bodyUniqueId >= 0)
 			{
-				
+
 				serverStatusOut.m_type = CMD_CREATE_MULTI_BODY_COMPLETED;
 				if (bufferSizeInBytes>0 && serverStatusOut.m_numDataStreamBytes==0)
 				{
@@ -7179,7 +7215,7 @@ bool PhysicsServerCommandProcessor::processCreateMultiBodyCommandSingle(const st
 						BT_PROFILE("autogenerateGraphicsObjects");
 						m_data->m_guiHelper->autogenerateGraphicsObjects(this->m_data->m_dynamicsWorld);
 					}
-					
+
 					BT_PROFILE("createBodyInfoStream");
 					int streamSizeInBytes = createBodyInfoStream(bodyUniqueId, bufferServerToClient, bufferSizeInBytes);
 					serverStatusOut.m_numDataStreamBytes = streamSizeInBytes;
@@ -7281,17 +7317,34 @@ bool PhysicsServerCommandProcessor::processLoadSoftBodyCommand(const struct Shar
 	btAssert((clientCmd.m_updateFlags & LOAD_SOFT_BODY_FILE_NAME) != 0);
 	btAssert(loadSoftBodyArgs.m_fileName);
 
+
+        btVector3 initialPos(0, 0, 0);
+        if (clientCmd.m_updateFlags & LOAD_SOFT_BODY_INITIAL_POSITION)
+	{
+          initialPos[0] = loadSoftBodyArgs.m_initialPosition[0];
+          initialPos[1] = loadSoftBodyArgs.m_initialPosition[1];
+          initialPos[2] = loadSoftBodyArgs.m_initialPosition[2];
+	}
+	btQuaternion initialOrn(0, 0, 0, 1);
+        if (clientCmd.m_updateFlags & LOAD_SOFT_BODY_INITIAL_ORIENTATION)
+	{
+          initialOrn[0] = loadSoftBodyArgs.m_initialOrientation[0];
+          initialOrn[1] = loadSoftBodyArgs.m_initialOrientation[1];
+          initialOrn[2] = loadSoftBodyArgs.m_initialOrientation[2];
+          initialOrn[3] = loadSoftBodyArgs.m_initialOrientation[3];
+	}
+
 	if (clientCmd.m_updateFlags & LOAD_SOFT_BODY_UPDATE_SCALE)
 	{
-		scale = clientCmd.m_loadSoftBodyArguments.m_scale;
+                scale = clientCmd.m_loadSoftBodyArguments.m_scale;
 	}
 	if (clientCmd.m_updateFlags & LOAD_SOFT_BODY_UPDATE_MASS)
 	{
-		mass = clientCmd.m_loadSoftBodyArguments.m_mass;
+                mass = clientCmd.m_loadSoftBodyArguments.m_mass;
 	}
-	if (clientCmd.m_updateFlags & LOAD_SOFT_BODY_UPDATE_COLLISION_MARGIN)
+   	if (clientCmd.m_updateFlags & LOAD_SOFT_BODY_UPDATE_COLLISION_MARGIN)
 	{
-		collisionMargin = clientCmd.m_loadSoftBodyArguments.m_collisionMargin;
+                collisionMargin = clientCmd.m_loadSoftBodyArguments.m_collisionMargin;
 	}
 
 	{
@@ -7310,7 +7363,7 @@ bool PhysicsServerCommandProcessor::processLoadSoftBodyCommand(const struct Shar
 		bool foundFile = UrdfFindMeshFile(fileIO,pathPrefix, relativeFileName, error_message_prefix, &out_found_filename, &out_type);
 		std::vector<tinyobj::shape_t> shapes;
 		std::string err = tinyobj::LoadObj(shapes, out_found_filename.c_str(),"",fileIO);
-		if (shapes.size() > 0)
+		if (!shapes.empty())
 		{
 			const tinyobj::shape_t& shape = shapes[0];
 			btAlignedObjectArray<btScalar> vertices;
@@ -7333,9 +7386,11 @@ bool PhysicsServerCommandProcessor::processLoadSoftBodyCommand(const struct Shar
 				psb->generateBendingConstraints(2, pm);
 				psb->m_cfg.piterations = 20;
 				psb->m_cfg.kDF = 0.5;
+				//turn on softbody vs softbody collision
+				psb->m_cfg.collisions |= btSoftBody::fCollision::VF_SS;
 				psb->randomizeConstraints();
-				psb->rotate(btQuaternion(0.70711, 0, 0, 0.70711));
-				psb->translate(btVector3(-0.05, 0, 1.0));
+				psb->rotate(initialOrn);
+				psb->translate(initialPos);
 				psb->scale(btVector3(scale, scale, scale));
 
 				psb->setTotalMass(mass, true);
@@ -7350,6 +7405,19 @@ bool PhysicsServerCommandProcessor::processLoadSoftBodyCommand(const struct Shar
 				serverStatusOut.m_loadSoftBodyResultArguments.m_objectUniqueId = bodyUniqueId;
 				serverStatusOut.m_type = CMD_LOAD_SOFT_BODY_COMPLETED;
 
+                                int streamSizeInBytes = createBodyInfoStream(bodyUniqueId, bufferServerToClient, bufferSizeInBytes);
+                                serverStatusOut.m_numDataStreamBytes = streamSizeInBytes;
+
+#ifdef ENABLE_LINK_MAPPER
+                                if (m_data->m_urdfLinkNameMapper.size())
+                                {
+                                  serverStatusOut.m_numDataStreamBytes = m_data->m_urdfLinkNameMapper.at(m_data->m_urdfLinkNameMapper.size() - 1)->m_memSerializer->getCurrentBufferSize();
+                                }
+#endif
+                                serverStatusOut.m_dataStreamArguments.m_bodyUniqueId = bodyUniqueId;
+                                InternalBodyData* body = m_data->m_bodyHandles.getHandle(bodyUniqueId);
+                                strcpy(serverStatusOut.m_dataStreamArguments.m_bodyName, body->m_bodyName.c_str());
+
 				b3Notification notification;
 				notification.m_notificationType = BODY_ADDED;
 				notification.m_bodyArgs.m_bodyUniqueId = bodyUniqueId;
@@ -7482,6 +7550,17 @@ bool PhysicsServerCommandProcessor::processProfileTimingCommand(const struct Sha
 	return hasStatus;
 }
 
+void setDefaultRootWorldAABB(SharedMemoryStatus& serverCmd)
+{
+    serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMin[0] = 0;
+		serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMin[1] = 0;
+		serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMin[2] = 0;
+
+		serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMax[0] = -1;
+		serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMax[1] = -1;
+		serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMax[2] = -1;
+}
+
 bool PhysicsServerCommandProcessor::processRequestCollisionInfoCommand(const struct SharedMemoryCommand& clientCmd, struct SharedMemoryStatus& serverStatusOut, char* bufferServerToClient, int bufferSizeInBytes)
 {
 	bool hasStatus = true;
@@ -7496,13 +7575,7 @@ bool PhysicsServerCommandProcessor::processRequestCollisionInfoCommand(const str
 		btMultiBody* mb = body->m_multiBody;
 		serverStatusOut.m_type = CMD_REQUEST_COLLISION_INFO_COMPLETED;
 		serverCmd.m_sendCollisionInfoArgs.m_numLinks = body->m_multiBody->getNumLinks();
-		serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMin[0] = 0;
-		serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMin[1] = 0;
-		serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMin[2] = 0;
-
-		serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMax[0] = -1;
-		serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMax[1] = -1;
-		serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMax[2] = -1;
+                setDefaultRootWorldAABB(serverCmd);
 
 		if (body->m_multiBody->getBaseCollider())
 		{
@@ -7544,21 +7617,14 @@ bool PhysicsServerCommandProcessor::processRequestCollisionInfoCommand(const str
 			}
 		}
 	}
-	else
+	else if (body && body->m_rigidBody)
 	{
-		if (body && body->m_rigidBody)
-		{
 			btRigidBody* rb = body->m_rigidBody;
 			SharedMemoryStatus& serverCmd = serverStatusOut;
 			serverStatusOut.m_type = CMD_REQUEST_COLLISION_INFO_COMPLETED;
 			serverCmd.m_sendCollisionInfoArgs.m_numLinks = 0;
-			serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMin[0] = 0;
-			serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMin[1] = 0;
-			serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMin[2] = 0;
+                        setDefaultRootWorldAABB(serverCmd);
 
-			serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMax[0] = -1;
-			serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMax[1] = -1;
-			serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMax[2] = -1;
 			if (rb->getCollisionShape())
 			{
 				btTransform tr = rb->getWorldTransform();
@@ -7573,8 +7639,15 @@ bool PhysicsServerCommandProcessor::processRequestCollisionInfoCommand(const str
 				serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMax[1] = aabbMax[1];
 				serverCmd.m_sendCollisionInfoArgs.m_rootWorldAABBMax[2] = aabbMax[2];
 			}
-		}
 	}
+#ifndef SKIP_SOFT_BODY_MULTI_BODY_DYNAMICS_WORLD
+        else if (body && body->m_softBody){
+          SharedMemoryStatus& serverCmd = serverStatusOut;
+          serverStatusOut.m_type = CMD_REQUEST_COLLISION_INFO_COMPLETED;
+          serverCmd.m_sendCollisionInfoArgs.m_numLinks = 0;
+          setDefaultRootWorldAABB(serverCmd);
+        }
+#endif
 	return hasStatus;
 }
 
@@ -7605,7 +7678,7 @@ bool PhysicsServerCommandProcessor::processForwardDynamicsCommand(const struct S
 	}
 
 	btScalar deltaTimeScaled = m_data->m_physicsDeltaTime * simTimeScalingFactor;
-	
+
 	int numSteps = 0;
 	if (m_data->m_numSimulationSubSteps > 0)
 	{
@@ -7632,7 +7705,7 @@ bool PhysicsServerCommandProcessor::processForwardDynamicsCommand(const struct S
 	m_data->m_dynamicsWorld->getAnalyticsData(islandAnalyticsData);
 	serverCmd.m_forwardDynamicsAnalyticsArgs.m_numIslands = islandAnalyticsData.size();
 	int numIslands = btMin(islandAnalyticsData.size(), MAX_ISLANDS_ANALYTICS);
-	
+
 	for (int i=0;i<numIslands;i++)
 	{
 		serverCmd.m_forwardDynamicsAnalyticsArgs.m_numSolverCalls = islandAnalyticsData[i].m_numSolverCalls;
@@ -7724,7 +7797,7 @@ bool PhysicsServerCommandProcessor::processChangeDynamicsInfoCommand(const struc
 			{
 				mb->setCanWakeup(false);
 			}
-			
+
 		}
 
 		if (clientCmd.m_updateFlags & CHANGE_DYNAMICS_INFO_SET_LINEAR_DAMPING)
@@ -7815,7 +7888,7 @@ bool PhysicsServerCommandProcessor::processChangeDynamicsInfoCommand(const struc
 						m_data->m_dynamicsWorld->addCollisionObject(mb->getBaseCollider(), collisionFilterGroup, collisionFilterMask);
 					}
 				}
-				
+
 			}
 			if (clientCmd.m_updateFlags & CHANGE_DYNAMICS_INFO_SET_LOCAL_INERTIA_DIAGONAL)
 			{
@@ -7830,7 +7903,7 @@ bool PhysicsServerCommandProcessor::processChangeDynamicsInfoCommand(const struc
 			{
 				mb->setMaxCoordinateVelocity(clientCmd.m_changeDynamicsInfoArgs.m_maxJointVelocity);
 			}
-			
+
 		}
 		else
 		{
@@ -7918,7 +7991,7 @@ bool PhysicsServerCommandProcessor::processChangeDynamicsInfoCommand(const struc
 					btRigidBody* childRb = &body->m_rigidBodyJoints[linkIndex]->getRigidBodyB();
 					rb = childRb;
 				}
-				
+
 			}
 		}
 
@@ -8009,13 +8082,13 @@ bool PhysicsServerCommandProcessor::processChangeDynamicsInfoCommand(const struc
 			{
 				rb->setAnisotropicFriction(anisotropicFriction);
 			}
-			
+
 			if (clientCmd.m_updateFlags & CHANGE_DYNAMICS_INFO_SET_CONTACT_PROCESSING_THRESHOLD)
 			{
 				rb->setContactProcessingThreshold(clientCmd.m_changeDynamicsInfoArgs.m_contactProcessingThreshold);
 			}
 
-			
+
 
 			if (clientCmd.m_updateFlags & CHANGE_DYNAMICS_INFO_SET_CCD_SWEPT_SPHERE_RADIUS)
 			{
@@ -8171,6 +8244,14 @@ bool PhysicsServerCommandProcessor::processGetDynamicsInfoCommand(const struct S
 		}
 		hasStatus = true;
 	}
+#ifndef SKIP_SOFT_BODY_MULTI_BODY_DYNAMICS_WORLD
+	else if (body && body->m_softBody){
+          //todo: @fuchuyuan implement dynamics info
+          b3Warning("Softbody dynamics info not set!!!");
+          SharedMemoryStatus& serverCmd = serverStatusOut;
+          serverCmd.m_type = CMD_GET_DYNAMICS_INFO_COMPLETED;
+        }
+#endif
 	return hasStatus;
 }
 
@@ -8430,7 +8511,7 @@ bool PhysicsServerCommandProcessor::processSendPhysicsParametersCommand(const st
 	{
 		m_data->m_dynamicsWorld->getSolverInfo().m_reportSolverAnalytics = clientCmd.m_physSimParamArgs.m_reportSolverAnalytics;
 	}
-	
+
 	SharedMemoryStatus& serverCmd = serverStatusOut;
 	serverCmd.m_type = CMD_CLIENT_COMMAND_COMPLETED;
 	return hasStatus;
@@ -8524,7 +8605,7 @@ bool PhysicsServerCommandProcessor::processInitPoseCommand(const struct SharedMe
 			{
 				int posVarCount = mb->getLink(i).m_posVarCount;
 				bool hasPosVar = posVarCount > 0;
-								
+
 				for (int j = 0; j < posVarCount; j++)
 				{
 					if (clientCmd.m_initPoseArgs.m_hasInitialStateQ[posVarCountIndex+j] == 0)
@@ -8553,7 +8634,7 @@ bool PhysicsServerCommandProcessor::processInitPoseCommand(const struct SharedMe
 						mb->setJointVelMultiDof(i, vel);
 					}
 				}
-				
+
 				bool hasVel = true;
 				for (int j = 0; j < mb->getLink(i).m_dofCount; j++)
 				{
@@ -8576,7 +8657,7 @@ bool PhysicsServerCommandProcessor::processInitPoseCommand(const struct SharedMe
 						mb->setJointVelMultiDof(i, &clientCmd.m_initPoseArgs.m_initialStateQdot[uDofIndex]);
 					}
 				}
-								
+
 				posVarCountIndex += mb->getLink(i).m_posVarCount;
 				uDofIndex += mb->getLink(i).m_dofCount;
 			}
@@ -8947,7 +9028,7 @@ bool PhysicsServerCommandProcessor::processInverseDynamicsCommand(const struct S
 		{
 #ifdef STATIC_LINK_SPD_PLUGIN
 			{
-				cRBDModel* rbdModel = m_data->findOrCreateRBDModel(bodyHandle->m_multiBody, 
+				cRBDModel* rbdModel = m_data->findOrCreateRBDModel(bodyHandle->m_multiBody,
 					clientCmd.m_calculateInverseDynamicsArguments.m_jointPositionsQ,
 					clientCmd.m_calculateInverseDynamicsArguments.m_jointVelocitiesQdot);
 				if (rbdModel)
@@ -8964,9 +9045,9 @@ bool PhysicsServerCommandProcessor::processInverseDynamicsCommand(const struct S
 					}
 					serverCmd.m_inverseDynamicsResultArgs.m_bodyUniqueId = clientCmd.m_calculateInverseDynamicsArguments.m_bodyUniqueId;
 					serverCmd.m_inverseDynamicsResultArgs.m_dofCount = dof;
-					
+
 					serverCmd.m_type = CMD_CALCULATED_INVERSE_DYNAMICS_COMPLETED;
-					
+
 				}
 			}
 #endif
@@ -9438,7 +9519,7 @@ bool PhysicsServerCommandProcessor::processRemoveBodyCommand(const struct Shared
 				int numCollisionObjects = m_data->m_dynamicsWorld->getNumCollisionObjects();
 				m_data->m_dynamicsWorld->removeMultiBody(bodyHandle->m_multiBody);
 				numCollisionObjects = m_data->m_dynamicsWorld->getNumCollisionObjects();
-				
+
 				delete bodyHandle->m_multiBody;
 				bodyHandle->m_multiBody = 0;
 				serverCmd.m_type = CMD_REMOVE_BODY_COMPLETED;
@@ -10496,6 +10577,18 @@ bool PhysicsServerCommandProcessor::processRequestVisualShapeInfoCommand(const s
 		int totalNumVisualShapes = m_data->m_pluginManager.getRenderInterface()->getNumVisualShapes(clientCmd.m_requestVisualShapeDataArguments.m_bodyUniqueId);
 		//int totalBytesPerVisualShape = sizeof (b3VisualShapeData);
 		//int visualShapeStorage = bufferSizeInBytes / totalBytesPerVisualShape - 1;
+
+                //set serverCmd.m_sendVisualShapeArgs when totalNumVisualShapes is zero
+                if (totalNumVisualShapes==0) {
+                  serverCmd.m_sendVisualShapeArgs.m_numRemainingVisualShapes = 0;
+                  serverCmd.m_sendVisualShapeArgs.m_numVisualShapesCopied = 0;
+                  serverCmd.m_sendVisualShapeArgs.m_startingVisualShapeIndex = clientCmd.m_requestVisualShapeDataArguments.m_startingVisualShapeIndex;
+                  serverCmd.m_sendVisualShapeArgs.m_bodyUniqueId = clientCmd.m_requestVisualShapeDataArguments.m_bodyUniqueId;
+                  serverCmd.m_numDataStreamBytes = sizeof(b3VisualShapeData) * serverCmd.m_sendVisualShapeArgs.m_numVisualShapesCopied;
+                  serverCmd.m_type = CMD_VISUAL_SHAPE_INFO_COMPLETED;
+                }
+
+    else{
 		b3VisualShapeData* visualShapeStoragePtr = (b3VisualShapeData*)bufferServerToClient;
 
 		int remain = totalNumVisualShapes - clientCmd.m_requestVisualShapeDataArguments.m_startingVisualShapeIndex;
@@ -10531,6 +10624,10 @@ bool PhysicsServerCommandProcessor::processRequestVisualShapeInfoCommand(const s
 			serverCmd.m_numDataStreamBytes = sizeof(b3VisualShapeData) * serverCmd.m_sendVisualShapeArgs.m_numVisualShapesCopied;
 			serverCmd.m_type = CMD_VISUAL_SHAPE_INFO_COMPLETED;
 		}
+                else{
+                  b3Warning("failed to get shape info");
+                }
+    }
 	}
 	return hasStatus;
 }
@@ -10849,7 +10946,7 @@ bool PhysicsServerCommandProcessor::processRemoveStateCommand(const struct Share
 	bool hasStatus = true;
 	SharedMemoryStatus& serverCmd = serverStatusOut;
 	serverCmd.m_type = CMD_REMOVE_STATE_FAILED;
-	
+
 	if (clientCmd.m_loadStateArguments.m_stateId >= 0)
 	{
 		if (clientCmd.m_loadStateArguments.m_stateId < m_data->m_savedStates.size())
@@ -11189,7 +11286,7 @@ bool PhysicsServerCommandProcessor::processCommand(const struct SharedMemoryComm
 			hasStatus = processCreateVisualShapeCommand(clientCmd, serverStatusOut, bufferServerToClient, bufferSizeInBytes);
 			break;
 		}
-		
+
 		case CMD_CREATE_MULTI_BODY:
 		{
 			hasStatus = processCreateMultiBodyCommand(clientCmd, serverStatusOut, bufferServerToClient, bufferSizeInBytes);
diff --git a/examples/SharedMemory/SharedMemoryCommands.h b/examples/SharedMemory/SharedMemoryCommands.h
index 1d70a9df0..083192c48 100644
--- a/examples/SharedMemory/SharedMemoryCommands.h
+++ b/examples/SharedMemory/SharedMemoryCommands.h
@@ -490,7 +490,9 @@ enum EnumLoadSoftBodyUpdateFlags
 	LOAD_SOFT_BODY_FILE_NAME = 1,
 	LOAD_SOFT_BODY_UPDATE_SCALE = 2,
 	LOAD_SOFT_BODY_UPDATE_MASS = 4,
-	LOAD_SOFT_BODY_UPDATE_COLLISION_MARGIN = 8
+	LOAD_SOFT_BODY_UPDATE_COLLISION_MARGIN = 8,
+	LOAD_SOFT_BODY_INITIAL_POSITION = 16,
+        LOAD_SOFT_BODY_INITIAL_ORIENTATION = 32
 };
 
 enum EnumSimParamInternalSimFlags
@@ -507,6 +509,8 @@ struct LoadSoftBodyArgs
 	double m_scale;
 	double m_mass;
 	double m_collisionMargin;
+	double m_initialPosition[3];
+        double m_initialOrientation[4];
 };
 
 struct b3LoadSoftBodyResultArgs
diff --git a/examples/SharedMemory/SharedMemoryPublic.h b/examples/SharedMemory/SharedMemoryPublic.h
index b2c2f6c68..6c62c788e 100644
--- a/examples/SharedMemory/SharedMemoryPublic.h
+++ b/examples/SharedMemory/SharedMemoryPublic.h
@@ -952,7 +952,7 @@ struct b3ForwardDynamicsAnalyticsIslandData
 	double m_remainingLeastSquaresResidual;
 };
 
-#define MAX_ISLANDS_ANALYTICS 1024
+#define MAX_ISLANDS_ANALYTICS 64
 
 struct b3ForwardDynamicsAnalyticsArgs
 {
diff --git a/examples/SharedMemory/b3PluginManager.cpp b/examples/SharedMemory/b3PluginManager.cpp
index fa06cd52b..7703abb54 100644
--- a/examples/SharedMemory/b3PluginManager.cpp
+++ b/examples/SharedMemory/b3PluginManager.cpp
@@ -112,7 +112,7 @@ struct b3PluginManagerInternalData
 	b3BulletDefaultFileIO m_defaultFileIO;
 	
 	b3PluginManagerInternalData()
-		: m_rpcCommandProcessorInterface(0), m_activeNotificationsBufferIndex(0), m_activeRendererPluginUid(-1), m_activeCollisionPluginUid(-1), m_numNotificationPlugins(0), m_activeFileIOPluginUid(-1)
+		: m_physicsDirect(0), m_rpcCommandProcessorInterface(0), m_activeNotificationsBufferIndex(0), m_activeRendererPluginUid(-1), m_activeCollisionPluginUid(-1), m_numNotificationPlugins(0), m_activeFileIOPluginUid(-1)
 	{
 	}
 };
diff --git a/examples/SharedMemory/b3RobotSimulatorClientAPI_NoDirect.cpp b/examples/SharedMemory/b3RobotSimulatorClientAPI_NoDirect.cpp
index 9efed7f7e..27bc23163 100644
--- a/examples/SharedMemory/b3RobotSimulatorClientAPI_NoDirect.cpp
+++ b/examples/SharedMemory/b3RobotSimulatorClientAPI_NoDirect.cpp
@@ -1134,7 +1134,7 @@ void b3RobotSimulatorClientAPI_NoDirect::submitProfileTiming(const std::string&
 	b3SubmitClientCommandAndWaitStatus(m_data->m_physicsClientHandle, commandHandle);
 }
 
-void b3RobotSimulatorClientAPI_NoDirect::loadSoftBody(const std::string& fileName, double scale, double mass, double collisionMargin)
+void b3RobotSimulatorClientAPI_NoDirect::loadSoftBody(const std::string& fileName, const struct b3RobotSimulatorLoadSoftBodyArgs& args)
 {
 	if (!isConnected())
 	{
@@ -1143,9 +1143,11 @@ void b3RobotSimulatorClientAPI_NoDirect::loadSoftBody(const std::string& fileNam
 	}
 
 	b3SharedMemoryCommandHandle command = b3LoadSoftBodyCommandInit(m_data->m_physicsClientHandle, fileName.c_str());
-	b3LoadSoftBodySetScale(command, scale);
-	b3LoadSoftBodySetMass(command, mass);
-	b3LoadSoftBodySetCollisionMargin(command, collisionMargin);
+	b3LoadSoftBodySetStartPosition(command, args.m_startPosition[0], args.m_startPosition[1], args.m_startPosition[2]);
+	b3LoadSoftBodySetStartOrientation(command, args.m_startOrientation[0], args.m_startOrientation[1], args.m_startOrientation[2], args.m_startOrientation[3]);
+	b3LoadSoftBodySetScale(command, args.m_scale);
+	b3LoadSoftBodySetMass(command, args.m_mass);
+	b3LoadSoftBodySetCollisionMargin(command, args.m_collisionMargin);
 	b3SubmitClientCommandAndWaitStatus(m_data->m_physicsClientHandle, command);
 }
 
diff --git a/examples/SharedMemory/b3RobotSimulatorClientAPI_NoDirect.h b/examples/SharedMemory/b3RobotSimulatorClientAPI_NoDirect.h
index 297c7e203..d8fd179c0 100644
--- a/examples/SharedMemory/b3RobotSimulatorClientAPI_NoDirect.h
+++ b/examples/SharedMemory/b3RobotSimulatorClientAPI_NoDirect.h
@@ -52,6 +52,43 @@ struct b3RobotSimulatorLoadSdfFileArgs
 	}
 };
 
+struct b3RobotSimulatorLoadSoftBodyArgs
+{
+	btVector3 m_startPosition;
+	btQuaternion m_startOrientation;
+	double m_scale;
+	double m_mass;
+	double m_collisionMargin;
+
+	b3RobotSimulatorLoadSoftBodyArgs(const btVector3 &startPos, const btQuaternion &startOrn, const double &scale, const double &mass, const double &collisionMargin)
+		: m_startPosition(startPos),
+		  m_startOrientation(startOrn),
+		  m_scale(scale),
+		  m_mass(mass),
+		  m_collisionMargin(collisionMargin)
+	{
+	}
+
+	b3RobotSimulatorLoadSoftBodyArgs(const btVector3 &startPos, const btQuaternion &startOrn)
+	{
+		b3RobotSimulatorLoadSoftBodyArgs(startPos, startOrn, 1.0, 1.0, 0.02);
+	}
+
+	b3RobotSimulatorLoadSoftBodyArgs()
+	{
+		b3RobotSimulatorLoadSoftBodyArgs(btVector3(0, 0, 0), btQuaternion(0, 0, 0, 1));
+	}
+
+	b3RobotSimulatorLoadSoftBodyArgs(double scale, double mass, double collisionMargin)
+		: m_startPosition(btVector3(0, 0, 0)),
+		  m_startOrientation(btQuaternion(0, 0, 0, 1)),
+		  m_scale(scale),
+		  m_mass(mass),
+		  m_collisionMargin(collisionMargin)
+	{
+	}
+};
+
 struct b3RobotSimulatorLoadFileResults
 {
 	btAlignedObjectArray<int> m_uniqueObjectIds;
@@ -647,7 +684,7 @@ public:
 
 	int getConstraintUniqueId(int serialIndex);
 
-	void loadSoftBody(const std::string &fileName, double scale, double mass, double collisionMargin);
+	void loadSoftBody(const std::string &fileName, const struct b3RobotSimulatorLoadSoftBodyArgs &args);
 
 	virtual void setGuiHelper(struct GUIHelperInterface *guiHelper);
 	virtual struct GUIHelperInterface *getGuiHelper();
diff --git a/examples/SharedMemory/grpc/ConvertGRPCBullet.cpp b/examples/SharedMemory/grpc/ConvertGRPCBullet.cpp
index 86acbe0d2..2596a3465 100644
--- a/examples/SharedMemory/grpc/ConvertGRPCBullet.cpp
+++ b/examples/SharedMemory/grpc/ConvertGRPCBullet.cpp
@@ -1261,7 +1261,7 @@ bool convertGRPCToStatus(const PyBulletStatus& grpcReply, SharedMemoryStatus& se
 			serverStatus.m_sendActualStateArgs.m_numDegreeOfFreedomU = numDegreeOfFreedomU;
 
 			serverStatus.m_sendActualStateArgs.m_stateDetails = (SendActualStateSharedMemoryStorage*)bufferServerToClient;
-			
+
 			for (int i = 0; i < numDegreeOfFreedomQ; i++)
 			{
 				serverStatus.m_sendActualStateArgs.m_stateDetails->m_actualStateQ[i] = stat->actualstateq(i);
@@ -1274,7 +1274,7 @@ bool convertGRPCToStatus(const PyBulletStatus& grpcReply, SharedMemoryStatus& se
 			{
 				serverStatus.m_sendActualStateArgs.m_rootLocalInertialFrame[i] = stat->rootlocalinertialframe(i);
 			}
-			for (int i = 0; i < numLinks * 6; i++)
+			for (int i = 0; i < numLinks * 7; i++)
 			{
 				serverStatus.m_sendActualStateArgs.m_stateDetails->m_linkLocalInertialFrames[i] = stat->linklocalinertialframes(i);
 			}
@@ -1413,6 +1413,22 @@ bool convertGRPCToStatus(const PyBulletStatus& grpcReply, SharedMemoryStatus& se
 
 			break;
 		}
+		case CMD_SDF_LOADING_COMPLETED:
+		{
+			converted = true;
+      const ::pybullet_grpc::SdfLoadedStatus* stat = &grpcReply.sdfstatus();
+      int numBodies = stat->bodyuniqueids_size();
+      if (numBodies > MAX_SDF_BODIES)
+			{
+				printf("SDF exceeds body capacity: %d > %d", numBodies, MAX_SDF_BODIES);
+			}
+      serverStatus.m_sdfLoadedArgs.m_numBodies = numBodies;
+      for (int i = 0; i < numBodies; i++)
+			{
+        serverStatus.m_sdfLoadedArgs.m_bodyUniqueIds[i] = stat->bodyuniqueids(i);
+			}
+			break;
+		}
 		case CMD_DESIRED_STATE_RECEIVED_COMPLETED:
 		{
 			converted = true;
@@ -1447,6 +1463,29 @@ bool convertGRPCToStatus(const PyBulletStatus& grpcReply, SharedMemoryStatus& se
 			serverStatus.m_sendPixelDataArguments.m_startingPixelIndex = cam->startingpixelindex();
 			break;
 		}
+		case CMD_GET_DYNAMICS_INFO_COMPLETED:
+		{
+			converted = true;
+			const ::pybullet_grpc::GetDynamicsStatus* stat = &grpcReply.getdynamicsstatus();
+			serverStatus.m_dynamicsInfo.m_mass = stat->mass();
+			serverStatus.m_dynamicsInfo.m_lateralFrictionCoeff = stat->lateralfriction();
+			serverStatus.m_dynamicsInfo.m_spinningFrictionCoeff = stat->spinningfriction();
+			serverStatus.m_dynamicsInfo.m_rollingFrictionCoeff = stat->rollingfriction();
+			serverStatus.m_dynamicsInfo.m_restitution = stat->restitution();
+			serverStatus.m_dynamicsInfo.m_linearDamping = stat->lineardamping();
+			serverStatus.m_dynamicsInfo.m_angularDamping = stat->angulardamping();
+			serverStatus.m_dynamicsInfo.m_contactStiffness = stat->contactstiffness();
+			serverStatus.m_dynamicsInfo.m_contactDamping = stat->contactdamping();
+			serverStatus.m_dynamicsInfo.m_localInertialDiagonal[0] = stat->localinertiadiagonal().x();
+			serverStatus.m_dynamicsInfo.m_localInertialDiagonal[1] = stat->localinertiadiagonal().y();
+			serverStatus.m_dynamicsInfo.m_localInertialDiagonal[2] = stat->localinertiadiagonal().z();
+			serverStatus.m_dynamicsInfo.m_frictionAnchor = stat->frictionanchor();
+			serverStatus.m_dynamicsInfo.m_ccdSweptSphereRadius = stat->ccdsweptsphereradius();
+			serverStatus.m_dynamicsInfo.m_contactProcessingThreshold = stat->contactprocessingthreshold();
+			serverStatus.m_dynamicsInfo.m_activationState = stat->activationstate();
+			break;
+		}
+
 		default:
 		{
 #endif  //ALLOW_GRPC_STATUS_CONVERSION
@@ -1658,7 +1697,10 @@ bool convertStatusToGRPC(const SharedMemoryStatus& serverStatus, char* bufferSer
 		case CMD_ACTUAL_STATE_UPDATE_COMPLETED:
 		{
 			converted = true;
-			b3SharedMemoryStatusHandle statusHandle = (b3SharedMemoryStatusHandle)&serverStatus;
+			SharedMemoryStatus* status = (SharedMemoryStatus*)&serverStatus;
+      status->m_sendActualStateArgs.m_stateDetails = (SendActualStateSharedMemoryStorage*)bufferServerToClient;
+			b3SharedMemoryStatusHandle statusHandle = (b3SharedMemoryStatusHandle)status;
+
 			int bodyUniqueId;
 			int numLinks;
 
@@ -1706,7 +1748,7 @@ bool convertStatusToGRPC(const SharedMemoryStatus& serverStatus, char* bufferSer
 				{
 					stat->add_rootlocalinertialframe(rootLocalInertialFramePtr[i]);
 				}
-				for (int i = 0; i < numLinks * 6; i++)
+				for (int i = 0; i < numLinks * 7; i++)
 				{
 					stat->add_linklocalinertialframes(linkLocalInertialFrames[i]);
 				}
diff --git a/examples/SharedMemory/grpc/pybullet_client.py b/examples/SharedMemory/grpc/pybullet_client.py
index dab9193bb..6b4a2d6de 100644
--- a/examples/SharedMemory/grpc/pybullet_client.py
+++ b/examples/SharedMemory/grpc/pybullet_client.py
@@ -10,69 +10,86 @@ import pybullet_pb2_grpc
 #todo: how to add this?
 MJCF_COLORS_FROM_FILE = 512
 
-def run():
-	print("grpc.insecure_channel")
-	channel = grpc.insecure_channel('localhost:6667')
-	print("pybullet_pb2_grpc.PyBulletAPIStub")
-	stub = pybullet_pb2_grpc.PyBulletAPIStub(channel)
-	response=0
-
-	print("submit CheckVersionCommand")
-	response = stub.SubmitCommand(pybullet_pb2.PyBulletCommand(checkVersionCommand=pybullet_pb2.CheckVersionCommand(clientVersion=123)))
-	print("PyBullet client received: " , response)
-
-
-        print("submit_ResetSimulationCommand")
-        response = stub.SubmitCommand(pybullet_pb2.PyBulletCommand(resetSimulationCommand=pybullet_pb2.ResetSimulationCommand()))
-        print("PyBullet client received: ", response)
-
-
-	print("submit LoadUrdfCommand ")
-	response = stub.SubmitCommand(pybullet_pb2.PyBulletCommand(loadUrdfCommand=pybullet_pb2.LoadUrdfCommand(fileName="door.urdf", initialPosition=pybullet_pb2.vec3(x=0,y=0,z=0), useMultiBody=True, useFixedBase=True, globalScaling=2, flags = 1)))
-	print("PyBullet client received: " , response)
-	bodyUniqueId = response.urdfStatus.bodyUniqueId
-	
-	
-
-	print("submit LoadSdfCommand")
-	response = stub.SubmitCommand(pybullet_pb2.PyBulletCommand(loadSdfCommand=pybullet_pb2.LoadSdfCommand(fileName="two_cubes.sdf", useMultiBody=True, globalScaling=2)))
-	print("PyBullet client received: " , response)
-	
-
-	print("submit LoadMjcfCommand")
-	response = stub.SubmitCommand(pybullet_pb2.PyBulletCommand(loadMjcfCommand=pybullet_pb2.LoadMjcfCommand(fileName="mjcf/humanoid.xml",flags=MJCF_COLORS_FROM_FILE)))
-	print("PyBullet client received: " , response)
-
-	
-	
-	print("submit ChangeDynamicsCommand ")
-	response = stub.SubmitCommand(pybullet_pb2.PyBulletCommand(changeDynamicsCommand=pybullet_pb2.ChangeDynamicsCommand(bodyUniqueId=bodyUniqueId, linkIndex=-1, mass=10)))
-	print("PyBullet client received: " , response)
-	
-	print("submit GetDynamicsCommand ")
-	response = stub.SubmitCommand(pybullet_pb2.PyBulletCommand(getDynamicsCommand=pybullet_pb2.GetDynamicsCommand(bodyUniqueId=bodyUniqueId, linkIndex=-1)))
-	print("PyBullet client received: " , response)
-
-	print("submit InitPoseCommand")
-	response = stub.SubmitCommand(pybullet_pb2.PyBulletCommand(initPoseCommand=pybullet_pb2.InitPoseCommand(bodyUniqueId=bodyUniqueId, initialStateQ=[1,2,3],hasInitialStateQ=[1,1,1])))
-	print("PyBullet client received: " , response)
-	
-	print("submit RequestActualStateCommand")
-	response = stub.SubmitCommand(pybullet_pb2.PyBulletCommand(requestActualStateCommand=pybullet_pb2.RequestActualStateCommand(bodyUniqueId=bodyUniqueId, computeForwardKinematics=True, computeLinkVelocities=True )))
-	print("PyBullet client received: " , response)
-	
-	
-        i=0
-        while(True):
-                        i=i+1
-			print("submit StepSimulationCommand: ", i)
-			response = stub.SubmitCommand(pybullet_pb2.PyBulletCommand(stepSimulationCommand=pybullet_pb2.StepSimulationCommand()))
-			print("PyBullet client received: " , response.statusType)
-  
-  #print("TerminateServerCommand")
-	#response = stub.SubmitCommand(pybullet_pb2.PyBulletCommand(terminateServerCommand=pybullet_pb2.TerminateServerCommand()))
-	#print("PyBullet client received: " , response.statusType)
 
+def run():
+  print("grpc.insecure_channel")
+  channel = grpc.insecure_channel('localhost:6667')
+  print("pybullet_pb2_grpc.PyBulletAPIStub")
+  stub = pybullet_pb2_grpc.PyBulletAPIStub(channel)
+  response = 0
+
+  print("submit CheckVersionCommand")
+  response = stub.SubmitCommand(
+      pybullet_pb2.PyBulletCommand(checkVersionCommand=pybullet_pb2.CheckVersionCommand(
+          clientVersion=123)))
+  print("PyBullet client received: ", response)
+
+  print("submit_ResetSimulationCommand")
+  response = stub.SubmitCommand(
+      pybullet_pb2.PyBulletCommand(resetSimulationCommand=pybullet_pb2.ResetSimulationCommand()))
+  print("PyBullet client received: ", response)
+
+  print("submit LoadUrdfCommand ")
+  response = stub.SubmitCommand(
+      pybullet_pb2.PyBulletCommand(loadUrdfCommand=pybullet_pb2.LoadUrdfCommand(
+          fileName="door.urdf",
+          initialPosition=pybullet_pb2.vec3(x=0, y=0, z=0),
+          useMultiBody=True,
+          useFixedBase=True,
+          globalScaling=2,
+          flags=1)))
+  print("PyBullet client received: ", response)
+  bodyUniqueId = response.urdfStatus.bodyUniqueId
+
+  print("submit LoadSdfCommand")
+  response = stub.SubmitCommand(
+      pybullet_pb2.PyBulletCommand(loadSdfCommand=pybullet_pb2.LoadSdfCommand(
+          fileName="two_cubes.sdf", useMultiBody=True, globalScaling=2)))
+  print("PyBullet client received: ", response)
+
+  print("submit LoadMjcfCommand")
+  response = stub.SubmitCommand(
+      pybullet_pb2.PyBulletCommand(loadMjcfCommand=pybullet_pb2.LoadMjcfCommand(
+          fileName="mjcf/humanoid.xml", flags=MJCF_COLORS_FROM_FILE)))
+  print("PyBullet client received: ", response)
+
+  print("submit ChangeDynamicsCommand ")
+  response = stub.SubmitCommand(
+      pybullet_pb2.PyBulletCommand(changeDynamicsCommand=pybullet_pb2.ChangeDynamicsCommand(
+          bodyUniqueId=bodyUniqueId, linkIndex=-1, mass=10)))
+  print("PyBullet client received: ", response)
+
+  print("submit GetDynamicsCommand ")
+  response = stub.SubmitCommand(
+      pybullet_pb2.PyBulletCommand(getDynamicsCommand=pybullet_pb2.GetDynamicsCommand(
+          bodyUniqueId=bodyUniqueId, linkIndex=-1)))
+  print("PyBullet client received: ", response)
+
+  print("submit InitPoseCommand")
+  response = stub.SubmitCommand(
+      pybullet_pb2.PyBulletCommand(initPoseCommand=pybullet_pb2.InitPoseCommand(
+          bodyUniqueId=bodyUniqueId, initialStateQ=[1, 2, 3], hasInitialStateQ=[1, 1, 1])))
+  print("PyBullet client received: ", response)
+
+  print("submit RequestActualStateCommand")
+  response = stub.SubmitCommand(
+      pybullet_pb2.
+      PyBulletCommand(requestActualStateCommand=pybullet_pb2.RequestActualStateCommand(
+          bodyUniqueId=bodyUniqueId, computeForwardKinematics=True, computeLinkVelocities=True)))
+  print("PyBullet client received: ", response)
+
+  i = 0
+  while (True):
+    i = i + 1
+    print("submit StepSimulationCommand: ", i)
+    response = stub.SubmitCommand(
+        pybullet_pb2.PyBulletCommand(stepSimulationCommand=pybullet_pb2.StepSimulationCommand()))
+    print("PyBullet client received: ", response.statusType)
+
+
+#print("TerminateServerCommand")
+#response = stub.SubmitCommand(pybullet_pb2.PyBulletCommand(terminateServerCommand=pybullet_pb2.TerminateServerCommand()))
+#print("PyBullet client received: " , response.statusType)
 
 if __name__ == '__main__':
-	run()
+  run()
diff --git a/examples/SharedMemory/plugins/fileIOPlugin/fileIOPlugin.cpp b/examples/SharedMemory/plugins/fileIOPlugin/fileIOPlugin.cpp
index 43d20387a..0be09d9db 100644
--- a/examples/SharedMemory/plugins/fileIOPlugin/fileIOPlugin.cpp
+++ b/examples/SharedMemory/plugins/fileIOPlugin/fileIOPlugin.cpp
@@ -388,35 +388,37 @@ struct WrapperFileIO : public CommonFileIOInterface
 						int childHandle = childFileIO->fileOpen(fileName, mode);
 						if (childHandle>=0)
 						{
-							int fileSize = childFileIO->getFileSize(childHandle);
-							char* buffer = 0;
-							if (fileSize)
+							if (m_enableFileCaching)
 							{
-								buffer = m_cachedFiles.allocateBuffer(fileSize);
-								if (buffer)
+								int fileSize = childFileIO->getFileSize(childHandle);
+								char* buffer = 0;
+								if (fileSize)
 								{
-									int readBytes = childFileIO->fileRead(childHandle, buffer, fileSize);
-									if (readBytes!=fileSize)
+									buffer = m_cachedFiles.allocateBuffer(fileSize);
+									if (buffer)
 									{
-										if (readBytes<fileSize)
-										{
-											fileSize = readBytes;
-										} else
+										int readBytes = childFileIO->fileRead(childHandle, buffer, fileSize);
+										if (readBytes!=fileSize)
 										{
-											printf("WrapperFileIO error: reading more bytes (%d) then reported file size (%d) of file %s.\n", readBytes, fileSize, fileName);
+											if (readBytes<fileSize)
+												{
+												fileSize = readBytes;
+											} else
+											{
+												printf("WrapperFileIO error: reading more bytes (%d) then reported file size (%d) of file %s.\n", readBytes, fileSize, fileName);
+											}
 										}
+									} else
+									{
+										fileSize=0;
 									}
-								} else
-								{
-									fileSize=0;
 								}
-							}
 
-							//potentially register a zero byte file, or files that only can be read partially
-							if (m_enableFileCaching)
-							{
+								//potentially register a zero byte file, or files that only can be read partially
+							
 								m_cachedFiles.registerFile(fileName, buffer, fileSize);
 							}
+							
 							childFileIO->fileClose(childHandle);
 							break;
 						}
diff --git a/examples/SharedMemory/plugins/grpcPlugin/grpcPlugin.cpp b/examples/SharedMemory/plugins/grpcPlugin/grpcPlugin.cpp
index eb6c04d9f..3193840d1 100644
--- a/examples/SharedMemory/plugins/grpcPlugin/grpcPlugin.cpp
+++ b/examples/SharedMemory/plugins/grpcPlugin/grpcPlugin.cpp
@@ -11,6 +11,9 @@
 
 #include <stdio.h>
 
+#include <mutex>
+#include <thread>
+
 #include <grpc++/grpc++.h>
 #include <grpc/support/log.h>
 #include "../../../Utils/b3Clock.h"
@@ -44,6 +47,9 @@ public:
 		if (server_)
 		{
 			server_->Shutdown();
+			m_requestThreadCancelled = true;
+			m_requestThread->join();
+			delete m_requestThread;
 			// Always shutdown the completion queue after the server.
 			cq_->Shutdown();
 			server_ = 0;
@@ -64,6 +70,10 @@ public:
 		server_ = m_builder.BuildAndStart();
 		std::cout << "grpcPlugin Bullet Physics GRPC server listening on " << hostNamePort << std::endl;
 
+		//Start the thread to gather the requests.
+		m_requestThreadCancelled = false;
+		m_requestThread = new std::thread(&ServerImpl::GatherRequests, this);
+
 		// Proceed to the server's main loop.
 		InitRpcs(comProc);
 	}
@@ -72,25 +82,13 @@ public:
 	bool HandleSingleRpc()
 	{
 		CallData::CallStatus status = CallData::CallStatus::CREATE;
+		std::lock_guard<std::mutex> guard(m_queueMutex);
+    if (!m_requestQueue.empty()) {
+      void* tag = m_requestQueue.front();
+      m_requestQueue.pop_front();
+      status = static_cast<CallData*>(tag)->Proceed();
+    }
 
-		{
-			void* tag;  // uniquely identifies a request.
-			bool ok;
-
-			// Block waiting to read the next event from the completion queue. The
-			// event is uniquely identified by its tag, which in this case is the
-			// memory address of a CallData instance.
-			// The return value of Next should always be checked. This return value
-			// tells us whether there is any kind of event or cq_ is shutting down.
-
-			grpc::CompletionQueue::NextStatus nextStatus = cq_->AsyncNext(&tag, &ok, gpr_now(GPR_CLOCK_MONOTONIC));
-			if (nextStatus == grpc::CompletionQueue::NextStatus::GOT_EVENT)
-			{
-				//GPR_ASSERT(cq_->Next(&tag, &ok));
-				GPR_ASSERT(ok);
-				status = static_cast<CallData*>(tag)->Proceed();
-			}
-		}
 		return status == CallData::CallStatus::TERMINATE;
 	}
 
@@ -206,7 +204,9 @@ private:
 			{
 				GPR_ASSERT(status_ == FINISH);
 				// Once in the FINISH state, deallocate ourselves (CallData).
+				CallData::CallStatus tmpStatus = status_;
 				delete this;
+				return tmpStatus;
 			}
 			return status_;
 		}
@@ -250,6 +250,39 @@ private:
 	std::unique_ptr<ServerCompletionQueue> cq_;
 	PyBulletAPI::AsyncService service_;
 	std::unique_ptr<Server> server_;
+
+	// Mutex to protect access to the request queue variables (m_requestQueue,
+	// m_requestThread, m_requestThreadCancelled).
+	std::mutex m_queueMutex;
+
+	// List of outstanding request tags.
+	std::list<void*> m_requestQueue;
+
+	// Whether or not the gathering thread is cancelled.
+	bool m_requestThreadCancelled;
+
+	// Thread to gather requests from the completion queue.
+	std::thread* m_requestThread;
+
+	void GatherRequests() {
+		void* tag;  // uniquely identifies a request.
+		bool ok;
+
+		while(!m_requestThreadCancelled) {
+			// Block waiting to read the next event from the completion queue. The
+			// event is uniquely identified by its tag, which in this case is the
+			// memory address of a CallData instance.
+			// The return value of Next should always be checked. This return value
+			// tells us whether there is any kind of event or cq_ is shutting down.
+			grpc::CompletionQueue::NextStatus nextStatus = cq_->AsyncNext(&tag, &ok, gpr_now(GPR_CLOCK_MONOTONIC));
+			if (nextStatus == grpc::CompletionQueue::NextStatus::GOT_EVENT)
+			{
+				GPR_ASSERT(ok);
+				std::lock_guard<std::mutex> guard(m_queueMutex);
+				m_requestQueue.push_back(tag);
+			}
+		}
+	}
 };
 
 struct grpcMyClass
diff --git a/examples/pybullet/examples/addPlanarReflection.py b/examples/pybullet/examples/addPlanarReflection.py
index 419789e90..6b939bbcf 100644
--- a/examples/pybullet/examples/addPlanarReflection.py
+++ b/examples/pybullet/examples/addPlanarReflection.py
@@ -2,74 +2,100 @@ import pybullet as p
 import time
 import math
 
-def getRayFromTo(mouseX,mouseY):
-	width, height, viewMat, projMat, cameraUp, camForward, horizon,vertical, _,_,dist, camTarget = p.getDebugVisualizerCamera()
-	camPos = [camTarget[0] - dist*camForward[0],camTarget[1] - dist*camForward[1],camTarget[2] - dist*camForward[2]]
-	farPlane = 10000
-	rayForward = [(camTarget[0]-camPos[0]),(camTarget[1]-camPos[1]),(camTarget[2]-camPos[2])]
-	invLen = farPlane*1./(math.sqrt(rayForward[0]*rayForward[0]+rayForward[1]*rayForward[1]+rayForward[2]*rayForward[2]))
-	rayForward = [invLen*rayForward[0],invLen*rayForward[1],invLen*rayForward[2]]
-	rayFrom = camPos
-	oneOverWidth = float(1)/float(width)
-	oneOverHeight = float(1)/float(height)
-	dHor = [horizon[0] * oneOverWidth,horizon[1] * oneOverWidth,horizon[2] * oneOverWidth]
-	dVer = [vertical[0] * oneOverHeight,vertical[1] * oneOverHeight,vertical[2] * oneOverHeight]
-	rayToCenter=[rayFrom[0]+rayForward[0],rayFrom[1]+rayForward[1],rayFrom[2]+rayForward[2]]
-	rayTo = [rayFrom[0]+rayForward[0]  - 0.5 * horizon[0] + 0.5 * vertical[0]+float(mouseX)*dHor[0]-float(mouseY)*dVer[0],
-					rayFrom[1]+rayForward[1]  - 0.5 * horizon[1] + 0.5 * vertical[1]+float(mouseX)*dHor[1]-float(mouseY)*dVer[1],
-					rayFrom[2]+rayForward[2]  - 0.5 * horizon[2] + 0.5 * vertical[2]+float(mouseX)*dHor[2]-float(mouseY)*dVer[2]]
-	return rayFrom,rayTo
+
+def getRayFromTo(mouseX, mouseY):
+  width, height, viewMat, projMat, cameraUp, camForward, horizon, vertical, _, _, dist, camTarget = p.getDebugVisualizerCamera(
+  )
+  camPos = [
+      camTarget[0] - dist * camForward[0], camTarget[1] - dist * camForward[1],
+      camTarget[2] - dist * camForward[2]
+  ]
+  farPlane = 10000
+  rayForward = [(camTarget[0] - camPos[0]), (camTarget[1] - camPos[1]), (camTarget[2] - camPos[2])]
+  invLen = farPlane * 1. / (math.sqrt(rayForward[0] * rayForward[0] + rayForward[1] *
+                                      rayForward[1] + rayForward[2] * rayForward[2]))
+  rayForward = [invLen * rayForward[0], invLen * rayForward[1], invLen * rayForward[2]]
+  rayFrom = camPos
+  oneOverWidth = float(1) / float(width)
+  oneOverHeight = float(1) / float(height)
+  dHor = [horizon[0] * oneOverWidth, horizon[1] * oneOverWidth, horizon[2] * oneOverWidth]
+  dVer = [vertical[0] * oneOverHeight, vertical[1] * oneOverHeight, vertical[2] * oneOverHeight]
+  rayToCenter = [
+      rayFrom[0] + rayForward[0], rayFrom[1] + rayForward[1], rayFrom[2] + rayForward[2]
+  ]
+  rayTo = [
+      rayFrom[0] + rayForward[0] - 0.5 * horizon[0] + 0.5 * vertical[0] + float(mouseX) * dHor[0] -
+      float(mouseY) * dVer[0], rayFrom[1] + rayForward[1] - 0.5 * horizon[1] + 0.5 * vertical[1] +
+      float(mouseX) * dHor[1] - float(mouseY) * dVer[1], rayFrom[2] + rayForward[2] -
+      0.5 * horizon[2] + 0.5 * vertical[2] + float(mouseX) * dHor[2] - float(mouseY) * dVer[2]
+  ]
+  return rayFrom, rayTo
+
 
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	p.connect(p.GUI)
+if (cid < 0):
+  p.connect(p.GUI)
 p.setPhysicsEngineParameter(numSolverIterations=10)
-p.setTimeStep(1./120.)
+p.setTimeStep(1. / 120.)
 logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "visualShapeBench.json")
 #useMaximalCoordinates is much faster then the default reduced coordinates (Featherstone)
 p.loadURDF("plane_transparent.urdf", useMaximalCoordinates=True)
 #disable rendering during creation.
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)
-p.configureDebugVisualizer(p.COV_ENABLE_PLANAR_REFLECTION,1)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
+p.configureDebugVisualizer(p.COV_ENABLE_PLANAR_REFLECTION, 1)
 
-p.configureDebugVisualizer(p.COV_ENABLE_GUI,0)
+p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0)
 #disable tinyrenderer, software (CPU) renderer, we don't use it here
-p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER,0)
+p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER, 0)
 
-shift = [0,-0.02,0]
-meshScale=[0.1,0.1,0.1]
+shift = [0, -0.02, 0]
+meshScale = [0.1, 0.1, 0.1]
 #the visual shape and collision shape can be re-used by all createMultiBody instances (instancing)
-visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,fileName="duck.obj", rgbaColor=[1,1,1,1], specularColor=[0.4,.4,0], visualFramePosition=shift, meshScale=meshScale)
-collisionShapeId = p.createCollisionShape(shapeType=p.GEOM_MESH, fileName="duck_vhacd.obj", collisionFramePosition=shift,meshScale=meshScale)
+visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,
+                                    fileName="duck.obj",
+                                    rgbaColor=[1, 1, 1, 1],
+                                    specularColor=[0.4, .4, 0],
+                                    visualFramePosition=shift,
+                                    meshScale=meshScale)
+collisionShapeId = p.createCollisionShape(shapeType=p.GEOM_MESH,
+                                          fileName="duck_vhacd.obj",
+                                          collisionFramePosition=shift,
+                                          meshScale=meshScale)
 
 rangex = 3
 rangey = 3
-for i in range (rangex):
-	for j in range (rangey ):
-		p.createMultiBody(baseMass=1,baseInertialFramePosition=[0,0,0],baseCollisionShapeIndex=collisionShapeId, baseVisualShapeIndex = visualShapeId, basePosition = [((-rangex/2)+i)*meshScale[0]*2,(-rangey/2+j)*meshScale[1]*2,1], useMaximalCoordinates=True)
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
+for i in range(rangex):
+  for j in range(rangey):
+    p.createMultiBody(baseMass=1,
+                      baseInertialFramePosition=[0, 0, 0],
+                      baseCollisionShapeIndex=collisionShapeId,
+                      baseVisualShapeIndex=visualShapeId,
+                      basePosition=[((-rangex / 2) + i) * meshScale[0] * 2,
+                                    (-rangey / 2 + j) * meshScale[1] * 2, 1],
+                      useMaximalCoordinates=True)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
 p.stopStateLogging(logId)
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 p.setRealTimeSimulation(1)
 
-colors = [[1,0,0,1],[0,1,0,1],[0,0,1,1],[1,1,1,1]]
+colors = [[1, 0, 0, 1], [0, 1, 0, 1], [0, 0, 1, 1], [1, 1, 1, 1]]
 currentColor = 0
 
 while (1):
-	mouseEvents = p.getMouseEvents()
-	for e in mouseEvents:
-		if ((e[0] == 2) and (e[3]==0) and (e[4]& p.KEY_WAS_TRIGGERED)):
-			mouseX = e[1]
-			mouseY = e[2]
-			rayFrom,rayTo=getRayFromTo(mouseX,mouseY)
-			rayInfo = p.rayTest(rayFrom,rayTo)
-			#p.addUserDebugLine(rayFrom,rayTo,[1,0,0],3)
-			for l in range(len(rayInfo)):
-				hit = rayInfo[l]
-				objectUid = hit[0]
-				if (objectUid>=1):
-					#p.removeBody(objectUid)
-					p.changeVisualShape(objectUid,-1,rgbaColor=colors[currentColor])
-					currentColor+=1
-					if (currentColor>=len(colors)):
-						currentColor=0
+  mouseEvents = p.getMouseEvents()
+  for e in mouseEvents:
+    if ((e[0] == 2) and (e[3] == 0) and (e[4] & p.KEY_WAS_TRIGGERED)):
+      mouseX = e[1]
+      mouseY = e[2]
+      rayFrom, rayTo = getRayFromTo(mouseX, mouseY)
+      rayInfo = p.rayTest(rayFrom, rayTo)
+      #p.addUserDebugLine(rayFrom,rayTo,[1,0,0],3)
+      for l in range(len(rayInfo)):
+        hit = rayInfo[l]
+        objectUid = hit[0]
+        if (objectUid >= 1):
+          #p.removeBody(objectUid)
+          p.changeVisualShape(objectUid, -1, rgbaColor=colors[currentColor])
+          currentColor += 1
+          if (currentColor >= len(colors)):
+            currentColor = 0
diff --git a/examples/pybullet/examples/batchRayCast.py b/examples/pybullet/examples/batchRayCast.py
index 44b262dba..ab2e3bc3c 100644
--- a/examples/pybullet/examples/batchRayCast.py
+++ b/examples/pybullet/examples/batchRayCast.py
@@ -5,73 +5,70 @@ import math
 useGui = True
 
 if (useGui):
-	p.connect(p.GUI)
+  p.connect(p.GUI)
 else:
-	p.connect(p.DIRECT)
+  p.connect(p.DIRECT)
 
-p.configureDebugVisualizer(p.COV_ENABLE_GUI,0)
+p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0)
 #p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)
 
 #p.loadURDF("samurai.urdf")
-p.loadURDF("r2d2.urdf",[3,3,1])
+p.loadURDF("r2d2.urdf", [3, 3, 1])
 
-
-rayFrom=[]
-rayTo=[]
-rayIds=[]
+rayFrom = []
+rayTo = []
+rayIds = []
 
 numRays = 1024
-	
-rayLen = 13
 
+rayLen = 13
 
-rayHitColor = [1,0,0]
-rayMissColor = [0,1,0]
+rayHitColor = [1, 0, 0]
+rayMissColor = [0, 1, 0]
 
 replaceLines = True
-	
-for i in range (numRays):
-	rayFrom.append([0,0,1])
-	rayTo.append([rayLen*math.sin(2.*math.pi*float(i)/numRays), rayLen*math.cos(2.*math.pi*float(i)/numRays),1])
-	if (replaceLines):
-		rayIds.append(p.addUserDebugLine(rayFrom[i], rayTo[i], rayMissColor))
-	else:
-		rayIds.append(-1)
+
+for i in range(numRays):
+  rayFrom.append([0, 0, 1])
+  rayTo.append([
+      rayLen * math.sin(2. * math.pi * float(i) / numRays),
+      rayLen * math.cos(2. * math.pi * float(i) / numRays), 1
+  ])
+  if (replaceLines):
+    rayIds.append(p.addUserDebugLine(rayFrom[i], rayTo[i], rayMissColor))
+  else:
+    rayIds.append(-1)
 
 if (not useGui):
-	timingLog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS,"rayCastBench.json")
+  timingLog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "rayCastBench.json")
 
 numSteps = 10
 if (useGui):
-	numSteps = 327680
-
-for i in range (numSteps):
-	p.stepSimulation()
-	for j in range (8):
-		results = p.rayTestBatch(rayFrom,rayTo,j+1)
-	
-	#for i in range (10):
-	#	p.removeAllUserDebugItems()	
-		
-		
-	
-	
-	if (useGui):
-		if (not replaceLines):
-			p.removeAllUserDebugItems()
-			
-		for i in range (numRays):
-			hitObjectUid=results[i][0]
-			
-			
-			if (hitObjectUid<0):
-				hitPosition =[0,0,0]
-				p.addUserDebugLine(rayFrom[i],rayTo[i], rayMissColor,replaceItemUniqueId=rayIds[i])
-			else:
-				hitPosition = results[i][3]
-				p.addUserDebugLine(rayFrom[i],hitPosition, rayHitColor,replaceItemUniqueId=rayIds[i])
-		
-	#time.sleep(1./240.)
-	
+  numSteps = 327680
+
+for i in range(numSteps):
+  p.stepSimulation()
+  for j in range(8):
+    results = p.rayTestBatch(rayFrom, rayTo, j + 1)
+
+  #for i in range (10):
+  #	p.removeAllUserDebugItems()
+
+  if (useGui):
+    if (not replaceLines):
+      p.removeAllUserDebugItems()
+
+    for i in range(numRays):
+      hitObjectUid = results[i][0]
+
+      if (hitObjectUid < 0):
+        hitPosition = [0, 0, 0]
+        p.addUserDebugLine(rayFrom[i], rayTo[i], rayMissColor, replaceItemUniqueId=rayIds[i])
+      else:
+        hitPosition = results[i][3]
+        p.addUserDebugLine(rayFrom[i], hitPosition, rayHitColor, replaceItemUniqueId=rayIds[i])
+
+  #time.sleep(1./240.)
+
 if (not useGui):
-	p.stopStateLogging(timingLog)
\ No newline at end of file
+  p.stopStateLogging(timingLog)
diff --git a/examples/pybullet/examples/biped2d_pybullet.py b/examples/pybullet/examples/biped2d_pybullet.py
index 01d3aaec9..9b7cf687f 100644
--- a/examples/pybullet/examples/biped2d_pybullet.py
+++ b/examples/pybullet/examples/biped2d_pybullet.py
@@ -4,28 +4,26 @@ import os
 import time
 GRAVITY = -9.8
 dt = 1e-3
-iters=2000
+iters = 2000
 
 physicsClient = p.connect(p.GUI)
 p.setAdditionalSearchPath(pybullet_data.getDataPath())
 p.resetSimulation()
 #p.setRealTimeSimulation(True)
-p.setGravity(0,0,GRAVITY)
+p.setGravity(0, 0, GRAVITY)
 p.setTimeStep(dt)
 planeId = p.loadURDF("plane.urdf")
-cubeStartPos = [0,0,1.13]
-cubeStartOrientation = p.getQuaternionFromEuler([0.,0,0])
-botId = p.loadURDF("biped/biped2d_pybullet.urdf",
-        cubeStartPos,
-        cubeStartOrientation) 
- 
-#disable the default velocity motors 
+cubeStartPos = [0, 0, 1.13]
+cubeStartOrientation = p.getQuaternionFromEuler([0., 0, 0])
+botId = p.loadURDF("biped/biped2d_pybullet.urdf", cubeStartPos, cubeStartOrientation)
+
+#disable the default velocity motors
 #and set some position control with small force to emulate joint friction/return to a rest pose
-jointFrictionForce=1
-for joint in range (p.getNumJoints(botId)):
-	p.setJointMotorControl2(botId,joint,p.POSITION_CONTROL,force=jointFrictionForce)
+jointFrictionForce = 1
+for joint in range(p.getNumJoints(botId)):
+  p.setJointMotorControl2(botId, joint, p.POSITION_CONTROL, force=jointFrictionForce)
 
-#for i in range(10000):     
+#for i in range(10000):
 #     p.setJointMotorControl2(botId, 1, p.TORQUE_CONTROL, force=1098.0)
 #     p.stepSimulation()
 #import ipdb
@@ -33,8 +31,8 @@ for joint in range (p.getNumJoints(botId)):
 import time
 p.setRealTimeSimulation(1)
 while (1):
-    #p.stepSimulation()
-    #p.setJointMotorControl2(botId, 1, p.TORQUE_CONTROL, force=1098.0)
-    p.setGravity(0,0,GRAVITY)
-    time.sleep(1/240.)
+  #p.stepSimulation()
+  #p.setJointMotorControl2(botId, 1, p.TORQUE_CONTROL, force=1098.0)
+  p.setGravity(0, 0, GRAVITY)
+  time.sleep(1 / 240.)
 time.sleep(1000)
diff --git a/examples/pybullet/examples/changeDynamicsMass.py b/examples/pybullet/examples/changeDynamicsMass.py
index fcd625638..8e14711cd 100644
--- a/examples/pybullet/examples/changeDynamicsMass.py
+++ b/examples/pybullet/examples/changeDynamicsMass.py
@@ -2,14 +2,14 @@ import pybullet as p
 import time
 
 p.connect(p.GUI)
-cube2 = p.loadURDF("cube.urdf",[0,0,3], useFixedBase=True)
+cube2 = p.loadURDF("cube.urdf", [0, 0, 3], useFixedBase=True)
 cube = p.loadURDF("cube.urdf", useFixedBase=True)
-p.setGravity(0,0,-10)
-timeStep = 1./240.
+p.setGravity(0, 0, -10)
+timeStep = 1. / 240.
 p.setTimeStep(timeStep)
-p.changeDynamics(cube2,-1,mass=1)
+p.changeDynamics(cube2, -1, mass=1)
 #now cube2 will have a floating base and move
 
 while (p.isConnected()):
-	p.stepSimulation()
-	time.sleep(timeStep)
\ No newline at end of file
+  p.stepSimulation()
+  time.sleep(timeStep)
diff --git a/examples/pybullet/examples/changeTexture.py b/examples/pybullet/examples/changeTexture.py
index c9e592f88..835b4533e 100644
--- a/examples/pybullet/examples/changeTexture.py
+++ b/examples/pybullet/examples/changeTexture.py
@@ -1,43 +1,41 @@
 import pybullet as p
 import time
 p.connect(p.GUI)
-planeUidA = p.loadURDF("plane_transparent.urdf",[0,0,0])
-planeUid = p.loadURDF("plane_transparent.urdf",[0,0,-1])
+planeUidA = p.loadURDF("plane_transparent.urdf", [0, 0, 0])
+planeUid = p.loadURDF("plane_transparent.urdf", [0, 0, -1])
 
 texUid = p.loadTexture("tex256.png")
 
-p.changeVisualShape(planeUidA,-1,rgbaColor=[1,1,1,0.5])
-p.changeVisualShape(planeUid,-1,rgbaColor=[1,1,1,0.5])
-p.changeVisualShape(planeUid,-1, textureUniqueId = texUid)
+p.changeVisualShape(planeUidA, -1, rgbaColor=[1, 1, 1, 0.5])
+p.changeVisualShape(planeUid, -1, rgbaColor=[1, 1, 1, 0.5])
+p.changeVisualShape(planeUid, -1, textureUniqueId=texUid)
 
 width = 256
 height = 256
-pixels = [255]*width*height*3
+pixels = [255] * width * height * 3
 colorR = 0
 colorG = 0
 colorB = 0
 
-
 #p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)
 #p.configureDebugVisualizer(p.COV_ENABLE_GUI,0)
 
-blue=0
+blue = 0
 logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "renderbench.json")
-for i in range (100000):
-	p.stepSimulation()
-	for i in range (width):
-		for j in range(height):
-			pixels[(i+j*width)*3+0]=i
-			pixels[(i+j*width)*3+1]=(j+blue)%256
-			pixels[(i+j*width)*3+2]=blue
-	blue=blue+1
-	p.changeTexture(texUid, pixels,width,height)
-	start = time.time()
-	p.getCameraImage(300,300,renderer=p.ER_BULLET_HARDWARE_OPENGL)
-	end = time.time()
-	print("rendering duraction")
-	print(end-start)
+for i in range(100000):
+  p.stepSimulation()
+  for i in range(width):
+    for j in range(height):
+      pixels[(i + j * width) * 3 + 0] = i
+      pixels[(i + j * width) * 3 + 1] = (j + blue) % 256
+      pixels[(i + j * width) * 3 + 2] = blue
+  blue = blue + 1
+  p.changeTexture(texUid, pixels, width, height)
+  start = time.time()
+  p.getCameraImage(300, 300, renderer=p.ER_BULLET_HARDWARE_OPENGL)
+  end = time.time()
+  print("rendering duraction")
+  print(end - start)
 p.stopStateLogging(logId)
 #p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
 #p.configureDebugVisualizer(p.COV_ENABLE_GUI,1)
-
diff --git a/examples/pybullet/examples/collisionFilter.py b/examples/pybullet/examples/collisionFilter.py
index e94b1f0f3..e2eae0a12 100644
--- a/examples/pybullet/examples/collisionFilter.py
+++ b/examples/pybullet/examples/collisionFilter.py
@@ -2,17 +2,17 @@ import pybullet as p
 import time
 p.connect(p.GUI)
 planeId = p.loadURDF("plane.urdf", useMaximalCoordinates=False)
-cubeId = p.loadURDF("cube_collisionfilter.urdf", [0,0,3], useMaximalCoordinates=False)
+cubeId = p.loadURDF("cube_collisionfilter.urdf", [0, 0, 3], useMaximalCoordinates=False)
 
 collisionFilterGroup = 0
 collisionFilterMask = 0
-p.setCollisionFilterGroupMask(cubeId,-1,collisionFilterGroup,collisionFilterMask)
+p.setCollisionFilterGroupMask(cubeId, -1, collisionFilterGroup, collisionFilterMask)
 
 enableCollision = 1
-p.setCollisionFilterPair(planeId, cubeId,-1,-1,enableCollision )
+p.setCollisionFilterPair(planeId, cubeId, -1, -1, enableCollision)
 
 p.setRealTimeSimulation(1)
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 while (p.isConnected()):
-	time.sleep(1./240.)
-	p.setGravity(0,0,-10)
+  time.sleep(1. / 240.)
+  p.setGravity(0, 0, -10)
diff --git a/examples/pybullet/examples/commandLogAndPlayback.py b/examples/pybullet/examples/commandLogAndPlayback.py
index 6cacda6b3..94f1848cb 100644
--- a/examples/pybullet/examples/commandLogAndPlayback.py
+++ b/examples/pybullet/examples/commandLogAndPlayback.py
@@ -2,14 +2,13 @@ import pybullet as p
 import time
 
 p.connect(p.GUI)
-logId = p.startStateLogging(p.STATE_LOGGING_ALL_COMMANDS,"commandLog.bin")
+logId = p.startStateLogging(p.STATE_LOGGING_ALL_COMMANDS, "commandLog.bin")
 p.loadURDF("plane.urdf")
-p.loadURDF("r2d2.urdf",[0,0,1])
+p.loadURDF("r2d2.urdf", [0, 0, 1])
 
 p.stopStateLogging(logId)
-p.resetSimulation();
-
-logId = p.startStateLogging(p.STATE_REPLAY_ALL_COMMANDS,"commandLog.bin")
-while(p.isConnected()):
-	time.sleep(1./240.)
+p.resetSimulation()
 
+logId = p.startStateLogging(p.STATE_REPLAY_ALL_COMMANDS, "commandLog.bin")
+while (p.isConnected()):
+  time.sleep(1. / 240.)
diff --git a/examples/pybullet/examples/constraint.py b/examples/pybullet/examples/constraint.py
index 5baf6952a..0d8dc0e3c 100644
--- a/examples/pybullet/examples/constraint.py
+++ b/examples/pybullet/examples/constraint.py
@@ -5,22 +5,22 @@ import math
 p.connect(p.GUI)
 
 p.loadURDF("plane.urdf")
-cubeId = p.loadURDF("cube_small.urdf",0,0,1)
-p.setGravity(0,0,-10)
+cubeId = p.loadURDF("cube_small.urdf", 0, 0, 1)
+p.setGravity(0, 0, -10)
 p.setRealTimeSimulation(1)
-cid = p.createConstraint(cubeId,-1,-1,-1,p.JOINT_FIXED,[0,0,0],[0,0,0],[0,0,1])
-print (cid)
-print (p.getConstraintUniqueId(0))
-prev=[0,0,1]
-a=-math.pi
+cid = p.createConstraint(cubeId, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0], [0, 0, 1])
+print(cid)
+print(p.getConstraintUniqueId(0))
+prev = [0, 0, 1]
+a = -math.pi
 while 1:
-	a=a+0.01
-	if (a>math.pi):
-		a=-math.pi
-	time.sleep(.01)
-	p.setGravity(0,0,-10)
-	pivot=[a,0,1]
-	orn = p.getQuaternionFromEuler([a,0,0])
-	p.changeConstraint(cid,pivot,jointChildFrameOrientation=orn, maxForce=50)
+  a = a + 0.01
+  if (a > math.pi):
+    a = -math.pi
+  time.sleep(.01)
+  p.setGravity(0, 0, -10)
+  pivot = [a, 0, 1]
+  orn = p.getQuaternionFromEuler([a, 0, 0])
+  p.changeConstraint(cid, pivot, jointChildFrameOrientation=orn, maxForce=50)
 
 p.removeConstraint(cid)
diff --git a/examples/pybullet/examples/contactFriction.py b/examples/pybullet/examples/contactFriction.py
index 44a90ecfb..334e5ab71 100644
--- a/examples/pybullet/examples/contactFriction.py
+++ b/examples/pybullet/examples/contactFriction.py
@@ -1,34 +1,29 @@
 import pybullet as p
 p.connect(p.GUI)
 useMaximalCoordinates = False
-p.loadURDF("plane.urdf", useMaximalCoordinates=useMaximalCoordinates )
+p.loadURDF("plane.urdf", useMaximalCoordinates=useMaximalCoordinates)
 #p.loadURDF("sphere2.urdf",[0,0,1])
-p.loadURDF("cube.urdf",[0,0,1], useMaximalCoordinates=useMaximalCoordinates )
-p.setGravity(0,3,-10)
-while(1):
-	p.stepSimulation()
-	pts = p.getContactPoints()
-	
-	print("num pts=",len(pts))
-	totalNormalForce = 0
-	totalFrictionForce = [0,0,0]
-	totalLateralFrictionForce=[0,0,0]
-	for pt in pts:
-		#print("pt.normal=",pt[7])
-		#print("pt.normalForce=",pt[9])
-		totalNormalForce += pt[9]
-		#print("pt.lateralFrictionA=",pt[10])
-		#print("pt.lateralFrictionADir=",pt[11])
-		#print("pt.lateralFrictionB=",pt[12])
-		#print("pt.lateralFrictionBDir=",pt[13])
-		totalLateralFrictionForce[0]+=pt[11][0]*pt[10]+pt[13][0]*pt[12]
-		totalLateralFrictionForce[1]+=pt[11][1]*pt[10]+pt[13][1]*pt[12]
-		totalLateralFrictionForce[2]+=pt[11][2]*pt[10]+pt[13][2]*pt[12]
+p.loadURDF("cube.urdf", [0, 0, 1], useMaximalCoordinates=useMaximalCoordinates)
+p.setGravity(0, 3, -10)
+while (1):
+  p.stepSimulation()
+  pts = p.getContactPoints()
 
-	
-	print("totalNormalForce=",totalNormalForce)
-	print("totalLateralFrictionForce=",totalLateralFrictionForce)
-	
-	
-		
-		
\ No newline at end of file
+  print("num pts=", len(pts))
+  totalNormalForce = 0
+  totalFrictionForce = [0, 0, 0]
+  totalLateralFrictionForce = [0, 0, 0]
+  for pt in pts:
+    #print("pt.normal=",pt[7])
+    #print("pt.normalForce=",pt[9])
+    totalNormalForce += pt[9]
+    #print("pt.lateralFrictionA=",pt[10])
+    #print("pt.lateralFrictionADir=",pt[11])
+    #print("pt.lateralFrictionB=",pt[12])
+    #print("pt.lateralFrictionBDir=",pt[13])
+    totalLateralFrictionForce[0] += pt[11][0] * pt[10] + pt[13][0] * pt[12]
+    totalLateralFrictionForce[1] += pt[11][1] * pt[10] + pt[13][1] * pt[12]
+    totalLateralFrictionForce[2] += pt[11][2] * pt[10] + pt[13][2] * pt[12]
+
+  print("totalNormalForce=", totalNormalForce)
+  print("totalLateralFrictionForce=", totalLateralFrictionForce)
diff --git a/examples/pybullet/examples/createMesh.py b/examples/pybullet/examples/createMesh.py
index 385312edb..6bbb09aa3 100644
--- a/examples/pybullet/examples/createMesh.py
+++ b/examples/pybullet/examples/createMesh.py
@@ -8,114 +8,129 @@ p.resetSimulation()
 #p.createCollisionShape(p.GEOM_PLANE)
 #p.createMultiBody(0,0)
 #p.resetDebugVisualizerCamera(5,75,-26,[0,0,1]);
-p.resetDebugVisualizerCamera(15,-346,-16,[-15,0,1]);
-
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)
+p.resetDebugVisualizerCamera(15, -346, -16, [-15, 0, 1])
 
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
 
 sphereRadius = 0.05
-colSphereId = p.createCollisionShape(p.GEOM_SPHERE,radius=sphereRadius)
+colSphereId = p.createCollisionShape(p.GEOM_SPHERE, radius=sphereRadius)
 
 #a few different ways to create a mesh:
 
-vertices=[ [-0.246350,-0.246483,-0.000624],
-[-0.151407, -0.176325, 0.172867],
-[ -0.246350, 0.249205, -0.000624],
-[ -0.151407, 0.129477, 0.172867],
-[ 0.249338, -0.246483, -0.000624],
-[ 0.154395, -0.176325, 0.172867],
-[ 0.249338, 0.249205, -0.000624],
-[ 0.154395, 0.129477, 0.172867]
+vertices = [[-0.246350, -0.246483, -0.000624], [-0.151407, -0.176325, 0.172867],
+            [-0.246350, 0.249205, -0.000624], [-0.151407, 0.129477, 0.172867],
+            [0.249338, -0.246483, -0.000624], [0.154395, -0.176325, 0.172867],
+            [0.249338, 0.249205, -0.000624], [0.154395, 0.129477, 0.172867]]
+indices = [
+    0, 3, 2, 3, 6, 2, 7, 4, 6, 5, 0, 4, 6, 0, 2, 3, 5, 7, 0, 1, 3, 3, 7, 6, 7, 5, 4, 5, 1, 0, 6, 4,
+    0, 3, 1, 5
 ]
-indices=[0,3,2,
-3,6,2,
-7,4,6,
-5,0,4,
-6,0,2,
-3,5,7,
-0,1,3,
-3,7,6,
-7,5,4,
-5,1,0,
-6,4,0,
-3,1,5]
 #convex mesh from obj
-stoneId = p.createCollisionShape(p.GEOM_MESH,vertices=vertices,indices=indices)
-
-
+stoneId = p.createCollisionShape(p.GEOM_MESH, vertices=vertices, indices=indices)
 
 boxHalfLength = 0.5
 boxHalfWidth = 2.5
 boxHalfHeight = 0.1
 segmentLength = 5
 
-colBoxId = p.createCollisionShape(p.GEOM_BOX,halfExtents=[boxHalfLength,boxHalfWidth,boxHalfHeight])
+colBoxId = p.createCollisionShape(p.GEOM_BOX,
+                                  halfExtents=[boxHalfLength, boxHalfWidth, boxHalfHeight])
 
 mass = 1
 visualShapeId = -1
 
 segmentStart = 0
 
-for i in range (segmentLength):
-	p.createMultiBody(baseMass=0,baseCollisionShapeIndex = colBoxId,basePosition = [segmentStart,0,-0.1])
-	segmentStart=segmentStart-1
-	
-for i in range (segmentLength):
-	height = 0
-	if (i%2):
-		height=1
-	p.createMultiBody(baseMass=0,baseCollisionShapeIndex = colBoxId,basePosition = [segmentStart,0,-0.1+height])
-	segmentStart=segmentStart-1
-
-baseOrientation = p.getQuaternionFromEuler([math.pi/2.,0,math.pi/2.])
-
-for i in range (segmentLength):
-	p.createMultiBody(baseMass=0,baseCollisionShapeIndex = colBoxId,basePosition = [segmentStart,0,-0.1])
-	segmentStart=segmentStart-1
-	if (i%2):
-		p.createMultiBody(baseMass=0,baseCollisionShapeIndex = colBoxId,basePosition = [segmentStart,i%3,-0.1+height+boxHalfWidth],baseOrientation=baseOrientation)
-
-for i in range (segmentLength):
-	p.createMultiBody(baseMass=0,baseCollisionShapeIndex = colBoxId,basePosition = [segmentStart,0,-0.1])
-	width=4
-	for j in range (width):
-		p.createMultiBody(baseMass=0,baseCollisionShapeIndex = stoneId,basePosition = [segmentStart,0.5*(i%2)+j-width/2.,0])
-	segmentStart=segmentStart-1
-
-
-link_Masses=[1]
-linkCollisionShapeIndices=[colBoxId]
-linkVisualShapeIndices=[-1]
-linkPositions=[[0,0,0]]
-linkOrientations=[[0,0,0,1]]
-linkInertialFramePositions=[[0,0,0]]
-linkInertialFrameOrientations=[[0,0,0,1]]
-indices=[0]
-jointTypes=[p.JOINT_REVOLUTE]
-axis=[[1,0,0]]
-
-baseOrientation = [0,0,0,1]
-for i in range (segmentLength):
-	boxId = p.createMultiBody(0,colSphereId,-1,[segmentStart,0,-0.1],baseOrientation,linkMasses=link_Masses,linkCollisionShapeIndices=linkCollisionShapeIndices,linkVisualShapeIndices=linkVisualShapeIndices,linkPositions=linkPositions,linkOrientations=linkOrientations,linkInertialFramePositions=linkInertialFramePositions, linkInertialFrameOrientations=linkInertialFrameOrientations,linkParentIndices=indices,linkJointTypes=jointTypes,linkJointAxis=axis)			
-	p.changeDynamics(boxId,-1,spinningFriction=0.001, rollingFriction=0.001,linearDamping=0.0)
-	print(p.getNumJoints(boxId))
-	for joint in range (p.getNumJoints(boxId)):
-		targetVelocity = 10
-		if (i%2):
-			targetVelocity =-10
-		p.setJointMotorControl2(boxId,joint,p.VELOCITY_CONTROL,targetVelocity=targetVelocity,force=100)
-	segmentStart=segmentStart-1.1
-
-
-
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
+for i in range(segmentLength):
+  p.createMultiBody(baseMass=0,
+                    baseCollisionShapeIndex=colBoxId,
+                    basePosition=[segmentStart, 0, -0.1])
+  segmentStart = segmentStart - 1
+
+for i in range(segmentLength):
+  height = 0
+  if (i % 2):
+    height = 1
+  p.createMultiBody(baseMass=0,
+                    baseCollisionShapeIndex=colBoxId,
+                    basePosition=[segmentStart, 0, -0.1 + height])
+  segmentStart = segmentStart - 1
+
+baseOrientation = p.getQuaternionFromEuler([math.pi / 2., 0, math.pi / 2.])
+
+for i in range(segmentLength):
+  p.createMultiBody(baseMass=0,
+                    baseCollisionShapeIndex=colBoxId,
+                    basePosition=[segmentStart, 0, -0.1])
+  segmentStart = segmentStart - 1
+  if (i % 2):
+    p.createMultiBody(baseMass=0,
+                      baseCollisionShapeIndex=colBoxId,
+                      basePosition=[segmentStart, i % 3, -0.1 + height + boxHalfWidth],
+                      baseOrientation=baseOrientation)
+
+for i in range(segmentLength):
+  p.createMultiBody(baseMass=0,
+                    baseCollisionShapeIndex=colBoxId,
+                    basePosition=[segmentStart, 0, -0.1])
+  width = 4
+  for j in range(width):
+    p.createMultiBody(baseMass=0,
+                      baseCollisionShapeIndex=stoneId,
+                      basePosition=[segmentStart, 0.5 * (i % 2) + j - width / 2., 0])
+  segmentStart = segmentStart - 1
+
+link_Masses = [1]
+linkCollisionShapeIndices = [colBoxId]
+linkVisualShapeIndices = [-1]
+linkPositions = [[0, 0, 0]]
+linkOrientations = [[0, 0, 0, 1]]
+linkInertialFramePositions = [[0, 0, 0]]
+linkInertialFrameOrientations = [[0, 0, 0, 1]]
+indices = [0]
+jointTypes = [p.JOINT_REVOLUTE]
+axis = [[1, 0, 0]]
+
+baseOrientation = [0, 0, 0, 1]
+for i in range(segmentLength):
+  boxId = p.createMultiBody(0,
+                            colSphereId,
+                            -1, [segmentStart, 0, -0.1],
+                            baseOrientation,
+                            linkMasses=link_Masses,
+                            linkCollisionShapeIndices=linkCollisionShapeIndices,
+                            linkVisualShapeIndices=linkVisualShapeIndices,
+                            linkPositions=linkPositions,
+                            linkOrientations=linkOrientations,
+                            linkInertialFramePositions=linkInertialFramePositions,
+                            linkInertialFrameOrientations=linkInertialFrameOrientations,
+                            linkParentIndices=indices,
+                            linkJointTypes=jointTypes,
+                            linkJointAxis=axis)
+  p.changeDynamics(boxId, -1, spinningFriction=0.001, rollingFriction=0.001, linearDamping=0.0)
+  print(p.getNumJoints(boxId))
+  for joint in range(p.getNumJoints(boxId)):
+    targetVelocity = 10
+    if (i % 2):
+      targetVelocity = -10
+    p.setJointMotorControl2(boxId,
+                            joint,
+                            p.VELOCITY_CONTROL,
+                            targetVelocity=targetVelocity,
+                            force=100)
+  segmentStart = segmentStart - 1.1
+
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
 while (1):
-	camData = p.getDebugVisualizerCamera()
-	viewMat = camData[2]
-	projMat = camData[3]
-	p.getCameraImage(256,256,viewMatrix=viewMat, projectionMatrix=projMat, renderer=p.ER_BULLET_HARDWARE_OPENGL)
-	keys = p.getKeyboardEvents()
-	p.stepSimulation()	
-	#print(keys)
-	time.sleep(0.01)
-	
+  camData = p.getDebugVisualizerCamera()
+  viewMat = camData[2]
+  projMat = camData[3]
+  p.getCameraImage(256,
+                   256,
+                   viewMatrix=viewMat,
+                   projectionMatrix=projMat,
+                   renderer=p.ER_BULLET_HARDWARE_OPENGL)
+  keys = p.getKeyboardEvents()
+  p.stepSimulation()
+  #print(keys)
+  time.sleep(0.01)
diff --git a/examples/pybullet/examples/createMultiBodyBatch.py b/examples/pybullet/examples/createMultiBodyBatch.py
index dd6fdc38b..3d9597387 100644
--- a/examples/pybullet/examples/createMultiBodyBatch.py
+++ b/examples/pybullet/examples/createMultiBodyBatch.py
@@ -3,145 +3,140 @@ import time
 import math
 
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	p.connect(p.GUI, options="--minGraphicsUpdateTimeMs=16000")
-	
-	
+if (cid < 0):
+  p.connect(p.GUI, options="--minGraphicsUpdateTimeMs=16000")
+
 p.setPhysicsEngineParameter(numSolverIterations=4, minimumSolverIslandSize=1024)
-p.setTimeStep(1./120.)
+p.setTimeStep(1. / 120.)
 logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "createMultiBodyBatch.json")
 #useMaximalCoordinates is much faster then the default reduced coordinates (Featherstone)
 p.loadURDF("plane100.urdf", useMaximalCoordinates=True)
 #disable rendering during creation.
 p.setPhysicsEngineParameter(contactBreakingThreshold=0.04)
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)
-p.configureDebugVisualizer(p.COV_ENABLE_GUI,0)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
+p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0)
 #disable tinyrenderer, software (CPU) renderer, we don't use it here
-p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER,0)
-
-shift = [0,-0.02,0]
-meshScale=[0.1,0.1,0.1]
-
-vertices=[
-[-1.000000,-1.000000,1.000000],
-[1.000000,-1.000000,1.000000],
-[1.000000,1.000000,1.000000],
-[-1.000000,1.000000,1.000000],
-[-1.000000,-1.000000,-1.000000],
-[1.000000,-1.000000,-1.000000],
-[1.000000,1.000000,-1.000000],
-[-1.000000,1.000000,-1.000000],
-[-1.000000,-1.000000,-1.000000],
-[-1.000000,1.000000,-1.000000],
-[-1.000000,1.000000,1.000000],
-[-1.000000,-1.000000,1.000000],
-[1.000000,-1.000000,-1.000000],
-[1.000000,1.000000,-1.000000],
-[1.000000,1.000000,1.000000],
-[1.000000,-1.000000,1.000000],
-[-1.000000,-1.000000,-1.000000],
-[-1.000000,-1.000000,1.000000],
-[1.000000,-1.000000,1.000000],
-[1.000000,-1.000000,-1.000000],
-[-1.000000,1.000000,-1.000000],
-[-1.000000,1.000000,1.000000],
-[1.000000,1.000000,1.000000],
-[1.000000,1.000000,-1.000000]
+p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER, 0)
+
+shift = [0, -0.02, 0]
+meshScale = [0.1, 0.1, 0.1]
+
+vertices = [[-1.000000, -1.000000, 1.000000], [1.000000, -1.000000, 1.000000],
+            [1.000000, 1.000000, 1.000000], [-1.000000, 1.000000, 1.000000],
+            [-1.000000, -1.000000, -1.000000], [1.000000, -1.000000, -1.000000],
+            [1.000000, 1.000000, -1.000000], [-1.000000, 1.000000, -1.000000],
+            [-1.000000, -1.000000, -1.000000], [-1.000000, 1.000000, -1.000000],
+            [-1.000000, 1.000000, 1.000000], [-1.000000, -1.000000, 1.000000],
+            [1.000000, -1.000000, -1.000000], [1.000000, 1.000000, -1.000000],
+            [1.000000, 1.000000, 1.000000], [1.000000, -1.000000, 1.000000],
+            [-1.000000, -1.000000, -1.000000], [-1.000000, -1.000000, 1.000000],
+            [1.000000, -1.000000, 1.000000], [1.000000, -1.000000, -1.000000],
+            [-1.000000, 1.000000, -1.000000], [-1.000000, 1.000000, 1.000000],
+            [1.000000, 1.000000, 1.000000], [1.000000, 1.000000, -1.000000]]
+
+normals = [[0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 1.000000],
+           [0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 1.000000],
+           [0.000000, 0.000000, -1.000000], [0.000000, 0.000000, -1.000000],
+           [0.000000, 0.000000, -1.000000], [0.000000, 0.000000, -1.000000],
+           [-1.000000, 0.000000, 0.000000], [-1.000000, 0.000000, 0.000000],
+           [-1.000000, 0.000000, 0.000000], [-1.000000, 0.000000, 0.000000],
+           [1.000000, 0.000000, 0.000000], [1.000000, 0.000000, 0.000000],
+           [1.000000, 0.000000, 0.000000], [1.000000, 0.000000, 0.000000],
+           [0.000000, -1.000000, 0.000000], [0.000000, -1.000000, 0.000000],
+           [0.000000, -1.000000, 0.000000], [0.000000, -1.000000, 0.000000],
+           [0.000000, 1.000000, 0.000000], [0.000000, 1.000000, 0.000000],
+           [0.000000, 1.000000, 0.000000], [0.000000, 1.000000, 0.000000]]
+
+uvs = [[0.750000, 0.250000], [1.000000, 0.250000], [1.000000, 0.000000], [0.750000, 0.000000],
+       [0.500000, 0.250000], [0.250000, 0.250000], [0.250000, 0.000000], [0.500000, 0.000000],
+       [0.500000, 0.000000], [0.750000, 0.000000], [0.750000, 0.250000], [0.500000, 0.250000],
+       [0.250000, 0.500000], [0.250000, 0.250000], [0.000000, 0.250000], [0.000000, 0.500000],
+       [0.250000, 0.500000], [0.250000, 0.250000], [0.500000, 0.250000], [0.500000, 0.500000],
+       [0.000000, 0.000000], [0.000000, 0.250000], [0.250000, 0.250000], [0.250000, 0.000000]]
+indices = [
+    0,
+    1,
+    2,
+    0,
+    2,
+    3,  #//ground face
+    6,
+    5,
+    4,
+    7,
+    6,
+    4,  #//top face
+    10,
+    9,
+    8,
+    11,
+    10,
+    8,
+    12,
+    13,
+    14,
+    12,
+    14,
+    15,
+    18,
+    17,
+    16,
+    19,
+    18,
+    16,
+    20,
+    21,
+    22,
+    20,
+    22,
+    23
 ]
 
-normals=[
-[0.000000,0.000000,1.000000],
-[0.000000,0.000000,1.000000],
-[0.000000,0.000000,1.000000],
-[0.000000,0.000000,1.000000],
-[0.000000,0.000000,-1.000000],
-[0.000000,0.000000,-1.000000],
-[0.000000,0.000000,-1.000000],
-[0.000000,0.000000,-1.000000],
-[-1.000000,0.000000,0.000000],
-[-1.000000,0.000000,0.000000],
-[-1.000000,0.000000,0.000000],
-[-1.000000,0.000000,0.000000],
-[1.000000,0.000000,0.000000],
-[1.000000,0.000000,0.000000],
-[1.000000,0.000000,0.000000],
-[1.000000,0.000000,0.000000],
-[0.000000,-1.000000,0.000000],
-[0.000000,-1.000000,0.000000],
-[0.000000,-1.000000,0.000000],
-[0.000000,-1.000000,0.000000],
-[0.000000,1.000000,0.000000],
-[0.000000,1.000000,0.000000],
-[0.000000,1.000000,0.000000],
-[0.000000,1.000000,0.000000]
-]
-
-uvs=[
-[0.750000,0.250000],
-[1.000000,0.250000],
-[1.000000,0.000000],
-[0.750000,0.000000],
-[0.500000,0.250000],
-[0.250000,0.250000],
-[0.250000,0.000000],
-[0.500000,0.000000],
-[0.500000,0.000000],
-[0.750000,0.000000],
-[0.750000,0.250000],
-[0.500000,0.250000],
-[0.250000,0.500000],
-[0.250000,0.250000],
-[0.000000,0.250000],
-[0.000000,0.500000],
-[0.250000,0.500000],
-[0.250000,0.250000],
-[0.500000,0.250000],
-[0.500000,0.500000],
-[0.000000,0.000000],
-[0.000000,0.250000],
-[0.250000,0.250000],
-[0.250000,0.000000]
-]
-indices=[	0, 1, 2, 0, 2, 3,  #//ground face
-		6, 5, 4, 7, 6, 4,  #//top face
-		10, 9, 8, 11, 10, 8,
-		12, 13, 14, 12, 14, 15,
-		18, 17, 16, 19, 18, 16,
-		20, 21, 22, 20, 22, 23]
-
 #p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER,0)
 #the visual shape and collision shape can be re-used by all createMultiBody instances (instancing)
-visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,rgbaColor=[1,1,1,1], specularColor=[0.4,.4,0], visualFramePosition=shift, meshScale=meshScale, vertices=vertices, indices=indices, uvs=uvs, normals=normals)
-collisionShapeId = p.createCollisionShape(shapeType=p.GEOM_BOX, halfExtents=meshScale)#MESH, vertices=vertices, collisionFramePosition=shift,meshScale=meshScale)
+visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,
+                                    rgbaColor=[1, 1, 1, 1],
+                                    specularColor=[0.4, .4, 0],
+                                    visualFramePosition=shift,
+                                    meshScale=meshScale,
+                                    vertices=vertices,
+                                    indices=indices,
+                                    uvs=uvs,
+                                    normals=normals)
+collisionShapeId = p.createCollisionShape(
+    shapeType=p.GEOM_BOX, halfExtents=meshScale
+)  #MESH, vertices=vertices, collisionFramePosition=shift,meshScale=meshScale)
 
 texUid = p.loadTexture("tex256.png")
 
+batchPositions = []
 
-batchPositions=[]
-
-
+for x in range(32):
+  for y in range(32):
+    for z in range(10):
+      batchPositions.append(
+          [x * meshScale[0] * 5.5, y * meshScale[1] * 5.5, (0.5 + z) * meshScale[2] * 2.5])
 
-for x in range (32):
-  for y in range (32):
-    for z in range (10):
-      batchPositions.append([x*meshScale[0]*5.5,y*meshScale[1]*5.5,(0.5+z)*meshScale[2]*2.5])
-
-bodyUid = p.createMultiBody(baseMass=0,baseInertialFramePosition=[0,0,0],baseCollisionShapeIndex=collisionShapeId, baseVisualShapeIndex = visualShapeId, basePosition =[0,0,2], batchPositions=batchPositions,useMaximalCoordinates=True)
-p.changeVisualShape(bodyUid,-1, textureUniqueId = texUid)		
+bodyUid = p.createMultiBody(baseMass=0,
+                            baseInertialFramePosition=[0, 0, 0],
+                            baseCollisionShapeIndex=collisionShapeId,
+                            baseVisualShapeIndex=visualShapeId,
+                            basePosition=[0, 0, 2],
+                            batchPositions=batchPositions,
+                            useMaximalCoordinates=True)
+p.changeVisualShape(bodyUid, -1, textureUniqueId=texUid)
 
 p.syncBodyInfo()
-print("numBodies=",p.getNumBodies())
+print("numBodies=", p.getNumBodies())
 p.stopStateLogging(logId)
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
 
-colors = [[1,0,0,1],[0,1,0,1],[0,0,1,1],[1,1,1,1]]
+colors = [[1, 0, 0, 1], [0, 1, 0, 1], [0, 0, 1, 1], [1, 1, 1, 1]]
 currentColor = 0
 
-
 while (1):
   p.stepSimulation()
   #time.sleep(1./120.)
   #p.getCameraImage(320,200)
-  
\ No newline at end of file
diff --git a/examples/pybullet/examples/createMultiBodyLinks.py b/examples/pybullet/examples/createMultiBodyLinks.py
index bdb7f6739..292c887f8 100644
--- a/examples/pybullet/examples/createMultiBodyLinks.py
+++ b/examples/pybullet/examples/createMultiBodyLinks.py
@@ -1,56 +1,73 @@
 import pybullet as p
 import time
 
-
 p.connect(p.GUI)
 p.createCollisionShape(p.GEOM_PLANE)
-p.createMultiBody(0,0)
+p.createMultiBody(0, 0)
 
 sphereRadius = 0.05
-colSphereId = p.createCollisionShape(p.GEOM_SPHERE,radius=sphereRadius)
-colBoxId = p.createCollisionShape(p.GEOM_BOX,halfExtents=[sphereRadius,sphereRadius,sphereRadius])
+colSphereId = p.createCollisionShape(p.GEOM_SPHERE, radius=sphereRadius)
+colBoxId = p.createCollisionShape(p.GEOM_BOX,
+                                  halfExtents=[sphereRadius, sphereRadius, sphereRadius])
 
 mass = 1
 visualShapeId = -1
 
-	
-	
-link_Masses=[1]
-linkCollisionShapeIndices=[colBoxId]
-linkVisualShapeIndices=[-1]
-linkPositions=[[0,0,0.11]]
-linkOrientations=[[0,0,0,1]]
-linkInertialFramePositions=[[0,0,0]]
-linkInertialFrameOrientations=[[0,0,0,1]]
-indices=[0]
-jointTypes=[p.JOINT_REVOLUTE]
-axis=[[0,0,1]]
-
-for i in range (3):
-	for j in range (3):
-		for k in range (3):
-			basePosition = [1+i*5*sphereRadius,1+j*5*sphereRadius,1+k*5*sphereRadius+1]
-			baseOrientation = [0,0,0,1]
-			if (k&2):
-				sphereUid = p.createMultiBody(mass,colSphereId,visualShapeId,basePosition,baseOrientation)
-			else:
-				sphereUid = p.createMultiBody(mass,colBoxId,visualShapeId,basePosition,baseOrientation,linkMasses=link_Masses,linkCollisionShapeIndices=linkCollisionShapeIndices,linkVisualShapeIndices=linkVisualShapeIndices,linkPositions=linkPositions,linkOrientations=linkOrientations,linkInertialFramePositions=linkInertialFramePositions, linkInertialFrameOrientations=linkInertialFrameOrientations,linkParentIndices=indices,linkJointTypes=jointTypes,linkJointAxis=axis)			
-			
-			p.changeDynamics(sphereUid,-1,spinningFriction=0.001, rollingFriction=0.001,linearDamping=0.0)
-			for joint in range (p.getNumJoints(sphereUid)):
-				p.setJointMotorControl2(sphereUid,joint,p.VELOCITY_CONTROL,targetVelocity=1,force=10)
-
-
-p.setGravity(0,0,-10)
+link_Masses = [1]
+linkCollisionShapeIndices = [colBoxId]
+linkVisualShapeIndices = [-1]
+linkPositions = [[0, 0, 0.11]]
+linkOrientations = [[0, 0, 0, 1]]
+linkInertialFramePositions = [[0, 0, 0]]
+linkInertialFrameOrientations = [[0, 0, 0, 1]]
+indices = [0]
+jointTypes = [p.JOINT_REVOLUTE]
+axis = [[0, 0, 1]]
+
+for i in range(3):
+  for j in range(3):
+    for k in range(3):
+      basePosition = [
+          1 + i * 5 * sphereRadius, 1 + j * 5 * sphereRadius, 1 + k * 5 * sphereRadius + 1
+      ]
+      baseOrientation = [0, 0, 0, 1]
+      if (k & 2):
+        sphereUid = p.createMultiBody(mass, colSphereId, visualShapeId, basePosition,
+                                      baseOrientation)
+      else:
+        sphereUid = p.createMultiBody(mass,
+                                      colBoxId,
+                                      visualShapeId,
+                                      basePosition,
+                                      baseOrientation,
+                                      linkMasses=link_Masses,
+                                      linkCollisionShapeIndices=linkCollisionShapeIndices,
+                                      linkVisualShapeIndices=linkVisualShapeIndices,
+                                      linkPositions=linkPositions,
+                                      linkOrientations=linkOrientations,
+                                      linkInertialFramePositions=linkInertialFramePositions,
+                                      linkInertialFrameOrientations=linkInertialFrameOrientations,
+                                      linkParentIndices=indices,
+                                      linkJointTypes=jointTypes,
+                                      linkJointAxis=axis)
+
+      p.changeDynamics(sphereUid,
+                       -1,
+                       spinningFriction=0.001,
+                       rollingFriction=0.001,
+                       linearDamping=0.0)
+      for joint in range(p.getNumJoints(sphereUid)):
+        p.setJointMotorControl2(sphereUid, joint, p.VELOCITY_CONTROL, targetVelocity=1, force=10)
+
+p.setGravity(0, 0, -10)
 p.setRealTimeSimulation(1)
 
 p.getNumJoints(sphereUid)
-for i in range (p.getNumJoints(sphereUid)):
-	p.getJointInfo(sphereUid,i)
-	
+for i in range(p.getNumJoints(sphereUid)):
+  p.getJointInfo(sphereUid, i)
+
 while (1):
-	keys = p.getKeyboardEvents()
-	print(keys)
+  keys = p.getKeyboardEvents()
+  print(keys)
 
-	time.sleep(0.01)
-	
\ No newline at end of file
+  time.sleep(0.01)
diff --git a/examples/pybullet/examples/createObstacleCourse.py b/examples/pybullet/examples/createObstacleCourse.py
index ef145c72c..2bfc45093 100644
--- a/examples/pybullet/examples/createObstacleCourse.py
+++ b/examples/pybullet/examples/createObstacleCourse.py
@@ -8,93 +8,121 @@ p.resetSimulation()
 #p.createCollisionShape(p.GEOM_PLANE)

 #p.createMultiBody(0,0)

 #p.resetDebugVisualizerCamera(5,75,-26,[0,0,1]);

-p.resetDebugVisualizerCamera(15,-346,-16,[-15,0,1]);

-

-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)

+p.resetDebugVisualizerCamera(15, -346, -16, [-15, 0, 1])

 

+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)

 

 sphereRadius = 0.05

-colSphereId = p.createCollisionShape(p.GEOM_SPHERE,radius=sphereRadius)

+colSphereId = p.createCollisionShape(p.GEOM_SPHERE, radius=sphereRadius)

 

 #a few different ways to create a mesh:

 

 #convex mesh from obj

-stoneId = p.createCollisionShape(p.GEOM_MESH,fileName="stone.obj")

-

-

+stoneId = p.createCollisionShape(p.GEOM_MESH, fileName="stone.obj")

 

 boxHalfLength = 0.5

 boxHalfWidth = 2.5

 boxHalfHeight = 0.1

 segmentLength = 5

 

-colBoxId = p.createCollisionShape(p.GEOM_BOX,halfExtents=[boxHalfLength,boxHalfWidth,boxHalfHeight])

+colBoxId = p.createCollisionShape(p.GEOM_BOX,

+                                  halfExtents=[boxHalfLength, boxHalfWidth, boxHalfHeight])

 

 mass = 1

 visualShapeId = -1

 

 segmentStart = 0

 

-for i in range (segmentLength):

-	p.createMultiBody(baseMass=0,baseCollisionShapeIndex = colBoxId,basePosition = [segmentStart,0,-0.1])

-	segmentStart=segmentStart-1

-	

-for i in range (segmentLength):

-	height = 0

-	if (i%2):

-		height=1

-	p.createMultiBody(baseMass=0,baseCollisionShapeIndex = colBoxId,basePosition = [segmentStart,0,-0.1+height])

-	segmentStart=segmentStart-1

-

-baseOrientation = p.getQuaternionFromEuler([math.pi/2.,0,math.pi/2.])

-

-for i in range (segmentLength):

-	p.createMultiBody(baseMass=0,baseCollisionShapeIndex = colBoxId,basePosition = [segmentStart,0,-0.1])

-	segmentStart=segmentStart-1

-	if (i%2):

-		p.createMultiBody(baseMass=0,baseCollisionShapeIndex = colBoxId,basePosition = [segmentStart,i%3,-0.1+height+boxHalfWidth],baseOrientation=baseOrientation)

-

-for i in range (segmentLength):

-	p.createMultiBody(baseMass=0,baseCollisionShapeIndex = colBoxId,basePosition = [segmentStart,0,-0.1])

-	width=4

-	for j in range (width):

-		p.createMultiBody(baseMass=0,baseCollisionShapeIndex = stoneId,basePosition = [segmentStart,0.5*(i%2)+j-width/2.,0])

-	segmentStart=segmentStart-1

-

-

-link_Masses=[1]

-linkCollisionShapeIndices=[colBoxId]

-linkVisualShapeIndices=[-1]

-linkPositions=[[0,0,0]]

-linkOrientations=[[0,0,0,1]]

-linkInertialFramePositions=[[0,0,0]]

-linkInertialFrameOrientations=[[0,0,0,1]]

-indices=[0]

-jointTypes=[p.JOINT_REVOLUTE]

-axis=[[1,0,0]]

-

-baseOrientation = [0,0,0,1]

-for i in range (segmentLength):

-	boxId = p.createMultiBody(0,colSphereId,-1,[segmentStart,0,-0.1],baseOrientation,linkMasses=link_Masses,linkCollisionShapeIndices=linkCollisionShapeIndices,linkVisualShapeIndices=linkVisualShapeIndices,linkPositions=linkPositions,linkOrientations=linkOrientations,linkInertialFramePositions=linkInertialFramePositions, linkInertialFrameOrientations=linkInertialFrameOrientations,linkParentIndices=indices,linkJointTypes=jointTypes,linkJointAxis=axis)			

-	p.changeDynamics(boxId,-1,spinningFriction=0.001, rollingFriction=0.001,linearDamping=0.0)

-	print(p.getNumJoints(boxId))

-	for joint in range (p.getNumJoints(boxId)):

-		targetVelocity = 10

-		if (i%2):

-			targetVelocity =-10

-		p.setJointMotorControl2(boxId,joint,p.VELOCITY_CONTROL,targetVelocity=targetVelocity,force=100)

-	segmentStart=segmentStart-1.1

-

-

-

-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)

+for i in range(segmentLength):

+  p.createMultiBody(baseMass=0,

+                    baseCollisionShapeIndex=colBoxId,

+                    basePosition=[segmentStart, 0, -0.1])

+  segmentStart = segmentStart - 1

+

+for i in range(segmentLength):

+  height = 0

+  if (i % 2):

+    height = 1

+  p.createMultiBody(baseMass=0,

+                    baseCollisionShapeIndex=colBoxId,

+                    basePosition=[segmentStart, 0, -0.1 + height])

+  segmentStart = segmentStart - 1

+

+baseOrientation = p.getQuaternionFromEuler([math.pi / 2., 0, math.pi / 2.])

+

+for i in range(segmentLength):

+  p.createMultiBody(baseMass=0,

+                    baseCollisionShapeIndex=colBoxId,

+                    basePosition=[segmentStart, 0, -0.1])

+  segmentStart = segmentStart - 1

+  if (i % 2):

+    p.createMultiBody(baseMass=0,

+                      baseCollisionShapeIndex=colBoxId,

+                      basePosition=[segmentStart, i % 3, -0.1 + height + boxHalfWidth],

+                      baseOrientation=baseOrientation)

+

+for i in range(segmentLength):

+  p.createMultiBody(baseMass=0,

+                    baseCollisionShapeIndex=colBoxId,

+                    basePosition=[segmentStart, 0, -0.1])

+  width = 4

+  for j in range(width):

+    p.createMultiBody(baseMass=0,

+                      baseCollisionShapeIndex=stoneId,

+                      basePosition=[segmentStart, 0.5 * (i % 2) + j - width / 2., 0])

+  segmentStart = segmentStart - 1

+

+link_Masses = [1]

+linkCollisionShapeIndices = [colBoxId]

+linkVisualShapeIndices = [-1]

+linkPositions = [[0, 0, 0]]

+linkOrientations = [[0, 0, 0, 1]]

+linkInertialFramePositions = [[0, 0, 0]]

+linkInertialFrameOrientations = [[0, 0, 0, 1]]

+indices = [0]

+jointTypes = [p.JOINT_REVOLUTE]

+axis = [[1, 0, 0]]

+

+baseOrientation = [0, 0, 0, 1]

+for i in range(segmentLength):

+  boxId = p.createMultiBody(0,

+                            colSphereId,

+                            -1, [segmentStart, 0, -0.1],

+                            baseOrientation,

+                            linkMasses=link_Masses,

+                            linkCollisionShapeIndices=linkCollisionShapeIndices,

+                            linkVisualShapeIndices=linkVisualShapeIndices,

+                            linkPositions=linkPositions,

+                            linkOrientations=linkOrientations,

+                            linkInertialFramePositions=linkInertialFramePositions,

+                            linkInertialFrameOrientations=linkInertialFrameOrientations,

+                            linkParentIndices=indices,

+                            linkJointTypes=jointTypes,

+                            linkJointAxis=axis)

+  p.changeDynamics(boxId, -1, spinningFriction=0.001, rollingFriction=0.001, linearDamping=0.0)

+  print(p.getNumJoints(boxId))

+  for joint in range(p.getNumJoints(boxId)):

+    targetVelocity = 10

+    if (i % 2):

+      targetVelocity = -10

+    p.setJointMotorControl2(boxId,

+                            joint,

+                            p.VELOCITY_CONTROL,

+                            targetVelocity=targetVelocity,

+                            force=100)

+  segmentStart = segmentStart - 1.1

+

+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)

 while (1):

-	camData = p.getDebugVisualizerCamera()

-	viewMat = camData[2]

-	projMat = camData[3]

-	p.getCameraImage(256,256,viewMatrix=viewMat, projectionMatrix=projMat, renderer=p.ER_BULLET_HARDWARE_OPENGL)

-	keys = p.getKeyboardEvents()

-	p.stepSimulation()	

-	#print(keys)

-	time.sleep(0.01)

-	

+  camData = p.getDebugVisualizerCamera()

+  viewMat = camData[2]

+  projMat = camData[3]

+  p.getCameraImage(256,

+                   256,

+                   viewMatrix=viewMat,

+                   projectionMatrix=projMat,

+                   renderer=p.ER_BULLET_HARDWARE_OPENGL)

+  keys = p.getKeyboardEvents()

+  p.stepSimulation()

+  #print(keys)

+  time.sleep(0.01)

diff --git a/examples/pybullet/examples/createSphereMultiBodies.py b/examples/pybullet/examples/createSphereMultiBodies.py
index f2b0fb59e..689205ca3 100644
--- a/examples/pybullet/examples/createSphereMultiBodies.py
+++ b/examples/pybullet/examples/createSphereMultiBodies.py
@@ -5,34 +5,45 @@ useMaximalCoordinates = 0
 
 p.connect(p.GUI)
 #p.loadSDF("stadium.sdf",useMaximalCoordinates=useMaximalCoordinates)
-monastryId = concaveEnv =p.createCollisionShape(p.GEOM_MESH,fileName="samurai_monastry.obj",flags=p.GEOM_FORCE_CONCAVE_TRIMESH)
-orn = p.getQuaternionFromEuler([1.5707963,0,0])
-p.createMultiBody (0,monastryId, baseOrientation=orn)
+monastryId = concaveEnv = p.createCollisionShape(p.GEOM_MESH,
+                                                 fileName="samurai_monastry.obj",
+                                                 flags=p.GEOM_FORCE_CONCAVE_TRIMESH)
+orn = p.getQuaternionFromEuler([1.5707963, 0, 0])
+p.createMultiBody(0, monastryId, baseOrientation=orn)
 
 sphereRadius = 0.05
-colSphereId = p.createCollisionShape(p.GEOM_SPHERE,radius=sphereRadius)
-colBoxId = p.createCollisionShape(p.GEOM_BOX,halfExtents=[sphereRadius,sphereRadius,sphereRadius])
+colSphereId = p.createCollisionShape(p.GEOM_SPHERE, radius=sphereRadius)
+colBoxId = p.createCollisionShape(p.GEOM_BOX,
+                                  halfExtents=[sphereRadius, sphereRadius, sphereRadius])
 
 mass = 1
 visualShapeId = -1
 
-
-for i in range (5):
-	for j in range (5):
-		for k in range (5):
-			if (k&2):
-				sphereUid = p.createMultiBody(mass,colSphereId,visualShapeId,[-i*2*sphereRadius,j*2*sphereRadius,k*2*sphereRadius+1],useMaximalCoordinates=useMaximalCoordinates)
-			else:
-				sphereUid = p.createMultiBody(mass,colBoxId,visualShapeId,[-i*2*sphereRadius,j*2*sphereRadius,k*2*sphereRadius+1], useMaximalCoordinates=useMaximalCoordinates)
-			p.changeDynamics(sphereUid,-1,spinningFriction=0.001, rollingFriction=0.001,linearDamping=0.0)
-			
-
-
-p.setGravity(0,0,-10)
+for i in range(5):
+  for j in range(5):
+    for k in range(5):
+      if (k & 2):
+        sphereUid = p.createMultiBody(
+            mass,
+            colSphereId,
+            visualShapeId, [-i * 2 * sphereRadius, j * 2 * sphereRadius, k * 2 * sphereRadius + 1],
+            useMaximalCoordinates=useMaximalCoordinates)
+      else:
+        sphereUid = p.createMultiBody(
+            mass,
+            colBoxId,
+            visualShapeId, [-i * 2 * sphereRadius, j * 2 * sphereRadius, k * 2 * sphereRadius + 1],
+            useMaximalCoordinates=useMaximalCoordinates)
+      p.changeDynamics(sphereUid,
+                       -1,
+                       spinningFriction=0.001,
+                       rollingFriction=0.001,
+                       linearDamping=0.0)
+
+p.setGravity(0, 0, -10)
 p.setRealTimeSimulation(1)
 
 while (1):
-	keys = p.getKeyboardEvents()
-	#print(keys)
-	time.sleep(0.01)
-	
\ No newline at end of file
+  keys = p.getKeyboardEvents()
+  #print(keys)
+  time.sleep(0.01)
diff --git a/examples/pybullet/examples/createTexturedMeshVisualShape.py b/examples/pybullet/examples/createTexturedMeshVisualShape.py
index 6bb7646d5..b84933741 100644
--- a/examples/pybullet/examples/createTexturedMeshVisualShape.py
+++ b/examples/pybullet/examples/createTexturedMeshVisualShape.py
@@ -2,167 +2,180 @@ import pybullet as p
 import time
 import math
 
-def getRayFromTo(mouseX,mouseY):
-	width, height, viewMat, projMat, cameraUp, camForward, horizon,vertical, _,_,dist, camTarget = p.getDebugVisualizerCamera()
-	camPos = [camTarget[0] - dist*camForward[0],camTarget[1] - dist*camForward[1],camTarget[2] - dist*camForward[2]]
-	farPlane = 10000
-	rayForward = [(camTarget[0]-camPos[0]),(camTarget[1]-camPos[1]),(camTarget[2]-camPos[2])]
-	invLen = farPlane*1./(math.sqrt(rayForward[0]*rayForward[0]+rayForward[1]*rayForward[1]+rayForward[2]*rayForward[2]))
-	rayForward = [invLen*rayForward[0],invLen*rayForward[1],invLen*rayForward[2]]
-	rayFrom = camPos
-	oneOverWidth = float(1)/float(width)
-	oneOverHeight = float(1)/float(height)
-	dHor = [horizon[0] * oneOverWidth,horizon[1] * oneOverWidth,horizon[2] * oneOverWidth]
-	dVer = [vertical[0] * oneOverHeight,vertical[1] * oneOverHeight,vertical[2] * oneOverHeight]
-	rayToCenter=[rayFrom[0]+rayForward[0],rayFrom[1]+rayForward[1],rayFrom[2]+rayForward[2]]
-	rayTo = [rayFrom[0]+rayForward[0]  - 0.5 * horizon[0] + 0.5 * vertical[0]+float(mouseX)*dHor[0]-float(mouseY)*dVer[0],
-					rayFrom[1]+rayForward[1]  - 0.5 * horizon[1] + 0.5 * vertical[1]+float(mouseX)*dHor[1]-float(mouseY)*dVer[1],
-					rayFrom[2]+rayForward[2]  - 0.5 * horizon[2] + 0.5 * vertical[2]+float(mouseX)*dHor[2]-float(mouseY)*dVer[2]]
-	return rayFrom,rayTo
+
+def getRayFromTo(mouseX, mouseY):
+  width, height, viewMat, projMat, cameraUp, camForward, horizon, vertical, _, _, dist, camTarget = p.getDebugVisualizerCamera(
+  )
+  camPos = [
+      camTarget[0] - dist * camForward[0], camTarget[1] - dist * camForward[1],
+      camTarget[2] - dist * camForward[2]
+  ]
+  farPlane = 10000
+  rayForward = [(camTarget[0] - camPos[0]), (camTarget[1] - camPos[1]), (camTarget[2] - camPos[2])]
+  invLen = farPlane * 1. / (math.sqrt(rayForward[0] * rayForward[0] + rayForward[1] *
+                                      rayForward[1] + rayForward[2] * rayForward[2]))
+  rayForward = [invLen * rayForward[0], invLen * rayForward[1], invLen * rayForward[2]]
+  rayFrom = camPos
+  oneOverWidth = float(1) / float(width)
+  oneOverHeight = float(1) / float(height)
+  dHor = [horizon[0] * oneOverWidth, horizon[1] * oneOverWidth, horizon[2] * oneOverWidth]
+  dVer = [vertical[0] * oneOverHeight, vertical[1] * oneOverHeight, vertical[2] * oneOverHeight]
+  rayToCenter = [
+      rayFrom[0] + rayForward[0], rayFrom[1] + rayForward[1], rayFrom[2] + rayForward[2]
+  ]
+  rayTo = [
+      rayFrom[0] + rayForward[0] - 0.5 * horizon[0] + 0.5 * vertical[0] + float(mouseX) * dHor[0] -
+      float(mouseY) * dVer[0], rayFrom[1] + rayForward[1] - 0.5 * horizon[1] + 0.5 * vertical[1] +
+      float(mouseX) * dHor[1] - float(mouseY) * dVer[1], rayFrom[2] + rayForward[2] -
+      0.5 * horizon[2] + 0.5 * vertical[2] + float(mouseX) * dHor[2] - float(mouseY) * dVer[2]
+  ]
+  return rayFrom, rayTo
+
 
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	p.connect(p.GUI)
+if (cid < 0):
+  p.connect(p.GUI)
 p.setPhysicsEngineParameter(numSolverIterations=10)
-p.setTimeStep(1./120.)
+p.setTimeStep(1. / 120.)
 logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "visualShapeBench.json")
 #useMaximalCoordinates is much faster then the default reduced coordinates (Featherstone)
 p.loadURDF("plane100.urdf", useMaximalCoordinates=True)
 #disable rendering during creation.
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)
-p.configureDebugVisualizer(p.COV_ENABLE_GUI,0)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
+p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0)
 #disable tinyrenderer, software (CPU) renderer, we don't use it here
-p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER,0)
+p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER, 0)
 
-shift = [0,-0.02,0]
-meshScale=[0.1,0.1,0.1]
+shift = [0, -0.02, 0]
+meshScale = [0.1, 0.1, 0.1]
 
-vertices=[
-[-1.000000,-1.000000,1.000000],
-[1.000000,-1.000000,1.000000],
-[1.000000,1.000000,1.000000],
-[-1.000000,1.000000,1.000000],
-[-1.000000,-1.000000,-1.000000],
-[1.000000,-1.000000,-1.000000],
-[1.000000,1.000000,-1.000000],
-[-1.000000,1.000000,-1.000000],
-[-1.000000,-1.000000,-1.000000],
-[-1.000000,1.000000,-1.000000],
-[-1.000000,1.000000,1.000000],
-[-1.000000,-1.000000,1.000000],
-[1.000000,-1.000000,-1.000000],
-[1.000000,1.000000,-1.000000],
-[1.000000,1.000000,1.000000],
-[1.000000,-1.000000,1.000000],
-[-1.000000,-1.000000,-1.000000],
-[-1.000000,-1.000000,1.000000],
-[1.000000,-1.000000,1.000000],
-[1.000000,-1.000000,-1.000000],
-[-1.000000,1.000000,-1.000000],
-[-1.000000,1.000000,1.000000],
-[1.000000,1.000000,1.000000],
-[1.000000,1.000000,-1.000000]
-]
+vertices = [[-1.000000, -1.000000, 1.000000], [1.000000, -1.000000, 1.000000],
+            [1.000000, 1.000000, 1.000000], [-1.000000, 1.000000, 1.000000],
+            [-1.000000, -1.000000, -1.000000], [1.000000, -1.000000, -1.000000],
+            [1.000000, 1.000000, -1.000000], [-1.000000, 1.000000, -1.000000],
+            [-1.000000, -1.000000, -1.000000], [-1.000000, 1.000000, -1.000000],
+            [-1.000000, 1.000000, 1.000000], [-1.000000, -1.000000, 1.000000],
+            [1.000000, -1.000000, -1.000000], [1.000000, 1.000000, -1.000000],
+            [1.000000, 1.000000, 1.000000], [1.000000, -1.000000, 1.000000],
+            [-1.000000, -1.000000, -1.000000], [-1.000000, -1.000000, 1.000000],
+            [1.000000, -1.000000, 1.000000], [1.000000, -1.000000, -1.000000],
+            [-1.000000, 1.000000, -1.000000], [-1.000000, 1.000000, 1.000000],
+            [1.000000, 1.000000, 1.000000], [1.000000, 1.000000, -1.000000]]
 
-normals=[
-[0.000000,0.000000,1.000000],
-[0.000000,0.000000,1.000000],
-[0.000000,0.000000,1.000000],
-[0.000000,0.000000,1.000000],
-[0.000000,0.000000,-1.000000],
-[0.000000,0.000000,-1.000000],
-[0.000000,0.000000,-1.000000],
-[0.000000,0.000000,-1.000000],
-[-1.000000,0.000000,0.000000],
-[-1.000000,0.000000,0.000000],
-[-1.000000,0.000000,0.000000],
-[-1.000000,0.000000,0.000000],
-[1.000000,0.000000,0.000000],
-[1.000000,0.000000,0.000000],
-[1.000000,0.000000,0.000000],
-[1.000000,0.000000,0.000000],
-[0.000000,-1.000000,0.000000],
-[0.000000,-1.000000,0.000000],
-[0.000000,-1.000000,0.000000],
-[0.000000,-1.000000,0.000000],
-[0.000000,1.000000,0.000000],
-[0.000000,1.000000,0.000000],
-[0.000000,1.000000,0.000000],
-[0.000000,1.000000,0.000000]
-]
+normals = [[0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 1.000000],
+           [0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 1.000000],
+           [0.000000, 0.000000, -1.000000], [0.000000, 0.000000, -1.000000],
+           [0.000000, 0.000000, -1.000000], [0.000000, 0.000000, -1.000000],
+           [-1.000000, 0.000000, 0.000000], [-1.000000, 0.000000, 0.000000],
+           [-1.000000, 0.000000, 0.000000], [-1.000000, 0.000000, 0.000000],
+           [1.000000, 0.000000, 0.000000], [1.000000, 0.000000, 0.000000],
+           [1.000000, 0.000000, 0.000000], [1.000000, 0.000000, 0.000000],
+           [0.000000, -1.000000, 0.000000], [0.000000, -1.000000, 0.000000],
+           [0.000000, -1.000000, 0.000000], [0.000000, -1.000000, 0.000000],
+           [0.000000, 1.000000, 0.000000], [0.000000, 1.000000, 0.000000],
+           [0.000000, 1.000000, 0.000000], [0.000000, 1.000000, 0.000000]]
 
-uvs=[
-[0.750000,0.250000],
-[1.000000,0.250000],
-[1.000000,0.000000],
-[0.750000,0.000000],
-[0.500000,0.250000],
-[0.250000,0.250000],
-[0.250000,0.000000],
-[0.500000,0.000000],
-[0.500000,0.000000],
-[0.750000,0.000000],
-[0.750000,0.250000],
-[0.500000,0.250000],
-[0.250000,0.500000],
-[0.250000,0.250000],
-[0.000000,0.250000],
-[0.000000,0.500000],
-[0.250000,0.500000],
-[0.250000,0.250000],
-[0.500000,0.250000],
-[0.500000,0.500000],
-[0.000000,0.000000],
-[0.000000,0.250000],
-[0.250000,0.250000],
-[0.250000,0.000000]
+uvs = [[0.750000, 0.250000], [1.000000, 0.250000], [1.000000, 0.000000], [0.750000, 0.000000],
+       [0.500000, 0.250000], [0.250000, 0.250000], [0.250000, 0.000000], [0.500000, 0.000000],
+       [0.500000, 0.000000], [0.750000, 0.000000], [0.750000, 0.250000], [0.500000, 0.250000],
+       [0.250000, 0.500000], [0.250000, 0.250000], [0.000000, 0.250000], [0.000000, 0.500000],
+       [0.250000, 0.500000], [0.250000, 0.250000], [0.500000, 0.250000], [0.500000, 0.500000],
+       [0.000000, 0.000000], [0.000000, 0.250000], [0.250000, 0.250000], [0.250000, 0.000000]]
+indices = [
+    0,
+    1,
+    2,
+    0,
+    2,
+    3,  #//ground face
+    6,
+    5,
+    4,
+    7,
+    6,
+    4,  #//top face
+    10,
+    9,
+    8,
+    11,
+    10,
+    8,
+    12,
+    13,
+    14,
+    12,
+    14,
+    15,
+    18,
+    17,
+    16,
+    19,
+    18,
+    16,
+    20,
+    21,
+    22,
+    20,
+    22,
+    23
 ]
-indices=[	0, 1, 2, 0, 2, 3,  #//ground face
-		6, 5, 4, 7, 6, 4,  #//top face
-		10, 9, 8, 11, 10, 8,
-		12, 13, 14, 12, 14, 15,
-		18, 17, 16, 19, 18, 16,
-		20, 21, 22, 20, 22, 23]
 
 #the visual shape and collision shape can be re-used by all createMultiBody instances (instancing)
-visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,rgbaColor=[1,1,1,1], specularColor=[0.4,.4,0], visualFramePosition=shift, meshScale=meshScale, vertices=vertices, indices=indices, uvs=uvs, normals=normals)
+visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,
+                                    rgbaColor=[1, 1, 1, 1],
+                                    specularColor=[0.4, .4, 0],
+                                    visualFramePosition=shift,
+                                    meshScale=meshScale,
+                                    vertices=vertices,
+                                    indices=indices,
+                                    uvs=uvs,
+                                    normals=normals)
 #visualShapeId = p.createVisualShape(shapeType=p.GEOM_BOX,rgbaColor=[1,1,1,1], halfExtents=[0.5,0.5,0.5],specularColor=[0.4,.4,0], visualFramePosition=shift, meshScale=[1,1,1], vertices=vertices, indices=indices)
 
 #visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,rgbaColor=[1,1,1,1], specularColor=[0.4,.4,0], visualFramePosition=shift, meshScale=meshScale, fileName="duck.obj")
-collisionShapeId = p.createCollisionShape(shapeType=p.GEOM_MESH, vertices=vertices, collisionFramePosition=shift,meshScale=meshScale)
+collisionShapeId = p.createCollisionShape(shapeType=p.GEOM_MESH,
+                                          vertices=vertices,
+                                          collisionFramePosition=shift,
+                                          meshScale=meshScale)
 
 texUid = p.loadTexture("tex256.png")
-		
+
 rangex = 1
 rangey = 1
-for i in range (rangex):
-	for j in range (rangey ):
-		bodyUid = p.createMultiBody(baseMass=1,baseInertialFramePosition=[0,0,0],baseCollisionShapeIndex=collisionShapeId, baseVisualShapeIndex = visualShapeId, basePosition = [((-rangex/2)+i)*meshScale[0]*2,(-rangey/2+j)*meshScale[1]*2,1], useMaximalCoordinates=True)
-		p.changeVisualShape(bodyUid,-1, textureUniqueId = texUid)		
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
+for i in range(rangex):
+  for j in range(rangey):
+    bodyUid = p.createMultiBody(baseMass=1,
+                                baseInertialFramePosition=[0, 0, 0],
+                                baseCollisionShapeIndex=collisionShapeId,
+                                baseVisualShapeIndex=visualShapeId,
+                                basePosition=[((-rangex / 2) + i) * meshScale[0] * 2,
+                                              (-rangey / 2 + j) * meshScale[1] * 2, 1],
+                                useMaximalCoordinates=True)
+    p.changeVisualShape(bodyUid, -1, textureUniqueId=texUid)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
 p.stopStateLogging(logId)
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 p.setRealTimeSimulation(1)
 
-colors = [[1,0,0,1],[0,1,0,1],[0,0,1,1],[1,1,1,1]]
+colors = [[1, 0, 0, 1], [0, 1, 0, 1], [0, 0, 1, 1], [1, 1, 1, 1]]
 currentColor = 0
 
 while (1):
-	p.getCameraImage(320,200)
-	mouseEvents = p.getMouseEvents()
-	for e in mouseEvents:
-		if ((e[0] == 2) and (e[3]==0) and (e[4]& p.KEY_WAS_TRIGGERED)):
-			mouseX = e[1]
-			mouseY = e[2]
-			rayFrom,rayTo=getRayFromTo(mouseX,mouseY)
-			rayInfo = p.rayTest(rayFrom,rayTo)
-			#p.addUserDebugLine(rayFrom,rayTo,[1,0,0],3)
-			for l in range(len(rayInfo)):
-				hit = rayInfo[l]
-				objectUid = hit[0]
-				if (objectUid>=1):
-					#p.removeBody(objectUid)
-					p.changeVisualShape(objectUid,-1,rgbaColor=colors[currentColor])
-					currentColor+=1
-					if (currentColor>=len(colors)):
-						currentColor=0
+  p.getCameraImage(320, 200)
+  mouseEvents = p.getMouseEvents()
+  for e in mouseEvents:
+    if ((e[0] == 2) and (e[3] == 0) and (e[4] & p.KEY_WAS_TRIGGERED)):
+      mouseX = e[1]
+      mouseY = e[2]
+      rayFrom, rayTo = getRayFromTo(mouseX, mouseY)
+      rayInfo = p.rayTest(rayFrom, rayTo)
+      #p.addUserDebugLine(rayFrom,rayTo,[1,0,0],3)
+      for l in range(len(rayInfo)):
+        hit = rayInfo[l]
+        objectUid = hit[0]
+        if (objectUid >= 1):
+          #p.removeBody(objectUid)
+          p.changeVisualShape(objectUid, -1, rgbaColor=colors[currentColor])
+          currentColor += 1
+          if (currentColor >= len(colors)):
+            currentColor = 0
diff --git a/examples/pybullet/examples/createVisualShape.py b/examples/pybullet/examples/createVisualShape.py
index c833ef378..9407feba3 100644
--- a/examples/pybullet/examples/createVisualShape.py
+++ b/examples/pybullet/examples/createVisualShape.py
@@ -2,72 +2,98 @@ import pybullet as p
 import time
 import math
 
-def getRayFromTo(mouseX,mouseY):
 
-	width, height, viewMat, projMat, cameraUp, camForward, horizon,vertical, _,_,dist, camTarget = p.getDebugVisualizerCamera()
-	camPos = [camTarget[0] - dist*camForward[0],camTarget[1] - dist*camForward[1],camTarget[2] - dist*camForward[2]]
-	farPlane = 10000
-	rayForward = [(camTarget[0]-camPos[0]),(camTarget[1]-camPos[1]),(camTarget[2]-camPos[2])]
-	invLen = farPlane*1./(math.sqrt(rayForward[0]*rayForward[0]+rayForward[1]*rayForward[1]+rayForward[2]*rayForward[2]))
-	rayForward = [invLen*rayForward[0],invLen*rayForward[1],invLen*rayForward[2]]
-	rayFrom = camPos
-	oneOverWidth = float(1)/float(width)
-	oneOverHeight = float(1)/float(height)
-	dHor = [horizon[0] * oneOverWidth,horizon[1] * oneOverWidth,horizon[2] * oneOverWidth]
-	dVer = [vertical[0] * oneOverHeight,vertical[1] * oneOverHeight,vertical[2] * oneOverHeight]
-	rayToCenter=[rayFrom[0]+rayForward[0],rayFrom[1]+rayForward[1],rayFrom[2]+rayForward[2]]
-	rayTo = [rayToCenter[0]  - 0.5 * horizon[0] + 0.5 * vertical[0]+float(mouseX)*dHor[0]-float(mouseY)*dVer[0],
-			rayToCenter[1]  - 0.5 * horizon[1] + 0.5 * vertical[1]+float(mouseX)*dHor[1]-float(mouseY)*dVer[1],
-				rayToCenter[2]  - 0.5 * horizon[2] + 0.5 * vertical[2]+float(mouseX)*dHor[2]-float(mouseY)*dVer[2]]
-	return rayFrom,rayTo
+def getRayFromTo(mouseX, mouseY):
+
+  width, height, viewMat, projMat, cameraUp, camForward, horizon, vertical, _, _, dist, camTarget = p.getDebugVisualizerCamera(
+  )
+  camPos = [
+      camTarget[0] - dist * camForward[0], camTarget[1] - dist * camForward[1],
+      camTarget[2] - dist * camForward[2]
+  ]
+  farPlane = 10000
+  rayForward = [(camTarget[0] - camPos[0]), (camTarget[1] - camPos[1]), (camTarget[2] - camPos[2])]
+  invLen = farPlane * 1. / (math.sqrt(rayForward[0] * rayForward[0] + rayForward[1] *
+                                      rayForward[1] + rayForward[2] * rayForward[2]))
+  rayForward = [invLen * rayForward[0], invLen * rayForward[1], invLen * rayForward[2]]
+  rayFrom = camPos
+  oneOverWidth = float(1) / float(width)
+  oneOverHeight = float(1) / float(height)
+  dHor = [horizon[0] * oneOverWidth, horizon[1] * oneOverWidth, horizon[2] * oneOverWidth]
+  dVer = [vertical[0] * oneOverHeight, vertical[1] * oneOverHeight, vertical[2] * oneOverHeight]
+  rayToCenter = [
+      rayFrom[0] + rayForward[0], rayFrom[1] + rayForward[1], rayFrom[2] + rayForward[2]
+  ]
+  rayTo = [
+      rayToCenter[0] - 0.5 * horizon[0] + 0.5 * vertical[0] + float(mouseX) * dHor[0] -
+      float(mouseY) * dVer[0], rayToCenter[1] - 0.5 * horizon[1] + 0.5 * vertical[1] +
+      float(mouseX) * dHor[1] - float(mouseY) * dVer[1], rayToCenter[2] - 0.5 * horizon[2] +
+      0.5 * vertical[2] + float(mouseX) * dHor[2] - float(mouseY) * dVer[2]
+  ]
+  return rayFrom, rayTo
+
 
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	p.connect(p.GUI)
+if (cid < 0):
+  p.connect(p.GUI)
 p.setPhysicsEngineParameter(numSolverIterations=10)
-p.setTimeStep(1./120.)
+p.setTimeStep(1. / 120.)
 logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "visualShapeBench.json")
 #useMaximalCoordinates is much faster then the default reduced coordinates (Featherstone)
 p.loadURDF("plane100.urdf", useMaximalCoordinates=True)
 #disable rendering during creation.
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)
-p.configureDebugVisualizer(p.COV_ENABLE_GUI,0)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
+p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0)
 #disable tinyrenderer, software (CPU) renderer, we don't use it here
-p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER,0)
+p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER, 0)
 
-shift = [0,-0.02,0]
-meshScale=[0.1,0.1,0.1]
+shift = [0, -0.02, 0]
+meshScale = [0.1, 0.1, 0.1]
 #the visual shape and collision shape can be re-used by all createMultiBody instances (instancing)
-visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,fileName="duck.obj", rgbaColor=[1,1,1,1], specularColor=[0.4,.4,0], visualFramePosition=shift, meshScale=meshScale)
-collisionShapeId = p.createCollisionShape(shapeType=p.GEOM_MESH, fileName="duck_vhacd.obj", collisionFramePosition=shift,meshScale=meshScale)
+visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,
+                                    fileName="duck.obj",
+                                    rgbaColor=[1, 1, 1, 1],
+                                    specularColor=[0.4, .4, 0],
+                                    visualFramePosition=shift,
+                                    meshScale=meshScale)
+collisionShapeId = p.createCollisionShape(shapeType=p.GEOM_MESH,
+                                          fileName="duck_vhacd.obj",
+                                          collisionFramePosition=shift,
+                                          meshScale=meshScale)
 
 rangex = 5
 rangey = 5
-for i in range (rangex):
-	for j in range (rangey ):
-		p.createMultiBody(baseMass=1,baseInertialFramePosition=[0,0,0],baseCollisionShapeIndex=collisionShapeId, baseVisualShapeIndex = visualShapeId, basePosition = [((-rangex/2)+i)*meshScale[0]*2,(-rangey/2+j)*meshScale[1]*2,1], useMaximalCoordinates=True)
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
+for i in range(rangex):
+  for j in range(rangey):
+    p.createMultiBody(baseMass=1,
+                      baseInertialFramePosition=[0, 0, 0],
+                      baseCollisionShapeIndex=collisionShapeId,
+                      baseVisualShapeIndex=visualShapeId,
+                      basePosition=[((-rangex / 2) + i) * meshScale[0] * 2,
+                                    (-rangey / 2 + j) * meshScale[1] * 2, 1],
+                      useMaximalCoordinates=True)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
 p.stopStateLogging(logId)
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 p.setRealTimeSimulation(1)
 
-colors = [[1,0,0,1],[0,1,0,1],[0,0,1,1],[1,1,1,1]]
+colors = [[1, 0, 0, 1], [0, 1, 0, 1], [0, 0, 1, 1], [1, 1, 1, 1]]
 currentColor = 0
 
 while (1):
-	mouseEvents = p.getMouseEvents()
-	for e in mouseEvents:
-		if ((e[0] == 2) and (e[3]==0) and (e[4]& p.KEY_WAS_TRIGGERED)):
-			mouseX = e[1]
-			mouseY = e[2]
-			rayFrom,rayTo=getRayFromTo(mouseX,mouseY)
-			rayInfo = p.rayTest(rayFrom,rayTo)
-			#p.addUserDebugLine(rayFrom,rayTo,[1,0,0],3)
-			for l in range(len(rayInfo)):
-				hit = rayInfo[l]
-				objectUid = hit[0]
-				if (objectUid>=1):
-					p.changeVisualShape(objectUid,-1,rgbaColor=colors[currentColor])
-					currentColor+=1
-					if (currentColor>=len(colors)):
-						currentColor=0
+  mouseEvents = p.getMouseEvents()
+  for e in mouseEvents:
+    if ((e[0] == 2) and (e[3] == 0) and (e[4] & p.KEY_WAS_TRIGGERED)):
+      mouseX = e[1]
+      mouseY = e[2]
+      rayFrom, rayTo = getRayFromTo(mouseX, mouseY)
+      rayInfo = p.rayTest(rayFrom, rayTo)
+      #p.addUserDebugLine(rayFrom,rayTo,[1,0,0],3)
+      for l in range(len(rayInfo)):
+        hit = rayInfo[l]
+        objectUid = hit[0]
+        if (objectUid >= 1):
+          p.changeVisualShape(objectUid, -1, rgbaColor=colors[currentColor])
+          currentColor += 1
+          if (currentColor >= len(colors)):
+            currentColor = 0
diff --git a/examples/pybullet/examples/createVisualShapeArray.py b/examples/pybullet/examples/createVisualShapeArray.py
index 36f94690c..713738eb1 100644
--- a/examples/pybullet/examples/createVisualShapeArray.py
+++ b/examples/pybullet/examples/createVisualShapeArray.py
@@ -2,80 +2,111 @@ import pybullet as p
 import time
 import math
 
-def getRayFromTo(mouseX,mouseY):
-	width, height, viewMat, projMat, cameraUp, camForward, horizon,vertical, _,_,dist, camTarget = p.getDebugVisualizerCamera()
-	camPos = [camTarget[0] - dist*camForward[0],camTarget[1] - dist*camForward[1],camTarget[2] - dist*camForward[2]]
-	farPlane = 10000
-	rayForward = [(camTarget[0]-camPos[0]),(camTarget[1]-camPos[1]),(camTarget[2]-camPos[2])]
-	invLen = farPlane*1./(math.sqrt(rayForward[0]*rayForward[0]+rayForward[1]*rayForward[1]+rayForward[2]*rayForward[2]))
-	rayForward = [invLen*rayForward[0],invLen*rayForward[1],invLen*rayForward[2]]
-	rayFrom = camPos
-	oneOverWidth = float(1)/float(width)
-	oneOverHeight = float(1)/float(height)
-	dHor = [horizon[0] * oneOverWidth,horizon[1] * oneOverWidth,horizon[2] * oneOverWidth]
-	dVer = [vertical[0] * oneOverHeight,vertical[1] * oneOverHeight,vertical[2] * oneOverHeight]
-	rayToCenter=[rayFrom[0]+rayForward[0],rayFrom[1]+rayForward[1],rayFrom[2]+rayForward[2]]
-	rayTo = [rayFrom[0]+rayForward[0]  - 0.5 * horizon[0] + 0.5 * vertical[0]+float(mouseX)*dHor[0]-float(mouseY)*dVer[0],
-					rayFrom[1]+rayForward[1]  - 0.5 * horizon[1] + 0.5 * vertical[1]+float(mouseX)*dHor[1]-float(mouseY)*dVer[1],
-					rayFrom[2]+rayForward[2]  - 0.5 * horizon[2] + 0.5 * vertical[2]+float(mouseX)*dHor[2]-float(mouseY)*dVer[2]]
-	return rayFrom,rayTo
+
+def getRayFromTo(mouseX, mouseY):
+  width, height, viewMat, projMat, cameraUp, camForward, horizon, vertical, _, _, dist, camTarget = p.getDebugVisualizerCamera(
+  )
+  camPos = [
+      camTarget[0] - dist * camForward[0], camTarget[1] - dist * camForward[1],
+      camTarget[2] - dist * camForward[2]
+  ]
+  farPlane = 10000
+  rayForward = [(camTarget[0] - camPos[0]), (camTarget[1] - camPos[1]), (camTarget[2] - camPos[2])]
+  invLen = farPlane * 1. / (math.sqrt(rayForward[0] * rayForward[0] + rayForward[1] *
+                                      rayForward[1] + rayForward[2] * rayForward[2]))
+  rayForward = [invLen * rayForward[0], invLen * rayForward[1], invLen * rayForward[2]]
+  rayFrom = camPos
+  oneOverWidth = float(1) / float(width)
+  oneOverHeight = float(1) / float(height)
+  dHor = [horizon[0] * oneOverWidth, horizon[1] * oneOverWidth, horizon[2] * oneOverWidth]
+  dVer = [vertical[0] * oneOverHeight, vertical[1] * oneOverHeight, vertical[2] * oneOverHeight]
+  rayToCenter = [
+      rayFrom[0] + rayForward[0], rayFrom[1] + rayForward[1], rayFrom[2] + rayForward[2]
+  ]
+  rayTo = [
+      rayFrom[0] + rayForward[0] - 0.5 * horizon[0] + 0.5 * vertical[0] + float(mouseX) * dHor[0] -
+      float(mouseY) * dVer[0], rayFrom[1] + rayForward[1] - 0.5 * horizon[1] + 0.5 * vertical[1] +
+      float(mouseX) * dHor[1] - float(mouseY) * dVer[1], rayFrom[2] + rayForward[2] -
+      0.5 * horizon[2] + 0.5 * vertical[2] + float(mouseX) * dHor[2] - float(mouseY) * dVer[2]
+  ]
+  return rayFrom, rayTo
+
 
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	p.connect(p.GUI)
+if (cid < 0):
+  p.connect(p.GUI)
 p.setPhysicsEngineParameter(numSolverIterations=10)
-p.setTimeStep(1./120.)
+p.setTimeStep(1. / 120.)
 logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "visualShapeBench.json")
 #useMaximalCoordinates is much faster then the default reduced coordinates (Featherstone)
 p.loadURDF("plane100.urdf", useMaximalCoordinates=True)
 #disable rendering during creation.
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)
-p.configureDebugVisualizer(p.COV_ENABLE_GUI,0)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
+p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0)
 #disable tinyrenderer, software (CPU) renderer, we don't use it here
-p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER,0)
+p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER, 0)
 
-shift = [0,-0.02,0]
-shift1 = [0,0.1,0]
-shift2 = [0,0,0]
+shift = [0, -0.02, 0]
+shift1 = [0, 0.1, 0]
+shift2 = [0, 0, 0]
 
-meshScale=[0.1,0.1,0.1]
+meshScale = [0.1, 0.1, 0.1]
 #the visual shape and collision shape can be re-used by all createMultiBody instances (instancing)
-visualShapeId = p.createVisualShapeArray(shapeTypes=[p.GEOM_MESH, p.GEOM_BOX], halfExtents=[[0,0,0],[0.1,0.1,0.1]],fileNames=["duck.obj",""], visualFramePositions=[shift1,shift2,],meshScales=[meshScale,meshScale])
-collisionShapeId = p.createCollisionShapeArray(shapeTypes=[p.GEOM_MESH, p.GEOM_BOX], halfExtents=[[0,0,0],[0.1,0.1,0.1]],fileNames=["duck_vhacd.obj",""], collisionFramePositions=[shift1,shift2,],meshScales=[meshScale,meshScale])
+visualShapeId = p.createVisualShapeArray(shapeTypes=[p.GEOM_MESH, p.GEOM_BOX],
+                                         halfExtents=[[0, 0, 0], [0.1, 0.1, 0.1]],
+                                         fileNames=["duck.obj", ""],
+                                         visualFramePositions=[
+                                             shift1,
+                                             shift2,
+                                         ],
+                                         meshScales=[meshScale, meshScale])
+collisionShapeId = p.createCollisionShapeArray(shapeTypes=[p.GEOM_MESH, p.GEOM_BOX],
+                                               halfExtents=[[0, 0, 0], [0.1, 0.1, 0.1]],
+                                               fileNames=["duck_vhacd.obj", ""],
+                                               collisionFramePositions=[
+                                                   shift1,
+                                                   shift2,
+                                               ],
+                                               meshScales=[meshScale, meshScale])
 
-
-rangex =2
+rangex = 2
 rangey = 2
-for i in range (rangex):
-	for j in range (rangey ):
-		mb = p.createMultiBody(baseMass=1,baseInertialFramePosition=[0,0,0],baseCollisionShapeIndex=collisionShapeId, baseVisualShapeIndex = visualShapeId, basePosition = [((-rangex/2)+i*2)*meshScale[0]*2,(-rangey/2+j)*meshScale[1]*4,1], useMaximalCoordinates=False)
-		p.changeVisualShape(mb,-1,rgbaColor=[1,1,1,1])
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
+for i in range(rangex):
+  for j in range(rangey):
+    mb = p.createMultiBody(baseMass=1,
+                           baseInertialFramePosition=[0, 0, 0],
+                           baseCollisionShapeIndex=collisionShapeId,
+                           baseVisualShapeIndex=visualShapeId,
+                           basePosition=[((-rangex / 2) + i * 2) * meshScale[0] * 2,
+                                         (-rangey / 2 + j) * meshScale[1] * 4, 1],
+                           useMaximalCoordinates=False)
+    p.changeVisualShape(mb, -1, rgbaColor=[1, 1, 1, 1])
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
 p.stopStateLogging(logId)
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 p.setRealTimeSimulation(1)
 
-colors = [[1,0,0,1],[0,1,0,1],[0,0,1,1],[1,1,1,1]]
+colors = [[1, 0, 0, 1], [0, 1, 0, 1], [0, 0, 1, 1], [1, 1, 1, 1]]
 currentColor = 0
 
-p.getCameraImage(64,64, renderer=p.ER_BULLET_HARDWARE_OPENGL)
+p.getCameraImage(64, 64, renderer=p.ER_BULLET_HARDWARE_OPENGL)
 
 while (1):
-	
-	mouseEvents = p.getMouseEvents()
-	for e in mouseEvents:
-		if ((e[0] == 2) and (e[3]==0) and (e[4]& p.KEY_WAS_TRIGGERED)):
-			mouseX = e[1]
-			mouseY = e[2]
-			rayFrom,rayTo=getRayFromTo(mouseX,mouseY)
-			rayInfo = p.rayTest(rayFrom,rayTo)
-			#p.addUserDebugLine(rayFrom,rayTo,[1,0,0],3)
-			for l in range(len(rayInfo)):
-				hit = rayInfo[l]
-				objectUid = hit[0]
-				if (objectUid>=0):
-					#p.removeBody(objectUid)
-					p.changeVisualShape(objectUid,-1,rgbaColor=colors[currentColor])
-					currentColor+=1
-					if (currentColor>=len(colors)):
-						currentColor=0
+
+  mouseEvents = p.getMouseEvents()
+  for e in mouseEvents:
+    if ((e[0] == 2) and (e[3] == 0) and (e[4] & p.KEY_WAS_TRIGGERED)):
+      mouseX = e[1]
+      mouseY = e[2]
+      rayFrom, rayTo = getRayFromTo(mouseX, mouseY)
+      rayInfo = p.rayTest(rayFrom, rayTo)
+      #p.addUserDebugLine(rayFrom,rayTo,[1,0,0],3)
+      for l in range(len(rayInfo)):
+        hit = rayInfo[l]
+        objectUid = hit[0]
+        if (objectUid >= 0):
+          #p.removeBody(objectUid)
+          p.changeVisualShape(objectUid, -1, rgbaColor=colors[currentColor])
+          currentColor += 1
+          if (currentColor >= len(colors)):
+            currentColor = 0
diff --git a/examples/pybullet/examples/debugDrawItems.py b/examples/pybullet/examples/debugDrawItems.py
index e493f1dad..0aac654bc 100644
--- a/examples/pybullet/examples/debugDrawItems.py
+++ b/examples/pybullet/examples/debugDrawItems.py
@@ -2,18 +2,22 @@ import pybullet as p
 import time
 
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	p.connect(p.GUI)
+if (cid < 0):
+  p.connect(p.GUI)
 p.loadURDF("plane.urdf")
 kuka = p.loadURDF("kuka_iiwa/model.urdf")
-p.addUserDebugText("tip", [0,0,0.1],textColorRGB=[1,0,0],textSize=1.5,parentObjectUniqueId=kuka, parentLinkIndex=6)
-p.addUserDebugLine([0,0,0],[0.1,0,0],[1,0,0],parentObjectUniqueId=kuka, parentLinkIndex=6)
-p.addUserDebugLine([0,0,0],[0,0.1,0],[0,1,0],parentObjectUniqueId=kuka, parentLinkIndex=6)
-p.addUserDebugLine([0,0,0],[0,0,0.1],[0,0,1],parentObjectUniqueId=kuka, parentLinkIndex=6)
+p.addUserDebugText("tip", [0, 0, 0.1],
+                   textColorRGB=[1, 0, 0],
+                   textSize=1.5,
+                   parentObjectUniqueId=kuka,
+                   parentLinkIndex=6)
+p.addUserDebugLine([0, 0, 0], [0.1, 0, 0], [1, 0, 0], parentObjectUniqueId=kuka, parentLinkIndex=6)
+p.addUserDebugLine([0, 0, 0], [0, 0.1, 0], [0, 1, 0], parentObjectUniqueId=kuka, parentLinkIndex=6)
+p.addUserDebugLine([0, 0, 0], [0, 0, 0.1], [0, 0, 1], parentObjectUniqueId=kuka, parentLinkIndex=6)
 p.setRealTimeSimulation(0)
-angle=0
+angle = 0
 while (True):
-	time.sleep(0.01)
-	p.resetJointState(kuka,2,angle)
-	p.resetJointState(kuka,3,angle)
-	angle+=0.01
\ No newline at end of file
+  time.sleep(0.01)
+  p.resetJointState(kuka, 2, angle)
+  p.resetJointState(kuka, 3, angle)
+  angle += 0.01
diff --git a/examples/pybullet/examples/dumpLog.py b/examples/pybullet/examples/dumpLog.py
index 1c73dfd63..05f283d08 100644
--- a/examples/pybullet/examples/dumpLog.py
+++ b/examples/pybullet/examples/dumpLog.py
@@ -7,7 +7,8 @@ import os, fnmatch
 import argparse
 from time import sleep
 
-def readLogFile(filename, verbose = True):
+
+def readLogFile(filename, verbose=True):
   f = open(filename, 'rb')
 
   print('Opened'),
@@ -44,19 +45,19 @@ def readLogFile(filename, verbose = True):
       if verbose:
         print("num chunks:")
         print(len(chunks))
-      
+
       for chunk in chunks:
-        print("len(chunk)=",len(chunk)," sz = ", sz)
+        print("len(chunk)=", len(chunk), " sz = ", sz)
         if len(chunk) == sz:
-          print("chunk #",chunkIndex)
-          chunkIndex=chunkIndex+1
+          print("chunk #", chunkIndex)
+          chunkIndex = chunkIndex + 1
           values = struct.unpack(fmt, chunk)
           record = list()
           for i in range(ncols):
             record.append(values[i])
             if verbose:
-              print("    ",keys[i],"=",values[i])
-    
+              print("    ", keys[i], "=", values[i])
+
           log.append(record)
     else:
       print("Error, expected aabb terminal")
@@ -65,16 +66,16 @@ def readLogFile(filename, verbose = True):
 
 numArgs = len(sys.argv)
 
-print ('Number of arguments:', numArgs, 'arguments.')
-print ('Argument List:', str(sys.argv))
+print('Number of arguments:', numArgs, 'arguments.')
+print('Argument List:', str(sys.argv))
 fileName = "log.bin"
 
-if (numArgs>1):
+if (numArgs > 1):
   fileName = sys.argv[1]
 
 print("filename=")
 print(fileName)
 
 verbose = True
-  
-readLogFile(fileName,verbose)
+
+readLogFile(fileName, verbose)
diff --git a/examples/pybullet/examples/dumpVrLog.py b/examples/pybullet/examples/dumpVrLog.py
index 0fe87cdf0..81767a6c8 100644
--- a/examples/pybullet/examples/dumpVrLog.py
+++ b/examples/pybullet/examples/dumpVrLog.py
@@ -7,7 +7,8 @@ import os, fnmatch
 import argparse
 from time import sleep
 
-def readLogFile(filename, verbose = True):
+
+def readLogFile(filename, verbose=True):
   f = open(filename, 'rb')
 
   print('Opened'),
@@ -37,20 +38,20 @@ def readLogFile(filename, verbose = True):
   chunks = wholeFile.split(b'\xaa\xbb')
   log = list()
   if verbose:
-  	print("num chunks:")
-  	print(len(chunks))
+    print("num chunks:")
+    print(len(chunks))
   chunkIndex = 0
   for chunk in chunks:
-    print("len(chunk)=",len(chunk)," sz = ", sz)
+    print("len(chunk)=", len(chunk), " sz = ", sz)
     if len(chunk) == sz:
-      print("chunk #",chunkIndex)
-      chunkIndex=chunkIndex+1
+      print("chunk #", chunkIndex)
+      chunkIndex = chunkIndex + 1
       values = struct.unpack(fmt, chunk)
       record = list()
       for i in range(ncols):
         record.append(values[i])
         if verbose:
-        	print("    ",keys[i],"=",values[i])
+          print("    ", keys[i], "=", values[i])
 
       log.append(record)
 
@@ -59,19 +60,19 @@ def readLogFile(filename, verbose = True):
 
 numArgs = len(sys.argv)
 
-print ('Number of arguments:', numArgs, 'arguments.')
-print ('Argument List:', str(sys.argv))
+print('Number of arguments:', numArgs, 'arguments.')
+print('Argument List:', str(sys.argv))
 fileName = "data/example_log_vr.bin"
 
-if (numArgs>1):
-	fileName = sys.argv[1]
+if (numArgs > 1):
+  fileName = sys.argv[1]
 
 print("filename=")
 print(fileName)
 
 verbose = True
-	
-log = readLogFile(fileName,verbose)
+
+log = readLogFile(fileName, verbose)
 
 # the index of the first integer in the vr log file for packed buttons
 firstPackedButtonIndex = 13
@@ -83,26 +84,27 @@ numPackedButtons = 7
 buttonMask = 7
 
 for record in log:
-	# indices of buttons that are down
-	buttonDownIndices = []
-	# indices of buttons that are triggered
-	buttonTriggeredIndices = []
-	# indices of buttons that are released
-	buttonReleasedIndices = []
-	buttonIndex = 0
-	for packedButtonIndex in range(firstPackedButtonIndex, firstPackedButtonIndex+numPackedButtons):
-		for packButtonShift in range(numGroupedButtons):
-			buttonEvent = buttonMask & record[packedButtonIndex]
-			if buttonEvent & 1:
-				buttonDownIndices.append(buttonIndex)
-			elif buttonEvent & 2:
-				buttonTriggeredIndices.append(buttonIndex)
-			elif buttonEvent & 4:
-				buttonReleasedIndices.append(buttonIndex)
-			record[packedButtonIndex] = record[packedButtonIndex] >> 3
-			buttonIndex += 1
-	if len(buttonDownIndices) or len(buttonTriggeredIndices) or len(buttonReleasedIndices):
-		print ('timestamp: ', record[1])
-		print ('button is down: ', buttonDownIndices)
-		print ('button is triggered: ', buttonTriggeredIndices)
-		print ('button is released: ', buttonReleasedIndices)
+  # indices of buttons that are down
+  buttonDownIndices = []
+  # indices of buttons that are triggered
+  buttonTriggeredIndices = []
+  # indices of buttons that are released
+  buttonReleasedIndices = []
+  buttonIndex = 0
+  for packedButtonIndex in range(firstPackedButtonIndex,
+                                 firstPackedButtonIndex + numPackedButtons):
+    for packButtonShift in range(numGroupedButtons):
+      buttonEvent = buttonMask & record[packedButtonIndex]
+      if buttonEvent & 1:
+        buttonDownIndices.append(buttonIndex)
+      elif buttonEvent & 2:
+        buttonTriggeredIndices.append(buttonIndex)
+      elif buttonEvent & 4:
+        buttonReleasedIndices.append(buttonIndex)
+      record[packedButtonIndex] = record[packedButtonIndex] >> 3
+      buttonIndex += 1
+  if len(buttonDownIndices) or len(buttonTriggeredIndices) or len(buttonReleasedIndices):
+    print('timestamp: ', record[1])
+    print('button is down: ', buttonDownIndices)
+    print('button is triggered: ', buttonTriggeredIndices)
+    print('button is released: ', buttonReleasedIndices)
diff --git a/examples/pybullet/examples/eglRenderTest.py b/examples/pybullet/examples/eglRenderTest.py
index 6d51c4bb8..13dbf2091 100644
--- a/examples/pybullet/examples/eglRenderTest.py
+++ b/examples/pybullet/examples/eglRenderTest.py
@@ -1,4 +1,3 @@
-
 import pybullet as p
 import time
 import pkgutil
@@ -7,41 +6,48 @@ egl = pkgutil.get_loader('eglRenderer')
 p.connect(p.DIRECT)
 
 plugin = p.loadPlugin(egl.get_filename(), "_eglRendererPlugin")
-print("plugin=",plugin)
+print("plugin=", plugin)
 
 p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
 p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0)
 
-p.setGravity(0,0,-10)
-p.loadURDF("plane.urdf",[0,0,-1])
+p.setGravity(0, 0, -10)
+p.loadURDF("plane.urdf", [0, 0, -1])
 p.loadURDF("r2d2.urdf")
 
 pixelWidth = 320
 pixelHeight = 220
-camTargetPos = [0,0,0]
+camTargetPos = [0, 0, 0]
 camDistance = 4
 pitch = -10.0
-roll=0
+roll = 0
 upAxisIndex = 2
 
-
 while (p.isConnected()):
-    for yaw in range (0,360,10) :
-        start = time.time()
-        p.stepSimulation()
-        stop = time.time()
-        print ("stepSimulation %f" % (stop - start))
-
-        #viewMatrix = [1.0, 0.0, -0.0, 0.0, -0.0, 0.1736481785774231, -0.9848078489303589, 0.0, 0.0, 0.9848078489303589, 0.1736481785774231, 0.0, -0.0, -5.960464477539063e-08, -4.0, 1.0]
-        viewMatrix = p.computeViewMatrixFromYawPitchRoll(camTargetPos, camDistance, yaw, pitch, roll, upAxisIndex)
-        projectionMatrix  = [1.0825318098068237, 0.0, 0.0, 0.0, 0.0, 1.732050895690918, 0.0, 0.0, 0.0, 0.0, -1.0002000331878662, -1.0, 0.0, 0.0, -0.020002000033855438, 0.0]
-
-        start = time.time()
-        img_arr = p.getCameraImage(pixelWidth, pixelHeight, viewMatrix=viewMatrix, projectionMatrix=projectionMatrix, shadow=1,lightDirection=[1,1,1])
-        stop = time.time()
-        print ("renderImage %f" % (stop - start))
-        #time.sleep(.1)
-        #print("img_arr=",img_arr)
-
+  for yaw in range(0, 360, 10):
+    start = time.time()
+    p.stepSimulation()
+    stop = time.time()
+    print("stepSimulation %f" % (stop - start))
+
+    #viewMatrix = [1.0, 0.0, -0.0, 0.0, -0.0, 0.1736481785774231, -0.9848078489303589, 0.0, 0.0, 0.9848078489303589, 0.1736481785774231, 0.0, -0.0, -5.960464477539063e-08, -4.0, 1.0]
+    viewMatrix = p.computeViewMatrixFromYawPitchRoll(camTargetPos, camDistance, yaw, pitch, roll,
+                                                     upAxisIndex)
+    projectionMatrix = [
+        1.0825318098068237, 0.0, 0.0, 0.0, 0.0, 1.732050895690918, 0.0, 0.0, 0.0, 0.0,
+        -1.0002000331878662, -1.0, 0.0, 0.0, -0.020002000033855438, 0.0
+    ]
+
+    start = time.time()
+    img_arr = p.getCameraImage(pixelWidth,
+                               pixelHeight,
+                               viewMatrix=viewMatrix,
+                               projectionMatrix=projectionMatrix,
+                               shadow=1,
+                               lightDirection=[1, 1, 1])
+    stop = time.time()
+    print("renderImage %f" % (stop - start))
+    #time.sleep(.1)
+    #print("img_arr=",img_arr)
 
 p.unloadPlugin(plugin)
diff --git a/examples/pybullet/examples/experimentalCcdSphereRadius.py b/examples/pybullet/examples/experimentalCcdSphereRadius.py
index eb7eef6c4..6f3604b1b 100644
--- a/examples/pybullet/examples/experimentalCcdSphereRadius.py
+++ b/examples/pybullet/examples/experimentalCcdSphereRadius.py
@@ -4,48 +4,50 @@ import time
 p.connect(p.GUI)
 p.setPhysicsEngineParameter(allowedCcdPenetration=0.0)
 
+terrain_mass = 0
+terrain_visual_shape_id = -1
+terrain_position = [0, 0, 0]
+terrain_orientation = [0, 0, 0, 1]
+terrain_collision_shape_id = p.createCollisionShape(shapeType=p.GEOM_MESH,
+                                                    fileName="terrain.obj",
+                                                    flags=p.GEOM_FORCE_CONCAVE_TRIMESH |
+                                                    p.GEOM_CONCAVE_INTERNAL_EDGE,
+                                                    meshScale=[0.5, 0.5, 0.5])
+p.createMultiBody(terrain_mass, terrain_collision_shape_id, terrain_visual_shape_id,
+                  terrain_position, terrain_orientation)
 
-terrain_mass=0
-terrain_visual_shape_id=-1
-terrain_position=[0,0,0]
-terrain_orientation=[0,0,0,1]
-terrain_collision_shape_id = p.createCollisionShape(
-	shapeType=p.GEOM_MESH,
-  fileName="terrain.obj",
-  flags=p.GEOM_FORCE_CONCAVE_TRIMESH|p.GEOM_CONCAVE_INTERNAL_EDGE,
-  meshScale=[0.5, 0.5, 0.5])
-p.createMultiBody(
-	terrain_mass, terrain_collision_shape_id, terrain_visual_shape_id,
-	terrain_position, terrain_orientation)
-            
-            
 useMaximalCoordinates = True
 sphereRadius = 0.005
-colSphereId = p.createCollisionShape(p.GEOM_SPHERE,radius=sphereRadius)
-colBoxId = p.createCollisionShape(p.GEOM_BOX,halfExtents=[sphereRadius,sphereRadius,sphereRadius])
+colSphereId = p.createCollisionShape(p.GEOM_SPHERE, radius=sphereRadius)
+colBoxId = p.createCollisionShape(p.GEOM_BOX,
+                                  halfExtents=[sphereRadius, sphereRadius, sphereRadius])
 
 mass = 1
 visualShapeId = -1
 
-
-for i in range (5):
-	for j in range (5):
-		for k in range (5):
-			#if (k&2):
-			sphereUid = p.createMultiBody(mass,colSphereId,visualShapeId,[-i*5*sphereRadius,j*5*sphereRadius,k*2*sphereRadius+1],useMaximalCoordinates=useMaximalCoordinates)
-			#else:
-			#	sphereUid = p.createMultiBody(mass,colBoxId,visualShapeId,[-i*2*sphereRadius,j*2*sphereRadius,k*2*sphereRadius+1], useMaximalCoordinates=useMaximalCoordinates)
-			p.changeDynamics(sphereUid,-1,spinningFriction=0.001, rollingFriction=0.001,linearDamping=0.0)
-			p.changeDynamics(sphereUid,-1,ccdSweptSphereRadius=0.002)
-			
-
-
-p.setGravity(0,0,-10)
+for i in range(5):
+  for j in range(5):
+    for k in range(5):
+      #if (k&2):
+      sphereUid = p.createMultiBody(
+          mass,
+          colSphereId,
+          visualShapeId, [-i * 5 * sphereRadius, j * 5 * sphereRadius, k * 2 * sphereRadius + 1],
+          useMaximalCoordinates=useMaximalCoordinates)
+      #else:
+      #	sphereUid = p.createMultiBody(mass,colBoxId,visualShapeId,[-i*2*sphereRadius,j*2*sphereRadius,k*2*sphereRadius+1], useMaximalCoordinates=useMaximalCoordinates)
+      p.changeDynamics(sphereUid,
+                       -1,
+                       spinningFriction=0.001,
+                       rollingFriction=0.001,
+                       linearDamping=0.0)
+      p.changeDynamics(sphereUid, -1, ccdSweptSphereRadius=0.002)
+
+p.setGravity(0, 0, -10)
 
 pts = p.getContactPoints()
-print("num points=",len(pts))
+print("num points=", len(pts))
 print(pts)
 while (p.isConnected()):
-	time.sleep(1./240.)
-	p.stepSimulation()
-	
\ No newline at end of file
+  time.sleep(1. / 240.)
+  p.stepSimulation()
diff --git a/examples/pybullet/examples/externalTorqueControlledSphere.py b/examples/pybullet/examples/externalTorqueControlledSphere.py
index 1beddc7b7..45b2cfe70 100644
--- a/examples/pybullet/examples/externalTorqueControlledSphere.py
+++ b/examples/pybullet/examples/externalTorqueControlledSphere.py
@@ -5,57 +5,55 @@ import time
 p.connect(p.GUI)
 p.setAdditionalSearchPath(pybullet_data.getDataPath())
 p.loadSDF("stadium.sdf")
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 objects = p.loadMJCF("mjcf/sphere.xml")
 sphere = objects[0]
-p.resetBasePositionAndOrientation(sphere,[0,0,1],[0,0,0,1])
-p.changeDynamics(sphere,-1,linearDamping=0.9)
-p.changeVisualShape(sphere,-1,rgbaColor=[1,0,0,1])
+p.resetBasePositionAndOrientation(sphere, [0, 0, 1], [0, 0, 0, 1])
+p.changeDynamics(sphere, -1, linearDamping=0.9)
+p.changeVisualShape(sphere, -1, rgbaColor=[1, 0, 0, 1])
 forward = 0
 turn = 0
 
-
-forwardVec = [2,0,0]
+forwardVec = [2, 0, 0]
 cameraDistance = 1
 cameraYaw = 35
 cameraPitch = -35
 
 while (1):
 
-	spherePos, orn = p.getBasePositionAndOrientation(sphere)
-	
-	cameraTargetPosition = spherePos
-	p.resetDebugVisualizerCamera(cameraDistance,cameraYaw,cameraPitch,cameraTargetPosition)
-	camInfo = p.getDebugVisualizerCamera()
-	camForward = camInfo[5]
-	
-	
-	keys = p.getKeyboardEvents()
-	for k,v in keys.items():
-		
-		if (k == p.B3G_RIGHT_ARROW and (v&p.KEY_WAS_TRIGGERED)):
-			turn = -0.5
-		if (k == p.B3G_RIGHT_ARROW and (v&p.KEY_WAS_RELEASED)):
-			turn = 0
-		if (k == p.B3G_LEFT_ARROW and (v&p.KEY_WAS_TRIGGERED)):
-			turn = 0.5
-		if (k == p.B3G_LEFT_ARROW and (v&p.KEY_WAS_RELEASED)):
-			turn = 0
-		
-		if (k == p.B3G_UP_ARROW and (v&p.KEY_WAS_TRIGGERED)):
-			forward=1
-		if (k == p.B3G_UP_ARROW and (v&p.KEY_WAS_RELEASED)):
-			forward=0
-		if (k == p.B3G_DOWN_ARROW and (v&p.KEY_WAS_TRIGGERED)):
-			forward=-1
-		if (k == p.B3G_DOWN_ARROW and (v&p.KEY_WAS_RELEASED)):
-			forward=0
-	
-	force  = [forward*camForward[0],forward*camForward[1],0]
-	cameraYaw = cameraYaw+turn
-	
-	if (forward):
-		p.applyExternalForce(sphere,-1, force , spherePos, flags = p.WORLD_FRAME )
-		
-	p.stepSimulation()
-	time.sleep(1./240.)
\ No newline at end of file
+  spherePos, orn = p.getBasePositionAndOrientation(sphere)
+
+  cameraTargetPosition = spherePos
+  p.resetDebugVisualizerCamera(cameraDistance, cameraYaw, cameraPitch, cameraTargetPosition)
+  camInfo = p.getDebugVisualizerCamera()
+  camForward = camInfo[5]
+
+  keys = p.getKeyboardEvents()
+  for k, v in keys.items():
+
+    if (k == p.B3G_RIGHT_ARROW and (v & p.KEY_WAS_TRIGGERED)):
+      turn = -0.5
+    if (k == p.B3G_RIGHT_ARROW and (v & p.KEY_WAS_RELEASED)):
+      turn = 0
+    if (k == p.B3G_LEFT_ARROW and (v & p.KEY_WAS_TRIGGERED)):
+      turn = 0.5
+    if (k == p.B3G_LEFT_ARROW and (v & p.KEY_WAS_RELEASED)):
+      turn = 0
+
+    if (k == p.B3G_UP_ARROW and (v & p.KEY_WAS_TRIGGERED)):
+      forward = 1
+    if (k == p.B3G_UP_ARROW and (v & p.KEY_WAS_RELEASED)):
+      forward = 0
+    if (k == p.B3G_DOWN_ARROW and (v & p.KEY_WAS_TRIGGERED)):
+      forward = -1
+    if (k == p.B3G_DOWN_ARROW and (v & p.KEY_WAS_RELEASED)):
+      forward = 0
+
+  force = [forward * camForward[0], forward * camForward[1], 0]
+  cameraYaw = cameraYaw + turn
+
+  if (forward):
+    p.applyExternalForce(sphere, -1, force, spherePos, flags=p.WORLD_FRAME)
+
+  p.stepSimulation()
+  time.sleep(1. / 240.)
diff --git a/examples/pybullet/examples/fileIOPlugin.py b/examples/pybullet/examples/fileIOPlugin.py
index 99a5bac62..3a51c8ac3 100644
--- a/examples/pybullet/examples/fileIOPlugin.py
+++ b/examples/pybullet/examples/fileIOPlugin.py
@@ -3,18 +3,17 @@ import time
 
 p.connect(p.GUI)
 fileIO = p.loadPlugin("fileIOPlugin")
-if (fileIO>=0):
-	p.executePluginCommand(fileIO, "pickup.zip", [p.AddFileIOAction, p.ZipFileIO])
-	objs= p.loadSDF("pickup/model.sdf")
-	dobot =objs[0]
-	p.changeVisualShape(dobot,-1,rgbaColor=[1,1,1,1])
-		
-else:
-	print("fileIOPlugin is disabled.")
+if (fileIO >= 0):
+  p.executePluginCommand(fileIO, "pickup.zip", [p.AddFileIOAction, p.ZipFileIO])
+  objs = p.loadSDF("pickup/model.sdf")
+  dobot = objs[0]
+  p.changeVisualShape(dobot, -1, rgbaColor=[1, 1, 1, 1])
 
+else:
+  print("fileIOPlugin is disabled.")
 
 p.setPhysicsEngineParameter(enableFileCaching=False)
 
 while (1):
-	p.stepSimulation()
-	time.sleep(1./240.)
\ No newline at end of file
+  p.stepSimulation()
+  time.sleep(1. / 240.)
diff --git a/examples/pybullet/examples/forcetorquesensor.py b/examples/pybullet/examples/forcetorquesensor.py
index 9e0f343a3..5293ab1e1 100644
--- a/examples/pybullet/examples/forcetorquesensor.py
+++ b/examples/pybullet/examples/forcetorquesensor.py
@@ -6,21 +6,20 @@ print("mass of linkA = 1kg, linkB = 1kg, total mass = 2kg")
 hingeJointIndex = 0
 
 #by default, joint motors are enabled, maintaining zero velocity
-p.setJointMotorControl2(hinge,hingeJointIndex,p.VELOCITY_CONTROL,0,0,0)
+p.setJointMotorControl2(hinge, hingeJointIndex, p.VELOCITY_CONTROL, 0, 0, 0)
 
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 p.stepSimulation()
 print("joint state without sensor")
 
-print(p.getJointState(0,0))
-p.enableJointForceTorqueSensor(hinge,hingeJointIndex)
+print(p.getJointState(0, 0))
+p.enableJointForceTorqueSensor(hinge, hingeJointIndex)
 p.stepSimulation()
 print("joint state with force/torque sensor, gravity [0,0,-10]")
-print(p.getJointState(0,0))
-p.setGravity(0,0,0)
+print(p.getJointState(0, 0))
+p.setGravity(0, 0, 0)
 p.stepSimulation()
 print("joint state with force/torque sensor, no gravity")
-print(p.getJointState(0,0))
+print(p.getJointState(0, 0))
 
 p.disconnect()
-
diff --git a/examples/pybullet/examples/frictionCone.py b/examples/pybullet/examples/frictionCone.py
index 96e9883a9..f12a6abd3 100644
--- a/examples/pybullet/examples/frictionCone.py
+++ b/examples/pybullet/examples/frictionCone.py
@@ -5,42 +5,42 @@ import math
 p.connect(p.GUI)
 useMaximalCoordinates = False
 
-p.setGravity(0,0,-10)
-plane=p.loadURDF("plane.urdf",[0,0,-1],useMaximalCoordinates=useMaximalCoordinates)
+p.setGravity(0, 0, -10)
+plane = p.loadURDF("plane.urdf", [0, 0, -1], useMaximalCoordinates=useMaximalCoordinates)
 
 p.setRealTimeSimulation(0)
 
-
 velocity = 1
 num = 40
-p.configureDebugVisualizer(p.COV_ENABLE_GUI,0)
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)#disable this to make it faster
-p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER,0)
+p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)  #disable this to make it faster
+p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER, 0)
 p.setPhysicsEngineParameter(enableConeFriction=1)
 
-for i in range (num):
-	print("progress:",i,num)
-	
-	x = velocity*math.sin(2.*3.1415*float(i)/num)
-	y = velocity*math.cos(2.*3.1415*float(i)/num)
-	print("velocity=",x,y)
-	sphere=p.loadURDF("sphere_small_zeroinertia.urdf", flags=p.URDF_USE_INERTIA_FROM_FILE, useMaximalCoordinates=useMaximalCoordinates)
-	p.changeDynamics(sphere,-1,lateralFriction=0.02)
-	#p.changeDynamics(sphere,-1,rollingFriction=10)
-	p.changeDynamics(sphere,-1,linearDamping=0)
-	p.changeDynamics(sphere,-1,angularDamping=0)
-	p.resetBaseVelocity(sphere,linearVelocity=[x,y,0])
-
-	prevPos = [0,0,0]
-	for i in range (2048):
-		p.stepSimulation()
-		pos = p.getBasePositionAndOrientation(sphere)[0]
-		if (i&64):
-			p.addUserDebugLine(prevPos,pos,[1,0,0],1)
-			prevPos = pos
-
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
-
-	
+for i in range(num):
+  print("progress:", i, num)
+
+  x = velocity * math.sin(2. * 3.1415 * float(i) / num)
+  y = velocity * math.cos(2. * 3.1415 * float(i) / num)
+  print("velocity=", x, y)
+  sphere = p.loadURDF("sphere_small_zeroinertia.urdf",
+                      flags=p.URDF_USE_INERTIA_FROM_FILE,
+                      useMaximalCoordinates=useMaximalCoordinates)
+  p.changeDynamics(sphere, -1, lateralFriction=0.02)
+  #p.changeDynamics(sphere,-1,rollingFriction=10)
+  p.changeDynamics(sphere, -1, linearDamping=0)
+  p.changeDynamics(sphere, -1, angularDamping=0)
+  p.resetBaseVelocity(sphere, linearVelocity=[x, y, 0])
+
+  prevPos = [0, 0, 0]
+  for i in range(2048):
+    p.stepSimulation()
+    pos = p.getBasePositionAndOrientation(sphere)[0]
+    if (i & 64):
+      p.addUserDebugLine(prevPos, pos, [1, 0, 0], 1)
+      prevPos = pos
+
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
+
 while (1):
-	time.sleep(0.01)
\ No newline at end of file
+  time.sleep(0.01)
diff --git a/examples/pybullet/examples/getAABB.py b/examples/pybullet/examples/getAABB.py
index a43c144e1..b80a6fe6d 100644
--- a/examples/pybullet/examples/getAABB.py
+++ b/examples/pybullet/examples/getAABB.py
@@ -1,78 +1,79 @@
 import pybullet as p
-draw=1
+draw = 1
 printtext = 0
 
 if (draw):
-	p.connect(p.GUI)
+  p.connect(p.GUI)
 else:
-	p.connect(p.DIRECT)
+  p.connect(p.DIRECT)
 r2d2 = p.loadURDF("r2d2.urdf")
 
+
 def drawAABB(aabb):
-	f = [aabbMin[0],aabbMin[1],aabbMin[2]]
-	t = [aabbMax[0],aabbMin[1],aabbMin[2]]
-	p.addUserDebugLine(f,t,[1,0,0])
-	f = [aabbMin[0],aabbMin[1],aabbMin[2]]
-	t = [aabbMin[0],aabbMax[1],aabbMin[2]]
-	p.addUserDebugLine(f,t,[0,1,0])
-	f = [aabbMin[0],aabbMin[1],aabbMin[2]]
-	t = [aabbMin[0],aabbMin[1],aabbMax[2]]
-	p.addUserDebugLine(f,t,[0,0,1])
+  f = [aabbMin[0], aabbMin[1], aabbMin[2]]
+  t = [aabbMax[0], aabbMin[1], aabbMin[2]]
+  p.addUserDebugLine(f, t, [1, 0, 0])
+  f = [aabbMin[0], aabbMin[1], aabbMin[2]]
+  t = [aabbMin[0], aabbMax[1], aabbMin[2]]
+  p.addUserDebugLine(f, t, [0, 1, 0])
+  f = [aabbMin[0], aabbMin[1], aabbMin[2]]
+  t = [aabbMin[0], aabbMin[1], aabbMax[2]]
+  p.addUserDebugLine(f, t, [0, 0, 1])
+
+  f = [aabbMin[0], aabbMin[1], aabbMax[2]]
+  t = [aabbMin[0], aabbMax[1], aabbMax[2]]
+  p.addUserDebugLine(f, t, [1, 1, 1])
+
+  f = [aabbMin[0], aabbMin[1], aabbMax[2]]
+  t = [aabbMax[0], aabbMin[1], aabbMax[2]]
+  p.addUserDebugLine(f, t, [1, 1, 1])
 
-	f = [aabbMin[0],aabbMin[1],aabbMax[2]]
-	t = [aabbMin[0],aabbMax[1],aabbMax[2]]
-	p.addUserDebugLine(f,t,[1,1,1])
+  f = [aabbMax[0], aabbMin[1], aabbMin[2]]
+  t = [aabbMax[0], aabbMin[1], aabbMax[2]]
+  p.addUserDebugLine(f, t, [1, 1, 1])
 
-	f = [aabbMin[0],aabbMin[1],aabbMax[2]]
-	t = [aabbMax[0],aabbMin[1],aabbMax[2]]
-	p.addUserDebugLine(f,t,[1,1,1])
+  f = [aabbMax[0], aabbMin[1], aabbMin[2]]
+  t = [aabbMax[0], aabbMax[1], aabbMin[2]]
+  p.addUserDebugLine(f, t, [1, 1, 1])
 
-	f = [aabbMax[0],aabbMin[1],aabbMin[2]]
-	t = [aabbMax[0],aabbMin[1],aabbMax[2]]
-	p.addUserDebugLine(f,t,[1,1,1])
+  f = [aabbMax[0], aabbMax[1], aabbMin[2]]
+  t = [aabbMin[0], aabbMax[1], aabbMin[2]]
+  p.addUserDebugLine(f, t, [1, 1, 1])
+
+  f = [aabbMin[0], aabbMax[1], aabbMin[2]]
+  t = [aabbMin[0], aabbMax[1], aabbMax[2]]
+  p.addUserDebugLine(f, t, [1, 1, 1])
+
+  f = [aabbMax[0], aabbMax[1], aabbMax[2]]
+  t = [aabbMin[0], aabbMax[1], aabbMax[2]]
+  p.addUserDebugLine(f, t, [1.0, 0.5, 0.5])
+  f = [aabbMax[0], aabbMax[1], aabbMax[2]]
+  t = [aabbMax[0], aabbMin[1], aabbMax[2]]
+  p.addUserDebugLine(f, t, [1, 1, 1])
+  f = [aabbMax[0], aabbMax[1], aabbMax[2]]
+  t = [aabbMax[0], aabbMax[1], aabbMin[2]]
+  p.addUserDebugLine(f, t, [1, 1, 1])
 
-	f = [aabbMax[0],aabbMin[1],aabbMin[2]]
-	t = [aabbMax[0],aabbMax[1],aabbMin[2]]
-	p.addUserDebugLine(f,t,[1,1,1])
-	
-	f = [aabbMax[0],aabbMax[1],aabbMin[2]]
-	t = [aabbMin[0],aabbMax[1],aabbMin[2]]
-	p.addUserDebugLine(f,t,[1,1,1])
-	
-	f = [aabbMin[0],aabbMax[1],aabbMin[2]]
-	t = [aabbMin[0],aabbMax[1],aabbMax[2]]
-	p.addUserDebugLine(f,t,[1,1,1])
-	
-	f = [aabbMax[0],aabbMax[1],aabbMax[2]]
-	t = [aabbMin[0],aabbMax[1],aabbMax[2]]
-	p.addUserDebugLine(f,t,[1.0,0.5,0.5])
-	f = [aabbMax[0],aabbMax[1],aabbMax[2]]
-	t = [aabbMax[0],aabbMin[1],aabbMax[2]]
-	p.addUserDebugLine(f,t,[1,1,1])
-	f = [aabbMax[0],aabbMax[1],aabbMax[2]]
-	t = [aabbMax[0],aabbMax[1],aabbMin[2]]
-	p.addUserDebugLine(f,t,[1,1,1])
 
 aabb = p.getAABB(r2d2)
 aabbMin = aabb[0]
 aabbMax = aabb[1]
 if (printtext):
-	print(aabbMin)
-	print(aabbMax)
-if (draw==1):
-	drawAABB(aabb)
-
-for i in range (p.getNumJoints(r2d2)):
-	aabb = p.getAABB(r2d2,i)
-	aabbMin = aabb[0]
-	aabbMax = aabb[1]
-	if (printtext):
-		print(aabbMin)
-		print(aabbMax)
-	if (draw==1):
-		drawAABB(aabb)
+  print(aabbMin)
+  print(aabbMax)
+if (draw == 1):
+  drawAABB(aabb)
 
+for i in range(p.getNumJoints(r2d2)):
+  aabb = p.getAABB(r2d2, i)
+  aabbMin = aabb[0]
+  aabbMax = aabb[1]
+  if (printtext):
+    print(aabbMin)
+    print(aabbMax)
+  if (draw == 1):
+    drawAABB(aabb)
 
-while(1):
-	a=0
-	p.stepSimulation()
+while (1):
+  a = 0
+  p.stepSimulation()
diff --git a/examples/pybullet/examples/getCameraImageTest.py b/examples/pybullet/examples/getCameraImageTest.py
index 290580cb4..c68888577 100644
--- a/examples/pybullet/examples/getCameraImageTest.py
+++ b/examples/pybullet/examples/getCameraImageTest.py
@@ -3,16 +3,14 @@ import numpy as np
 import pybullet as p
 import time
 
-
-direct = p.connect(p.GUI)#, options="--window_backend=2 --render_device=0")
+direct = p.connect(p.GUI)  #, options="--window_backend=2 --render_device=0")
 #egl = p.loadPlugin("eglRendererPlugin")
 
-
 p.loadURDF('plane.urdf')
-p.loadURDF("r2d2.urdf",[0,0,1])
+p.loadURDF("r2d2.urdf", [0, 0, 1])
 p.loadURDF('cube_small.urdf', basePosition=[0.0, 0.0, 0.025])
 cube_trans = p.loadURDF('cube_small.urdf', basePosition=[0.0, 0.1, 0.025])
-p.changeVisualShape(cube_trans,-1,rgbaColor=[1,1,1,0.1])
+p.changeVisualShape(cube_trans, -1, rgbaColor=[1, 1, 1, 0.1])
 width = 128
 height = 128
 
@@ -25,42 +23,71 @@ view_matrix = p.computeViewMatrix([0, 0, 0.5], [0, 0, 0], [1, 0, 0])
 projection_matrix = p.computeProjectionMatrixFOV(fov, aspect, near, far)
 
 # Get depth values using the OpenGL renderer
-images = p.getCameraImage(width, height, view_matrix, projection_matrix, shadow=True,renderer=p.ER_BULLET_HARDWARE_OPENGL)
-rgb_opengl= np.reshape(images[2], (height, width, 4))*1./255.
+images = p.getCameraImage(width,
+                          height,
+                          view_matrix,
+                          projection_matrix,
+                          shadow=True,
+                          renderer=p.ER_BULLET_HARDWARE_OPENGL)
+rgb_opengl = np.reshape(images[2], (height, width, 4)) * 1. / 255.
 depth_buffer_opengl = np.reshape(images[3], [width, height])
 depth_opengl = far * near / (far - (far - near) * depth_buffer_opengl)
-seg_opengl = np.reshape(images[4], [width, height])*1./255.
+seg_opengl = np.reshape(images[4], [width, height]) * 1. / 255.
 time.sleep(1)
 
 # Get depth values using Tiny renderer
-images = p.getCameraImage(width, height, view_matrix, projection_matrix, shadow=True, renderer=p.ER_TINY_RENDERER)
+images = p.getCameraImage(width,
+                          height,
+                          view_matrix,
+                          projection_matrix,
+                          shadow=True,
+                          renderer=p.ER_TINY_RENDERER)
 depth_buffer_tiny = np.reshape(images[3], [width, height])
 depth_tiny = far * near / (far - (far - near) * depth_buffer_tiny)
-rgb_tiny= np.reshape(images[2], (height, width, 4))*1./255.
-seg_tiny = np.reshape(images[4],[width,height])*1./255.
-
+rgb_tiny = np.reshape(images[2], (height, width, 4)) * 1. / 255.
+seg_tiny = np.reshape(images[4], [width, height]) * 1. / 255.
 
-
-bearStartPos1 = [-3.3,0,0]
-bearStartOrientation1 = p.getQuaternionFromEuler([0,0,0])
+bearStartPos1 = [-3.3, 0, 0]
+bearStartOrientation1 = p.getQuaternionFromEuler([0, 0, 0])
 bearId1 = p.loadURDF("plane.urdf", bearStartPos1, bearStartOrientation1)
-bearStartPos2 = [0,0,0]
-bearStartOrientation2 = p.getQuaternionFromEuler([0,0,0])
-bearId2 = p.loadURDF("teddy_large.urdf",bearStartPos2, bearStartOrientation2)
+bearStartPos2 = [0, 0, 0]
+bearStartOrientation2 = p.getQuaternionFromEuler([0, 0, 0])
+bearId2 = p.loadURDF("teddy_large.urdf", bearStartPos2, bearStartOrientation2)
 textureId = p.loadTexture("checker_grid.jpg")
-for b in range (p.getNumBodies()):
-	p.changeVisualShape(b,linkIndex=-1,textureUniqueId=textureId)
-	for j in range(p.getNumJoints(b)):
-		p.changeVisualShape(b,linkIndex=j,textureUniqueId=textureId)
-
-viewMat = [0.642787516117096, -0.4393851161003113, 0.6275069713592529, 0.0, 0.766044557094574, 0.36868777871131897, -0.5265407562255859, 0.0, -0.0, 0.8191521167755127, 0.5735764503479004, 0.0, 2.384185791015625e-07, 2.384185791015625e-07, -5.000000476837158, 1.0]
-projMat = [0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0]
-images = p.getCameraImage(width, height, viewMatrix = viewMat, projectionMatrix = projMat, renderer=p.ER_BULLET_HARDWARE_OPENGL, flags=p.ER_USE_PROJECTIVE_TEXTURE, projectiveTextureView=viewMat, projectiveTextureProj=projMat)
-proj_opengl= np.reshape(images[2], (height, width, 4))*1./255.
+for b in range(p.getNumBodies()):
+  p.changeVisualShape(b, linkIndex=-1, textureUniqueId=textureId)
+  for j in range(p.getNumJoints(b)):
+    p.changeVisualShape(b, linkIndex=j, textureUniqueId=textureId)
+
+viewMat = [
+    0.642787516117096, -0.4393851161003113, 0.6275069713592529, 0.0, 0.766044557094574,
+    0.36868777871131897, -0.5265407562255859, 0.0, -0.0, 0.8191521167755127, 0.5735764503479004,
+    0.0, 2.384185791015625e-07, 2.384185791015625e-07, -5.000000476837158, 1.0
+]
+projMat = [
+    0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0,
+    0.0, 0.0, -0.02000020071864128, 0.0
+]
+images = p.getCameraImage(width,
+                          height,
+                          viewMatrix=viewMat,
+                          projectionMatrix=projMat,
+                          renderer=p.ER_BULLET_HARDWARE_OPENGL,
+                          flags=p.ER_USE_PROJECTIVE_TEXTURE,
+                          projectiveTextureView=viewMat,
+                          projectiveTextureProj=projMat)
+proj_opengl = np.reshape(images[2], (height, width, 4)) * 1. / 255.
 time.sleep(1)
 
-images = p.getCameraImage(width, height, viewMatrix = viewMat, projectionMatrix = projMat, renderer=p.ER_TINY_RENDERER, flags=p.ER_USE_PROJECTIVE_TEXTURE, projectiveTextureView=viewMat, projectiveTextureProj=projMat)
-proj_tiny= np.reshape(images[2], (height, width, 4))*1./255.
+images = p.getCameraImage(width,
+                          height,
+                          viewMatrix=viewMat,
+                          projectionMatrix=projMat,
+                          renderer=p.ER_TINY_RENDERER,
+                          flags=p.ER_USE_PROJECTIVE_TEXTURE,
+                          projectiveTextureView=viewMat,
+                          projectiveTextureProj=projMat)
+proj_tiny = np.reshape(images[2], (height, width, 4)) * 1. / 255.
 
 # Plot both images - should show depth values of 0.45 over the cube and 0.5 over the plane
 plt.subplot(4, 2, 1)
@@ -71,30 +98,29 @@ plt.subplot(4, 2, 2)
 plt.imshow(depth_tiny, cmap='gray', vmin=0, vmax=1)
 plt.title('Depth TinyRenderer')
 
-plt.subplot(4,2,3)
+plt.subplot(4, 2, 3)
 plt.imshow(rgb_opengl)
 plt.title('RGB OpenGL3')
 
-plt.subplot(4,2,4)
+plt.subplot(4, 2, 4)
 plt.imshow(rgb_tiny)
 plt.title('RGB Tiny')
 
-plt.subplot(4,2,5)
+plt.subplot(4, 2, 5)
 plt.imshow(seg_opengl)
 plt.title('Seg OpenGL3')
 
-plt.subplot(4,2,6)
+plt.subplot(4, 2, 6)
 plt.imshow(seg_tiny)
 plt.title('Seg Tiny')
 
-plt.subplot(4,2,7)
+plt.subplot(4, 2, 7)
 plt.imshow(proj_opengl)
 plt.title('Proj OpenGL')
 
-plt.subplot(4,2,8)
+plt.subplot(4, 2, 8)
 plt.imshow(proj_tiny)
 plt.title('Proj Tiny')
 plt.subplots_adjust(hspace=0.7)
 
-
 plt.show()
diff --git a/examples/pybullet/examples/getClosestPoints.py b/examples/pybullet/examples/getClosestPoints.py
index 5db54dbb7..c8db4dfb0 100644
--- a/examples/pybullet/examples/getClosestPoints.py
+++ b/examples/pybullet/examples/getClosestPoints.py
@@ -2,51 +2,68 @@ import pybullet as p
 import time
 p.connect(p.GUI)
 useCollisionShapeQuery = True
-p.configureDebugVisualizer(p.COV_ENABLE_GUI,0)
+p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0)
 geom = p.createCollisionShape(p.GEOM_SPHERE, radius=0.1)
-geomBox = p.createCollisionShape(p.GEOM_BOX, halfExtents=[0.2,0.2,0.2])
-baseOrientationB = p.getQuaternionFromEuler([0,0.3,0])#[0,0.5,0.5,0]
-basePositionB = [1.5,0,1]
-obA=-1
-obB=-1
+geomBox = p.createCollisionShape(p.GEOM_BOX, halfExtents=[0.2, 0.2, 0.2])
+baseOrientationB = p.getQuaternionFromEuler([0, 0.3, 0])  #[0,0.5,0.5,0]
+basePositionB = [1.5, 0, 1]
+obA = -1
+obB = -1
 
-obA = p.createMultiBody(baseMass=0, baseCollisionShapeIndex=geom,basePosition=[0.5,0,1])
-obB = p.createMultiBody(baseMass=0, baseCollisionShapeIndex=geomBox,basePosition=basePositionB,baseOrientation=baseOrientationB )
+obA = p.createMultiBody(baseMass=0, baseCollisionShapeIndex=geom, basePosition=[0.5, 0, 1])
+obB = p.createMultiBody(baseMass=0,
+                        baseCollisionShapeIndex=geomBox,
+                        basePosition=basePositionB,
+                        baseOrientation=baseOrientationB)
 
-
-lineWidth=3
-colorRGB=[1,0,0]
-lineId=p.addUserDebugLine(lineFromXYZ=[0,0,0],lineToXYZ=[0,0,0],lineColorRGB=colorRGB,lineWidth=lineWidth,lifeTime=0)
-pitch=0
-yaw=0
+lineWidth = 3
+colorRGB = [1, 0, 0]
+lineId = p.addUserDebugLine(lineFromXYZ=[0, 0, 0],
+                            lineToXYZ=[0, 0, 0],
+                            lineColorRGB=colorRGB,
+                            lineWidth=lineWidth,
+                            lifeTime=0)
+pitch = 0
+yaw = 0
 
 while (p.isConnected()):
-	pitch += 0.01
-	if (pitch>=3.1415*2.):
-		pitch=0
-	yaw+= 0.01
-	if (yaw>=3.1415*2.):
-		yaw=0
-		
-	baseOrientationB = p.getQuaternionFromEuler([yaw,pitch,0])
-	if (obB>=0):
-		p.resetBasePositionAndOrientation(obB, basePositionB, baseOrientationB)
+  pitch += 0.01
+  if (pitch >= 3.1415 * 2.):
+    pitch = 0
+  yaw += 0.01
+  if (yaw >= 3.1415 * 2.):
+    yaw = 0
+
+  baseOrientationB = p.getQuaternionFromEuler([yaw, pitch, 0])
+  if (obB >= 0):
+    p.resetBasePositionAndOrientation(obB, basePositionB, baseOrientationB)
 
-	if (useCollisionShapeQuery):
-		pts = p.getClosestPoints(bodyA=-1, bodyB=-1, distance=100, collisionShapeA=geom,collisionShapeB=geomBox, collisionShapePositionA=[0.5,0,1],collisionShapePositionB=basePositionB, collisionShapeOrientationB=baseOrientationB)
-		#pts = p.getClosestPoints(bodyA=obA, bodyB=-1, distance=100, collisionShapeB=geomBox, collisionShapePositionB=basePositionB, collisionShapeOrientationB=baseOrientationB)
-	else:
-		pts = p.getClosestPoints(bodyA=obA, bodyB=obB, distance=100)
+  if (useCollisionShapeQuery):
+    pts = p.getClosestPoints(bodyA=-1,
+                             bodyB=-1,
+                             distance=100,
+                             collisionShapeA=geom,
+                             collisionShapeB=geomBox,
+                             collisionShapePositionA=[0.5, 0, 1],
+                             collisionShapePositionB=basePositionB,
+                             collisionShapeOrientationB=baseOrientationB)
+    #pts = p.getClosestPoints(bodyA=obA, bodyB=-1, distance=100, collisionShapeB=geomBox, collisionShapePositionB=basePositionB, collisionShapeOrientationB=baseOrientationB)
+  else:
+    pts = p.getClosestPoints(bodyA=obA, bodyB=obB, distance=100)
 
-	if len(pts)>0:
-		#print(pts)	
-		distance = pts[0][8]
-		#print("distance=",distance)
-		ptA = pts[0][5]
-		ptB = pts[0][6]
-		p.addUserDebugLine(lineFromXYZ=ptA,lineToXYZ=ptB,lineColorRGB=colorRGB,lineWidth=lineWidth,lifeTime=0,replaceItemUniqueId=lineId); 
-	#time.sleep(1./240.)
-	
+  if len(pts) > 0:
+    #print(pts)
+    distance = pts[0][8]
+    #print("distance=",distance)
+    ptA = pts[0][5]
+    ptB = pts[0][6]
+    p.addUserDebugLine(lineFromXYZ=ptA,
+                       lineToXYZ=ptB,
+                       lineColorRGB=colorRGB,
+                       lineWidth=lineWidth,
+                       lifeTime=0,
+                       replaceItemUniqueId=lineId)
+  #time.sleep(1./240.)
 
 #removeCollisionShape is optional:
 #only use removeCollisionShape if the collision shape is not used to create a body
diff --git a/examples/pybullet/examples/getTextureUid.py b/examples/pybullet/examples/getTextureUid.py
index 50eda65e9..e8fbe1462 100644
--- a/examples/pybullet/examples/getTextureUid.py
+++ b/examples/pybullet/examples/getTextureUid.py
@@ -6,14 +6,13 @@ print(visualData)
 curTexUid = visualData[0][8]
 print(curTexUid)
 texUid = p.loadTexture("tex256.png")
-print("texUid=",texUid)
+print("texUid=", texUid)
 
-p.changeVisualShape(plane,-1,textureUniqueId=texUid)
+p.changeVisualShape(plane, -1, textureUniqueId=texUid)
 
-for i in range (100):
-	p.getCameraImage(320,200)
-p.changeVisualShape(plane,-1,textureUniqueId=curTexUid)
-
-for i in range (100):
-	p.getCameraImage(320,200)
+for i in range(100):
+  p.getCameraImage(320, 200)
+p.changeVisualShape(plane, -1, textureUniqueId=curTexUid)
 
+for i in range(100):
+  p.getCameraImage(320, 200)
diff --git a/examples/pybullet/examples/grpcClient.py b/examples/pybullet/examples/grpcClient.py
index cc44d06f1..e28e1112a 100644
--- a/examples/pybullet/examples/grpcClient.py
+++ b/examples/pybullet/examples/grpcClient.py
@@ -1,19 +1,17 @@
-
 import pybullet as p
 
 usePort = True
 
 if (usePort):
-	id = p.connect(p.GRPC,"localhost:12345")
+  id = p.connect(p.GRPC, "localhost:12345")
 else:
-	id = p.connect(p.GRPC,"localhost") 
-print("id=",id)
+  id = p.connect(p.GRPC, "localhost")
+print("id=", id)
+
+if (id < 0):
+  print("Cannot connect to GRPC server")
+  exit(0)
 
-if (id<0):
-	print("Cannot connect to GRPC server")
-	exit(0)
-	
-print ("Connected to GRPC")
+print("Connected to GRPC")
 r2d2 = p.loadURDF("r2d2.urdf")
 print("numJoints = ", p.getNumJoints(r2d2))
-
diff --git a/examples/pybullet/examples/grpcServer.py b/examples/pybullet/examples/grpcServer.py
index a1960714f..7b4e62485 100644
--- a/examples/pybullet/examples/grpcServer.py
+++ b/examples/pybullet/examples/grpcServer.py
@@ -10,20 +10,20 @@ id = p.loadPlugin("grpcPlugin")
 #id = p.loadPlugin("E:/develop/bullet3/bin/pybullet_grpcPlugin_vs2010_x64_debug.dll", postFix="_grpcPlugin")
 
 #start the GRPC server at hostname, port
-if (id<0):
-	print("Cannot load grpcPlugin")
-	exit(0)
-	
+if (id < 0):
+  print("Cannot load grpcPlugin")
+  exit(0)
+
 if usePort:
-	p.executePluginCommand(id, "localhost:12345")
+  p.executePluginCommand(id, "localhost:12345")
 else:
-	p.executePluginCommand(id, "localhost")
+  p.executePluginCommand(id, "localhost")
 
 while p.isConnected():
-	if (useDirect):
-		#Only in DIRECT mode, since there is no 'ping' you need to manually call to handle RCPs:
-		numRPC = 10
-		p.executePluginCommand(id, intArgs=[numRPC])
-	else:
-		dt = 1./240.
-		time.sleep(dt)
+  if (useDirect):
+    #Only in DIRECT mode, since there is no 'ping' you need to manually call to handle RCPs:
+    numRPC = 10
+    p.executePluginCommand(id, intArgs=[numRPC])
+  else:
+    dt = 1. / 240.
+    time.sleep(dt)
diff --git a/examples/pybullet/examples/hand.py b/examples/pybullet/examples/hand.py
index 277e8ec79..5f1b0793a 100644
--- a/examples/pybullet/examples/hand.py
+++ b/examples/pybullet/examples/hand.py
@@ -12,18 +12,18 @@ import pybullet as p
 #first try to connect to shared memory (VR), if it fails use local GUI
 c = p.connect(p.SHARED_MEMORY)
 print(c)
-if (c<0):
-		p.connect(p.GUI)
-		
+if (c < 0):
+  p.connect(p.GUI)
+
 #load the MuJoCo MJCF hand
 objects = p.loadMJCF("MPL/MPL.xml")
 
-hand=objects[0]
+hand = objects[0]
 #clamp in range 400-600
 #minV = 400
 #maxV = 600
-minVarray = [275,280,350,290]
-maxVarray = [450,550,500,400]
+minVarray = [275, 280, 350, 290]
+maxVarray = [450, 550, 500, 400]
 
 pinkId = 0
 middleId = 1
@@ -32,82 +32,93 @@ thumbId = 3
 
 p.setRealTimeSimulation(1)
 
+
 def getSerialOrNone(portname):
-    try:
-       return serial.Serial(port=portname,baudrate=115200,parity=serial.PARITY_ODD,stopbits=serial.STOPBITS_TWO,bytesize=serial.SEVENBITS)
-    except:
-       return None
+  try:
+    return serial.Serial(port=portname,
+                         baudrate=115200,
+                         parity=serial.PARITY_ODD,
+                         stopbits=serial.STOPBITS_TWO,
+                         bytesize=serial.SEVENBITS)
+  except:
+    return None
+
 
 def convertSensor(x, fingerIndex):
-	minV = minVarray[fingerIndex]
-	maxV = maxVarray[fingerIndex]
-	
-	v = minV
-	try:
-		v = float(x)
-	except ValueError:
-		v = minV    
-	if (v<minV):
-		v=minV
-	if (v>maxV):
-		v=maxV
-	b = (v-minV)/float(maxV-minV)
-	return (b)
+  minV = minVarray[fingerIndex]
+  maxV = maxVarray[fingerIndex]
+
+  v = minV
+  try:
+    v = float(x)
+  except ValueError:
+    v = minV
+  if (v < minV):
+    v = minV
+  if (v > maxV):
+    v = maxV
+  b = (v - minV) / float(maxV - minV)
+  return (b)
+
 
 ser = None
 portindex = 0
 while (ser is None and portindex < 30):
-	portname = 'COM'+str(portindex)
-	print(portname)
-	ser = getSerialOrNone(portname)
-	if (ser is None):
-		portname = "/dev/cu.usbmodem14"+str(portindex)
-		print(portname)
-		ser = getSerialOrNone(portname)
-		if (ser is not None):
-			print("COnnected!")
-	portindex = portindex+1
+  portname = 'COM' + str(portindex)
+  print(portname)
+  ser = getSerialOrNone(portname)
+  if (ser is None):
+    portname = "/dev/cu.usbmodem14" + str(portindex)
+    print(portname)
+    ser = getSerialOrNone(portname)
+    if (ser is not None):
+      print("COnnected!")
+  portindex = portindex + 1
 
 if (ser is None):
-	ser = serial.Serial(port = "/dev/cu.usbmodem1421",baudrate=115200,parity=serial.PARITY_ODD,stopbits=serial.STOPBITS_TWO,bytesize=serial.SEVENBITS)
-pi=3.141592
+  ser = serial.Serial(port="/dev/cu.usbmodem1421",
+                      baudrate=115200,
+                      parity=serial.PARITY_ODD,
+                      stopbits=serial.STOPBITS_TWO,
+                      bytesize=serial.SEVENBITS)
+pi = 3.141592
 
 if (ser is not None and ser.isOpen()):
-	while True:
-		while ser.inWaiting() > 0:
-			line = str(ser.readline())
-			words = line.split(",")
-			if (len(words)==6):
-				pink = convertSensor(words[1],pinkId)
-				middle = convertSensor(words[2],middleId)
-				index = convertSensor(words[3],indexId)
-				thumb = convertSensor(words[4],thumbId)
-			
-				p.setJointMotorControl2(hand,7,p.POSITION_CONTROL,pi/4.)	
-				p.setJointMotorControl2(hand,9,p.POSITION_CONTROL,thumb+pi/10)
-				p.setJointMotorControl2(hand,11,p.POSITION_CONTROL,thumb)
-				p.setJointMotorControl2(hand,13,p.POSITION_CONTROL,thumb)
-				
-				p.setJointMotorControl2(hand,17,p.POSITION_CONTROL,index)
-				p.setJointMotorControl2(hand,19,p.POSITION_CONTROL,index)
-				p.setJointMotorControl2(hand,21,p.POSITION_CONTROL,index)
-
-				p.setJointMotorControl2(hand,24,p.POSITION_CONTROL,middle)
-				p.setJointMotorControl2(hand,26,p.POSITION_CONTROL,middle)
-				p.setJointMotorControl2(hand,28,p.POSITION_CONTROL,middle)
-				
-				p.setJointMotorControl2(hand,40,p.POSITION_CONTROL,pink)
-				p.setJointMotorControl2(hand,42,p.POSITION_CONTROL,pink)
-				p.setJointMotorControl2(hand,44,p.POSITION_CONTROL,pink)
-
-				ringpos = 0.5*(pink+middle)
-				p.setJointMotorControl2(hand,32,p.POSITION_CONTROL,ringpos)
-				p.setJointMotorControl2(hand,34,p.POSITION_CONTROL,ringpos)
-				p.setJointMotorControl2(hand,36,p.POSITION_CONTROL,ringpos)
-								
-				#print(middle)
-				#print(pink)
-				#print(index)
-				#print(thumb)
+  while True:
+    while ser.inWaiting() > 0:
+      line = str(ser.readline())
+      words = line.split(",")
+      if (len(words) == 6):
+        pink = convertSensor(words[1], pinkId)
+        middle = convertSensor(words[2], middleId)
+        index = convertSensor(words[3], indexId)
+        thumb = convertSensor(words[4], thumbId)
+
+        p.setJointMotorControl2(hand, 7, p.POSITION_CONTROL, pi / 4.)
+        p.setJointMotorControl2(hand, 9, p.POSITION_CONTROL, thumb + pi / 10)
+        p.setJointMotorControl2(hand, 11, p.POSITION_CONTROL, thumb)
+        p.setJointMotorControl2(hand, 13, p.POSITION_CONTROL, thumb)
+
+        p.setJointMotorControl2(hand, 17, p.POSITION_CONTROL, index)
+        p.setJointMotorControl2(hand, 19, p.POSITION_CONTROL, index)
+        p.setJointMotorControl2(hand, 21, p.POSITION_CONTROL, index)
+
+        p.setJointMotorControl2(hand, 24, p.POSITION_CONTROL, middle)
+        p.setJointMotorControl2(hand, 26, p.POSITION_CONTROL, middle)
+        p.setJointMotorControl2(hand, 28, p.POSITION_CONTROL, middle)
+
+        p.setJointMotorControl2(hand, 40, p.POSITION_CONTROL, pink)
+        p.setJointMotorControl2(hand, 42, p.POSITION_CONTROL, pink)
+        p.setJointMotorControl2(hand, 44, p.POSITION_CONTROL, pink)
+
+        ringpos = 0.5 * (pink + middle)
+        p.setJointMotorControl2(hand, 32, p.POSITION_CONTROL, ringpos)
+        p.setJointMotorControl2(hand, 34, p.POSITION_CONTROL, ringpos)
+        p.setJointMotorControl2(hand, 36, p.POSITION_CONTROL, ringpos)
+
+        #print(middle)
+        #print(pink)
+        #print(index)
+        #print(thumb)
 else:
-	print("Cannot find port")
+  print("Cannot find port")
diff --git a/examples/pybullet/examples/hello_pybullet.py b/examples/pybullet/examples/hello_pybullet.py
index 28cd0a049..389f2dc67 100644
--- a/examples/pybullet/examples/hello_pybullet.py
+++ b/examples/pybullet/examples/hello_pybullet.py
@@ -3,21 +3,21 @@ from time import sleep
 
 physicsClient = p.connect(p.GUI)
 
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 planeId = p.loadURDF("plane.urdf")
-cubeStartPos = [0,0,1]
-cubeStartOrientation = p.getQuaternionFromEuler([0,0,0])
-boxId = p.loadURDF("r2d2.urdf",cubeStartPos, cubeStartOrientation)
+cubeStartPos = [0, 0, 1]
+cubeStartOrientation = p.getQuaternionFromEuler([0, 0, 0])
+boxId = p.loadURDF("r2d2.urdf", cubeStartPos, cubeStartOrientation)
 cubePos, cubeOrn = p.getBasePositionAndOrientation(boxId)
 
 useRealTimeSimulation = 0
 
 if (useRealTimeSimulation):
-	p.setRealTimeSimulation(1)
+  p.setRealTimeSimulation(1)
 
 while 1:
-	if (useRealTimeSimulation):
-		p.setGravity(0,0,-10)
-		sleep(0.01) # Time in seconds.
-	else:
-		p.stepSimulation()
\ No newline at end of file
+  if (useRealTimeSimulation):
+    p.setGravity(0, 0, -10)
+    sleep(0.01)  # Time in seconds.
+  else:
+    p.stepSimulation()
diff --git a/examples/pybullet/examples/humanoidMotionCapture.py b/examples/pybullet/examples/humanoidMotionCapture.py
index 852fcda4f..fc3fa9a7c 100644
--- a/examples/pybullet/examples/humanoidMotionCapture.py
+++ b/examples/pybullet/examples/humanoidMotionCapture.py
@@ -1,182 +1,233 @@
-import pybullet	as p
+import pybullet as p
 import json
 import time
 
 useGUI = True
 if useGUI:
-	p.connect(p.GUI)
+  p.connect(p.GUI)
 else:
-	p.connect(p.DIRECT)
+  p.connect(p.DIRECT)
 
 useZUp = False
 useYUp = not useZUp
-showJointMotorTorques	=	False
+showJointMotorTorques = False
 
 if useYUp:
-	p.configureDebugVisualizer(p.COV_ENABLE_Y_AXIS_UP	,	1)
+  p.configureDebugVisualizer(p.COV_ENABLE_Y_AXIS_UP, 1)
 
-from pdControllerExplicit	import PDControllerExplicitMultiDof
-from pdControllerStable	import PDControllerStableMultiDof
+from pdControllerExplicit import PDControllerExplicitMultiDof
+from pdControllerStable import PDControllerStableMultiDof
 
 explicitPD = PDControllerExplicitMultiDof(p)
 stablePD = PDControllerStableMultiDof(p)
 
-p.resetDebugVisualizerCamera(cameraDistance=7, cameraYaw=-94,	cameraPitch=-14, cameraTargetPosition=[0.31,0.03,-1.16])
+p.resetDebugVisualizerCamera(cameraDistance=7,
+                             cameraYaw=-94,
+                             cameraPitch=-14,
+                             cameraTargetPosition=[0.31, 0.03, -1.16])
 
 import pybullet_data
 p.setTimeOut(10000)
-useMotionCapture=False
-useMotionCaptureReset=False#not	useMotionCapture
-useExplicitPD	=	True
+useMotionCapture = False
+useMotionCaptureReset = False  #not	useMotionCapture
+useExplicitPD = True
 
 p.setAdditionalSearchPath(pybullet_data.getDataPath())
 p.setPhysicsEngineParameter(numSolverIterations=30)
 #p.setPhysicsEngineParameter(solverResidualThreshold=1e-30)
 
 #explicit	PD control requires	small	timestep
-timeStep = 1./600.
+timeStep = 1. / 600.
 #timeStep	=	1./240.
 
 p.setPhysicsEngineParameter(fixedTimeStep=timeStep)
 
-path = pybullet_data.getDataPath()+"/data/motions/humanoid3d_backflip.txt"
+path = pybullet_data.getDataPath() + "/data/motions/humanoid3d_backflip.txt"
 #path	=	pybullet_data.getDataPath()+"/data/motions/humanoid3d_cartwheel.txt"
 #path	=	pybullet_data.getDataPath()+"/data/motions/humanoid3d_walk.txt"
 
-
 #p.loadURDF("plane.urdf",[0,0,-1.03])
 print("path	=	", path)
-with open(path,	'r') as	f:
-		motion_dict	=	json.load(f)
+with open(path, 'r') as f:
+  motion_dict = json.load(f)
 #print("motion_dict	=	", motion_dict)
 print("len motion=", len(motion_dict))
 print(motion_dict['Loop'])
-numFrames	=	len(motion_dict['Frames'])
-print("#frames = ",	numFrames)
-
-
-frameId= p.addUserDebugParameter("frame",0,numFrames-1,0)
-
-
-erpId	=	p.addUserDebugParameter("erp",0,1,0.2)
-
-kpMotorId	=	p.addUserDebugParameter("kpMotor",0,1,.2)
-forceMotorId = p.addUserDebugParameter("forceMotor",0,2000,1000)
-
-
-
-
-jointTypes = ["JOINT_REVOLUTE","JOINT_PRISMATIC",
-							"JOINT_SPHERICAL","JOINT_PLANAR","JOINT_FIXED"]
-							
-startLocations=[[0,0,2],[0,0,0],[0,0,-2],[0,0,-4]]
-
-p.addUserDebugText("Stable PD",	[startLocations[0][0],startLocations[0][1]+1,startLocations[0][2]],	[0,0,0])
-p.addUserDebugText("Spherical	Drive",	[startLocations[1][0],startLocations[1][1]+1,startLocations[1][2]],	[0,0,0])
-p.addUserDebugText("Explicit PD",	[startLocations[2][0],startLocations[2][1]+1,startLocations[2][2]],	[0,0,0])
-p.addUserDebugText("Kinematic",	[startLocations[3][0],startLocations[3][1]+1,startLocations[3][2]],	[0,0,0])
-
-humanoid =	p.loadURDF("humanoid/humanoid.urdf", startLocations[0],globalScaling=0.25, useFixedBase=False, flags=p.URDF_MAINTAIN_LINK_ORDER)
-humanoid2	=	p.loadURDF("humanoid/humanoid.urdf", startLocations[1],globalScaling=0.25, useFixedBase=False, flags=p.URDF_MAINTAIN_LINK_ORDER)
-humanoid3	=	p.loadURDF("humanoid/humanoid.urdf", startLocations[2],globalScaling=0.25, useFixedBase=False, flags=p.URDF_MAINTAIN_LINK_ORDER)
-humanoid4	=	p.loadURDF("humanoid/humanoid.urdf", startLocations[3],globalScaling=0.25, useFixedBase=False, flags=p.URDF_MAINTAIN_LINK_ORDER)
-
-humanoid_fix = p.createConstraint(humanoid,-1,-1,-1,p.JOINT_FIXED,[0,0,0],[0,0,0],startLocations[0],[0,0,0,1])
-humanoid2_fix	=	p.createConstraint(humanoid2,-1,-1,-1,p.JOINT_FIXED,[0,0,0],[0,0,0],startLocations[1],[0,0,0,1])
-humanoid3_fix	=	p.createConstraint(humanoid3,-1,-1,-1,p.JOINT_FIXED,[0,0,0],[0,0,0],startLocations[2],[0,0,0,1])
-humanoid3_fix	=	p.createConstraint(humanoid4,-1,-1,-1,p.JOINT_FIXED,[0,0,0],[0,0,0],startLocations[3],[0,0,0,1])
-
-
-startPose	=	[2,0.847532,0,0.9986781045,0.01410400148,-0.0006980000731,-0.04942300517,0.9988133229,0.009485003066,-0.04756001538,-0.004475001447,
-1,0,0,0,0.9649395871,0.02436898957,-0.05755497537,0.2549218909,-0.249116,0.9993661511,0.009952001505,0.03265400494,0.01009800153,
-0.9854981188,-0.06440700776,0.09324301124,-0.1262970152,0.170571,0.9927545808,-0.02090099117,0.08882396249,-0.07817796699,-0.391532,0.9828788495,
-0.1013909845,-0.05515999155,0.143618978,0.9659421276,0.1884590249,-0.1422460188,0.105854014,0.581348]
-
-startVel = [1.235314324,-0.008525509087,0.1515293946,-1.161516553,0.1866449799,-0.1050802848,0,0.935706195,0.08277326387,0.3002461862,0,0,0,0,0,1.114409628,0.3618553952,
--0.4505575061,0,-1.725374735,-0.5052852598,-0.8555179722,-0.2221173515,0,-0.1837617357,0.00171895706,0.03912837591,0,0.147945294,1.837653345,0.1534535548,1.491385941,0,
--4.632454387,-0.9111172777,-1.300648184,-1.345694622,0,-1.084238535,0.1313680236,-0.7236998534,0,-0.5278312973]
-
-p.resetBasePositionAndOrientation(humanoid,	startLocations[0],[0,0,0,1])
-p.resetBasePositionAndOrientation(humanoid2, startLocations[1],[0,0,0,1])
-p.resetBasePositionAndOrientation(humanoid3, startLocations[2],[0,0,0,1])
-p.resetBasePositionAndOrientation(humanoid4, startLocations[3],[0,0,0,1])
-
-
-
-index0=7
-for	j	in range (p.getNumJoints(humanoid)):
-	ji = p.getJointInfo(humanoid,j)
-	targetPosition=[0]
-	jointType	=	ji[2]
-	if (jointType	== p.JOINT_SPHERICAL):
-		targetPosition=[startPose[index0+1],startPose[index0+2],startPose[index0+3],startPose[index0+0]]
-		targetVel	=	[startVel[index0+0],startVel[index0+1],startVel[index0+2]]
-		index0+=4
-		print("spherical position: ",targetPosition)
-		print("spherical velocity: ",targetVel)
-		p.resetJointStateMultiDof(humanoid,j,targetValue=targetPosition,targetVelocity=targetVel)
-		p.resetJointStateMultiDof(humanoid2,j,targetValue=targetPosition,targetVelocity=targetVel)
-	if (jointType==p.JOINT_PRISMATIC or	jointType==p.JOINT_REVOLUTE):
-		targetPosition=[startPose[index0]]
-		targetVel	=	[startVel[index0]]
-		index0+=1
-		print("revolute:", targetPosition)
-		print("revolute	velocity:",	targetVel)
-		p.resetJointStateMultiDof(humanoid,j,targetValue=targetPosition,targetVelocity=targetVel)
-		p.resetJointStateMultiDof(humanoid2,j,targetValue=targetPosition,targetVelocity=targetVel)
-	
-
-
-
-for	j	in range (p.getNumJoints(humanoid)):
-	ji = p.getJointInfo(humanoid,j)
-	targetPosition=[0]
-	jointType	=	ji[2]
-	if (jointType	== p.JOINT_SPHERICAL):
-		targetPosition=[0,0,0,1]
-		p.setJointMotorControlMultiDof(humanoid,j,p.POSITION_CONTROL,targetPosition, targetVelocity=[0,0,0], positionGain=0,velocityGain=1,force=[0,0,0])
-		p.setJointMotorControlMultiDof(humanoid3,j,p.POSITION_CONTROL,targetPosition,	targetVelocity=[0,0,0],	positionGain=0,velocityGain=1,force=[31,31,31])
-		p.setJointMotorControlMultiDof(humanoid4,j,p.POSITION_CONTROL,targetPosition,	targetVelocity=[0,0,0],	positionGain=0,velocityGain=1,force=[1,1,1])
-
-	if (jointType==p.JOINT_PRISMATIC or	jointType==p.JOINT_REVOLUTE):
-		p.setJointMotorControl2(humanoid,j,p.VELOCITY_CONTROL,targetVelocity=0,	force=0)
-		p.setJointMotorControl2(humanoid3,j,p.VELOCITY_CONTROL,targetVelocity=0, force=31)
-		p.setJointMotorControl2(humanoid4,j,p.VELOCITY_CONTROL,targetVelocity=0, force=10)
-	
-	#print(ji)
-	print("joint[",j,"].type=",jointTypes[ji[2]])
-	print("joint[",j,"].name=",ji[1])
-
-
-	
-jointIds=[]
-paramIds=[]
-for	j	in range (p.getNumJoints(humanoid)):
-	#p.changeDynamics(humanoid,j,linearDamping=0,	angularDamping=0)
-	p.changeVisualShape(humanoid,j,rgbaColor=[1,1,1,1])
-	info = p.getJointInfo(humanoid,j)
-	#print(info)
-	if (not	useMotionCapture):
-		jointName	=	info[1]
-		jointType	=	info[2]
-		if (jointType==p.JOINT_PRISMATIC or	jointType==p.JOINT_REVOLUTE):
-			jointIds.append(j)
-			#paramIds.append(p.addUserDebugParameter(jointName.decode("utf-8"),-4,4,0))
-			#print("jointName=",jointName, "at ",	j)
-		
-p.changeVisualShape(humanoid,2,rgbaColor=[1,0,0,1])
-chest=1
-neck=2
-rightHip=3
-rightKnee=4
-rightAnkle=5
-rightShoulder=6
-rightElbow=7
-leftHip=9
-leftKnee=10
-leftAnkle=11
-leftShoulder=12
-leftElbow=13
+numFrames = len(motion_dict['Frames'])
+print("#frames = ", numFrames)
+
+frameId = p.addUserDebugParameter("frame", 0, numFrames - 1, 0)
+
+erpId = p.addUserDebugParameter("erp", 0, 1, 0.2)
+
+kpMotorId = p.addUserDebugParameter("kpMotor", 0, 1, .2)
+forceMotorId = p.addUserDebugParameter("forceMotor", 0, 2000, 1000)
+
+jointTypes = [
+    "JOINT_REVOLUTE", "JOINT_PRISMATIC", "JOINT_SPHERICAL", "JOINT_PLANAR", "JOINT_FIXED"
+]
+
+startLocations = [[0, 0, 2], [0, 0, 0], [0, 0, -2], [0, 0, -4]]
+
+p.addUserDebugText("Stable PD",
+                   [startLocations[0][0], startLocations[0][1] + 1, startLocations[0][2]],
+                   [0, 0, 0])
+p.addUserDebugText("Spherical	Drive",
+                   [startLocations[1][0], startLocations[1][1] + 1, startLocations[1][2]],
+                   [0, 0, 0])
+p.addUserDebugText("Explicit PD",
+                   [startLocations[2][0], startLocations[2][1] + 1, startLocations[2][2]],
+                   [0, 0, 0])
+p.addUserDebugText("Kinematic",
+                   [startLocations[3][0], startLocations[3][1] + 1, startLocations[3][2]],
+                   [0, 0, 0])
+
+humanoid = p.loadURDF("humanoid/humanoid.urdf",
+                      startLocations[0],
+                      globalScaling=0.25,
+                      useFixedBase=False,
+                      flags=p.URDF_MAINTAIN_LINK_ORDER)
+humanoid2 = p.loadURDF("humanoid/humanoid.urdf",
+                       startLocations[1],
+                       globalScaling=0.25,
+                       useFixedBase=False,
+                       flags=p.URDF_MAINTAIN_LINK_ORDER)
+humanoid3 = p.loadURDF("humanoid/humanoid.urdf",
+                       startLocations[2],
+                       globalScaling=0.25,
+                       useFixedBase=False,
+                       flags=p.URDF_MAINTAIN_LINK_ORDER)
+humanoid4 = p.loadURDF("humanoid/humanoid.urdf",
+                       startLocations[3],
+                       globalScaling=0.25,
+                       useFixedBase=False,
+                       flags=p.URDF_MAINTAIN_LINK_ORDER)
+
+humanoid_fix = p.createConstraint(humanoid, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0],
+                                  startLocations[0], [0, 0, 0, 1])
+humanoid2_fix = p.createConstraint(humanoid2, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0],
+                                   startLocations[1], [0, 0, 0, 1])
+humanoid3_fix = p.createConstraint(humanoid3, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0],
+                                   startLocations[2], [0, 0, 0, 1])
+humanoid3_fix = p.createConstraint(humanoid4, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0],
+                                   startLocations[3], [0, 0, 0, 1])
+
+startPose = [
+    2, 0.847532, 0, 0.9986781045, 0.01410400148, -0.0006980000731, -0.04942300517, 0.9988133229,
+    0.009485003066, -0.04756001538, -0.004475001447, 1, 0, 0, 0, 0.9649395871, 0.02436898957,
+    -0.05755497537, 0.2549218909, -0.249116, 0.9993661511, 0.009952001505, 0.03265400494,
+    0.01009800153, 0.9854981188, -0.06440700776, 0.09324301124, -0.1262970152, 0.170571,
+    0.9927545808, -0.02090099117, 0.08882396249, -0.07817796699, -0.391532, 0.9828788495,
+    0.1013909845, -0.05515999155, 0.143618978, 0.9659421276, 0.1884590249, -0.1422460188,
+    0.105854014, 0.581348
+]
+
+startVel = [
+    1.235314324, -0.008525509087, 0.1515293946, -1.161516553, 0.1866449799, -0.1050802848, 0,
+    0.935706195, 0.08277326387, 0.3002461862, 0, 0, 0, 0, 0, 1.114409628, 0.3618553952,
+    -0.4505575061, 0, -1.725374735, -0.5052852598, -0.8555179722, -0.2221173515, 0, -0.1837617357,
+    0.00171895706, 0.03912837591, 0, 0.147945294, 1.837653345, 0.1534535548, 1.491385941, 0,
+    -4.632454387, -0.9111172777, -1.300648184, -1.345694622, 0, -1.084238535, 0.1313680236,
+    -0.7236998534, 0, -0.5278312973
+]
+
+p.resetBasePositionAndOrientation(humanoid, startLocations[0], [0, 0, 0, 1])
+p.resetBasePositionAndOrientation(humanoid2, startLocations[1], [0, 0, 0, 1])
+p.resetBasePositionAndOrientation(humanoid3, startLocations[2], [0, 0, 0, 1])
+p.resetBasePositionAndOrientation(humanoid4, startLocations[3], [0, 0, 0, 1])
+
+index0 = 7
+for j in range(p.getNumJoints(humanoid)):
+  ji = p.getJointInfo(humanoid, j)
+  targetPosition = [0]
+  jointType = ji[2]
+  if (jointType == p.JOINT_SPHERICAL):
+    targetPosition = [
+        startPose[index0 + 1], startPose[index0 + 2], startPose[index0 + 3], startPose[index0 + 0]
+    ]
+    targetVel = [startVel[index0 + 0], startVel[index0 + 1], startVel[index0 + 2]]
+    index0 += 4
+    print("spherical position: ", targetPosition)
+    print("spherical velocity: ", targetVel)
+    p.resetJointStateMultiDof(humanoid, j, targetValue=targetPosition, targetVelocity=targetVel)
+    p.resetJointStateMultiDof(humanoid2, j, targetValue=targetPosition, targetVelocity=targetVel)
+  if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE):
+    targetPosition = [startPose[index0]]
+    targetVel = [startVel[index0]]
+    index0 += 1
+    print("revolute:", targetPosition)
+    print("revolute	velocity:", targetVel)
+    p.resetJointStateMultiDof(humanoid, j, targetValue=targetPosition, targetVelocity=targetVel)
+    p.resetJointStateMultiDof(humanoid2, j, targetValue=targetPosition, targetVelocity=targetVel)
+
+for j in range(p.getNumJoints(humanoid)):
+  ji = p.getJointInfo(humanoid, j)
+  targetPosition = [0]
+  jointType = ji[2]
+  if (jointType == p.JOINT_SPHERICAL):
+    targetPosition = [0, 0, 0, 1]
+    p.setJointMotorControlMultiDof(humanoid,
+                                   j,
+                                   p.POSITION_CONTROL,
+                                   targetPosition,
+                                   targetVelocity=[0, 0, 0],
+                                   positionGain=0,
+                                   velocityGain=1,
+                                   force=[0, 0, 0])
+    p.setJointMotorControlMultiDof(humanoid3,
+                                   j,
+                                   p.POSITION_CONTROL,
+                                   targetPosition,
+                                   targetVelocity=[0, 0, 0],
+                                   positionGain=0,
+                                   velocityGain=1,
+                                   force=[31, 31, 31])
+    p.setJointMotorControlMultiDof(humanoid4,
+                                   j,
+                                   p.POSITION_CONTROL,
+                                   targetPosition,
+                                   targetVelocity=[0, 0, 0],
+                                   positionGain=0,
+                                   velocityGain=1,
+                                   force=[1, 1, 1])
+
+  if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE):
+    p.setJointMotorControl2(humanoid, j, p.VELOCITY_CONTROL, targetVelocity=0, force=0)
+    p.setJointMotorControl2(humanoid3, j, p.VELOCITY_CONTROL, targetVelocity=0, force=31)
+    p.setJointMotorControl2(humanoid4, j, p.VELOCITY_CONTROL, targetVelocity=0, force=10)
+
+  #print(ji)
+  print("joint[", j, "].type=", jointTypes[ji[2]])
+  print("joint[", j, "].name=", ji[1])
+
+jointIds = []
+paramIds = []
+for j in range(p.getNumJoints(humanoid)):
+  #p.changeDynamics(humanoid,j,linearDamping=0,	angularDamping=0)
+  p.changeVisualShape(humanoid, j, rgbaColor=[1, 1, 1, 1])
+  info = p.getJointInfo(humanoid, j)
+  #print(info)
+  if (not useMotionCapture):
+    jointName = info[1]
+    jointType = info[2]
+    if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE):
+      jointIds.append(j)
+      #paramIds.append(p.addUserDebugParameter(jointName.decode("utf-8"),-4,4,0))
+      #print("jointName=",jointName, "at ",	j)
+
+p.changeVisualShape(humanoid, 2, rgbaColor=[1, 0, 0, 1])
+chest = 1
+neck = 2
+rightHip = 3
+rightKnee = 4
+rightAnkle = 5
+rightShoulder = 6
+rightElbow = 7
+leftHip = 9
+leftKnee = 10
+leftAnkle = 11
+leftShoulder = 12
+leftElbow = 13
 
 #rightShoulder=3
 #rightElbow=4
@@ -191,249 +242,320 @@ leftElbow=13
 
 import time
 
-
-kpOrg	=	[0,0,0,0,0,0,0,1000,1000,1000,1000,100,100,100,100,500,500,500,500,500,400,400,400,400,400,400,400,400,300,500,500,500,500,500,400,400,400,400,400,400,400,400,300]
-kdOrg	=	[0,0,0,0,0,0,0,100,100,100,100,10,10,10,10,50,50,50,50,50,40,40,40,40,40,40,40,40,30,50,50,50,50,50,40,40,40,40,40,40,40,40,30]
-
-once=True
-p.getCameraImage(320,200)
-
-
-
-
-while	(p.isConnected()):
-
-	if useGUI:	
-		erp	=	p.readUserDebugParameter(erpId)
-		kpMotor	=	p.readUserDebugParameter(kpMotorId)
-		maxForce=p.readUserDebugParameter(forceMotorId)
-		frameReal	=	p.readUserDebugParameter(frameId)
-	else:
-		erp	=	0.2
-		kpMotor	=	0.2
-		maxForce=1000
-		frameReal	=	0
-
-	kp=kpMotor
-	
-	frame	=	int(frameReal)
-	frameNext	=	frame+1
-	if (frameNext	>=	numFrames):
-		frameNext	=	frame
-	
-	frameFraction	=	frameReal	-	frame
-	#print("frameFraction=",frameFraction)
-	#print("frame=",frame)
-	#print("frameNext=", frameNext)
-	
-	#getQuaternionSlerp
-	
-	frameData	=	motion_dict['Frames'][frame]
-	frameDataNext	=	motion_dict['Frames'][frameNext]
-	
-	#print("duration=",frameData[0])
-	#print(pos=[frameData])
-	
-	basePos1Start	=	[frameData[1],frameData[2],frameData[3]]
-	basePos1End	=	[frameDataNext[1],frameDataNext[2],frameDataNext[3]]
-	basePos1 = [basePos1Start[0]+frameFraction*(basePos1End[0]-basePos1Start[0]),	
-		basePos1Start[1]+frameFraction*(basePos1End[1]-basePos1Start[1]),	
-		basePos1Start[2]+frameFraction*(basePos1End[2]-basePos1Start[2])]
-	baseOrn1Start	=	[frameData[5],frameData[6],	frameData[7],frameData[4]]
-	baseOrn1Next = [frameDataNext[5],frameDataNext[6], frameDataNext[7],frameDataNext[4]]
-	baseOrn1 = p.getQuaternionSlerp(baseOrn1Start,baseOrn1Next,frameFraction)
-	#pre-rotate	to make	z-up
-	if (useZUp):
-		y2zPos=[0,0,0.0]
-		y2zOrn = p.getQuaternionFromEuler([1.57,0,0])
-		basePos,baseOrn	=	p.multiplyTransforms(y2zPos, y2zOrn,basePos1,baseOrn1)
-		p.resetBasePositionAndOrientation(humanoid,	basePos,baseOrn)
-		
-		y2zPos=[0,2,0.0]
-		y2zOrn = p.getQuaternionFromEuler([1.57,0,0])
-		basePos,baseOrn	=	p.multiplyTransforms(y2zPos, y2zOrn,basePos1,baseOrn1)
-		p.resetBasePositionAndOrientation(humanoid2, basePos,baseOrn)
-	
-	
-	chestRotStart	=	[frameData[9],frameData[10],frameData[11],frameData[8]]
-	chestRotEnd	=	[frameDataNext[9],frameDataNext[10],frameDataNext[11],frameDataNext[8]]
-	chestRot = p.getQuaternionSlerp(chestRotStart,chestRotEnd,frameFraction)
-	
-	neckRotStart = [frameData[13],frameData[14],frameData[15],frameData[12]]
-	neckRotEnd=	[frameDataNext[13],frameDataNext[14],frameDataNext[15],frameDataNext[12]]
-	neckRot	=	 p.getQuaternionSlerp(neckRotStart,neckRotEnd,frameFraction)
-	
-	rightHipRotStart = [frameData[17],frameData[18],frameData[19],frameData[16]]
-	rightHipRotEnd = [frameDataNext[17],frameDataNext[18],frameDataNext[19],frameDataNext[16]]
-	rightHipRot	=	p.getQuaternionSlerp(rightHipRotStart,rightHipRotEnd,frameFraction)
-	
-	rightKneeRotStart	=	[frameData[20]]
-	rightKneeRotEnd	=	[frameDataNext[20]]
-	rightKneeRot = [rightKneeRotStart[0]+frameFraction*(rightKneeRotEnd[0]-rightKneeRotStart[0])]
-	
-	rightAnkleRotStart = [frameData[22],frameData[23],frameData[24],frameData[21]]
-	rightAnkleRotEnd = [frameDataNext[22],frameDataNext[23],frameDataNext[24],frameDataNext[21]]
-	rightAnkleRot	=	 p.getQuaternionSlerp(rightAnkleRotStart,rightAnkleRotEnd,frameFraction)
-		
-	rightShoulderRotStart	=	[frameData[26],frameData[27],frameData[28],frameData[25]]
-	rightShoulderRotEnd	=	[frameDataNext[26],frameDataNext[27],frameDataNext[28],frameDataNext[25]]
-	rightShoulderRot = p.getQuaternionSlerp(rightShoulderRotStart,rightShoulderRotEnd,frameFraction)
-	
-	rightElbowRotStart = [frameData[29]]
-	rightElbowRotEnd = [frameDataNext[29]]
-	rightElbowRot	=	[rightElbowRotStart[0]+frameFraction*(rightElbowRotEnd[0]-rightElbowRotStart[0])]
-	
-	leftHipRotStart	=	[frameData[31],frameData[32],frameData[33],frameData[30]]
-	leftHipRotEnd	=	[frameDataNext[31],frameDataNext[32],frameDataNext[33],frameDataNext[30]]
-	leftHipRot = p.getQuaternionSlerp(leftHipRotStart,leftHipRotEnd,frameFraction)
-	
-	leftKneeRotStart = [frameData[34]]
-	leftKneeRotEnd = [frameDataNext[34]]
-	leftKneeRot	=	[leftKneeRotStart[0] +frameFraction*(leftKneeRotEnd[0]-leftKneeRotStart[0])	]
-	
-	leftAnkleRotStart	=	[frameData[36],frameData[37],frameData[38],frameData[35]]
-	leftAnkleRotEnd	=	[frameDataNext[36],frameDataNext[37],frameDataNext[38],frameDataNext[35]]
-	leftAnkleRot = p.getQuaternionSlerp(leftAnkleRotStart,leftAnkleRotEnd,frameFraction)
-	
-	leftShoulderRotStart = [frameData[40],frameData[41],frameData[42],frameData[39]]
-	leftShoulderRotEnd = [frameDataNext[40],frameDataNext[41],frameDataNext[42],frameDataNext[39]]
-	leftShoulderRot	=	p.getQuaternionSlerp(leftShoulderRotStart,leftShoulderRotEnd,frameFraction)
-	leftElbowRotStart	=	[frameData[43]]
-	leftElbowRotEnd	=	[frameDataNext[43]]
-	leftElbowRot = [leftElbowRotStart[0]+frameFraction*(leftElbowRotEnd[0]-leftElbowRotStart[0])]
-	
-	if (0):#if (once):
-		p.resetJointStateMultiDof(humanoid,chest,chestRot)
-		p.resetJointStateMultiDof(humanoid,neck,neckRot)
-		p.resetJointStateMultiDof(humanoid,rightHip,rightHipRot)
-		p.resetJointStateMultiDof(humanoid,rightKnee,rightKneeRot)
-		p.resetJointStateMultiDof(humanoid,rightAnkle,rightAnkleRot)
-		p.resetJointStateMultiDof(humanoid,rightShoulder,rightShoulderRot)
-		p.resetJointStateMultiDof(humanoid,rightElbow, rightElbowRot)
-		p.resetJointStateMultiDof(humanoid,leftHip,	leftHipRot)
-		p.resetJointStateMultiDof(humanoid,leftKnee, leftKneeRot)
-		p.resetJointStateMultiDof(humanoid,leftAnkle,	leftAnkleRot)
-		p.resetJointStateMultiDof(humanoid,leftShoulder, leftShoulderRot)
-		p.resetJointStateMultiDof(humanoid,leftElbow,	leftElbowRot)
-	once=False
-	#print("chestRot=",chestRot)
-	p.setGravity(0,0,-10)
-	
-	kp=kpMotor
-	if (useExplicitPD):
-		jointDofCounts=[4,4,4,1,4,4,1,4,1,4,4,1]
-		#[x,y,z] base	position and [x,y,z,w] base	orientation!
-		totalDofs	=7 
-		for	dof	in jointDofCounts:
-			totalDofs	+= dof
-		
-		jointIndicesAll	=	[
-								chest,
-								neck,	
-								rightHip,
-												rightKnee,
-								rightAnkle,
-								rightShoulder,
-												rightElbow,
-								leftHip,
-												leftKnee,
-								leftAnkle,
-								leftShoulder,
-												leftElbow
-		]
-		basePos,baseOrn	=	p.getBasePositionAndOrientation(humanoid)
-		pose = [ basePos[0],basePos[1],basePos[2],
-													baseOrn[0],baseOrn[1],baseOrn[2],baseOrn[3],
-													chestRot[0],chestRot[1],chestRot[2],chestRot[3],
-													neckRot[0],neckRot[1],neckRot[2],neckRot[3],
-													rightHipRot[0],rightHipRot[1],rightHipRot[2],rightHipRot[3],
-													rightKneeRot[0],
-													rightAnkleRot[0],rightAnkleRot[1],rightAnkleRot[2],rightAnkleRot[3],
-													rightShoulderRot[0],rightShoulderRot[1],rightShoulderRot[2],rightShoulderRot[3],
-													rightElbowRot[0],
-													leftHipRot[0],leftHipRot[1],leftHipRot[2],leftHipRot[3],
-													leftKneeRot[0],
-													leftAnkleRot[0],leftAnkleRot[1],leftAnkleRot[2],leftAnkleRot[3],
-													leftShoulderRot[0],leftShoulderRot[1],leftShoulderRot[2],leftShoulderRot[3],
-													leftElbowRot[0]	]
-		
-				
-		#print("pose=")
-		#for po	in pose:
-		#	print(po)
-		
-		
-		taus = stablePD.computePD(bodyUniqueId=humanoid, 
-			jointIndices = jointIndicesAll,
-			desiredPositions = pose,
-			desiredVelocities	=	[0]*totalDofs,
-			kps	=	kpOrg,
-			kds	=	kdOrg,
-			maxForces	=	[maxForce]*totalDofs,	
-			timeStep=timeStep)
-			
-		taus3	=	explicitPD.computePD(bodyUniqueId=humanoid3, 
-			jointIndices = jointIndicesAll,
-			desiredPositions = pose,
-			desiredVelocities	=	[0]*totalDofs,
-			kps	=	kpOrg,
-			kds	=	kdOrg,
-			maxForces	=	[maxForce*0.05]*totalDofs, 
-			timeStep=timeStep)
-
-		#taus=[0]*43
-		dofIndex=7
-		for	index	in range (len(jointIndicesAll)):
-			jointIndex = jointIndicesAll[index]
-			if jointDofCounts[index]==4:
-				
-				p.setJointMotorControlMultiDof(humanoid,jointIndex,p.TORQUE_CONTROL,force=[taus[dofIndex+0],taus[dofIndex+1],taus[dofIndex+2]])
-				p.setJointMotorControlMultiDof(humanoid3,jointIndex,p.TORQUE_CONTROL,force=[taus3[dofIndex+0],taus3[dofIndex+1],taus3[dofIndex+2]])
-	
-			if jointDofCounts[index]==1:
-				
-				p.setJointMotorControlMultiDof(humanoid, jointIndex, controlMode=p.TORQUE_CONTROL, force=[taus[dofIndex]])
-				p.setJointMotorControlMultiDof(humanoid3,	jointIndex,	controlMode=p.TORQUE_CONTROL,	force=[taus3[dofIndex]])
-
-			dofIndex+=jointDofCounts[index]
-		
-		#print("len(taus)=",len(taus))
-		#print("taus=",taus)
-		
-		
-	p.setJointMotorControlMultiDof(humanoid2,chest,p.POSITION_CONTROL, targetPosition=chestRot,positionGain=kp,	force=[maxForce])
-	p.setJointMotorControlMultiDof(humanoid2,neck,p.POSITION_CONTROL,targetPosition=neckRot,positionGain=kp, force=[maxForce])
-	p.setJointMotorControlMultiDof(humanoid2,rightHip,p.POSITION_CONTROL,targetPosition=rightHipRot,positionGain=kp, force=[maxForce])
-	p.setJointMotorControlMultiDof(humanoid2,rightKnee,p.POSITION_CONTROL,targetPosition=rightKneeRot,positionGain=kp, force=[maxForce])
-	p.setJointMotorControlMultiDof(humanoid2,rightAnkle,p.POSITION_CONTROL,targetPosition=rightAnkleRot,positionGain=kp, force=[maxForce])
-	p.setJointMotorControlMultiDof(humanoid2,rightShoulder,p.POSITION_CONTROL,targetPosition=rightShoulderRot,positionGain=kp, force=[maxForce])
-	p.setJointMotorControlMultiDof(humanoid2,rightElbow, p.POSITION_CONTROL,targetPosition=rightElbowRot,positionGain=kp,	force=[maxForce])
-	p.setJointMotorControlMultiDof(humanoid2,leftHip,	p.POSITION_CONTROL,targetPosition=leftHipRot,positionGain=kp,	force=[maxForce])
-	p.setJointMotorControlMultiDof(humanoid2,leftKnee, p.POSITION_CONTROL,targetPosition=leftKneeRot,positionGain=kp,	force=[maxForce])
-	p.setJointMotorControlMultiDof(humanoid2,leftAnkle,	p.POSITION_CONTROL,targetPosition=leftAnkleRot,positionGain=kp,	force=[maxForce])
-	p.setJointMotorControlMultiDof(humanoid2,leftShoulder, p.POSITION_CONTROL,targetPosition=leftShoulderRot,positionGain=kp,	force=[maxForce])
-	p.setJointMotorControlMultiDof(humanoid2,leftElbow,	p.POSITION_CONTROL,targetPosition=leftElbowRot,positionGain=kp,	force=[maxForce])
-	
-	kinematicHumanoid4 = True
-	if (kinematicHumanoid4):
-		p.resetJointStateMultiDof(humanoid4,chest,chestRot)
-		p.resetJointStateMultiDof(humanoid4,neck,neckRot)
-		p.resetJointStateMultiDof(humanoid4,rightHip,rightHipRot)
-		p.resetJointStateMultiDof(humanoid4,rightKnee,rightKneeRot)
-		p.resetJointStateMultiDof(humanoid4,rightAnkle,rightAnkleRot)
-		p.resetJointStateMultiDof(humanoid4,rightShoulder,rightShoulderRot)
-		p.resetJointStateMultiDof(humanoid4,rightElbow,	rightElbowRot)
-		p.resetJointStateMultiDof(humanoid4,leftHip, leftHipRot)
-		p.resetJointStateMultiDof(humanoid4,leftKnee,	leftKneeRot)
-		p.resetJointStateMultiDof(humanoid4,leftAnkle, leftAnkleRot)
-		p.resetJointStateMultiDof(humanoid4,leftShoulder,	leftShoulderRot)
-		p.resetJointStateMultiDof(humanoid4,leftElbow, leftElbowRot)
-	p.stepSimulation()
-	
-	if showJointMotorTorques:
-		for	j	in range(p.getNumJoints(humanoid2)):
-			jointState = p.getJointStateMultiDof(humanoid2,j)
-			print("jointStateMultiDof[",j,"].pos=",jointState[0])
-			print("jointStateMultiDof[",j,"].vel=",jointState[1])
-			print("jointStateMultiDof[",j,"].jointForces=",jointState[3])
-	time.sleep(timeStep)
+kpOrg = [
+    0, 0, 0, 0, 0, 0, 0, 1000, 1000, 1000, 1000, 100, 100, 100, 100, 500, 500, 500, 500, 500, 400,
+    400, 400, 400, 400, 400, 400, 400, 300, 500, 500, 500, 500, 500, 400, 400, 400, 400, 400, 400,
+    400, 400, 300
+]
+kdOrg = [
+    0, 0, 0, 0, 0, 0, 0, 100, 100, 100, 100, 10, 10, 10, 10, 50, 50, 50, 50, 50, 40, 40, 40, 40,
+    40, 40, 40, 40, 30, 50, 50, 50, 50, 50, 40, 40, 40, 40, 40, 40, 40, 40, 30
+]
+
+once = True
+p.getCameraImage(320, 200)
+
+while (p.isConnected()):
+
+  if useGUI:
+    erp = p.readUserDebugParameter(erpId)
+    kpMotor = p.readUserDebugParameter(kpMotorId)
+    maxForce = p.readUserDebugParameter(forceMotorId)
+    frameReal = p.readUserDebugParameter(frameId)
+  else:
+    erp = 0.2
+    kpMotor = 0.2
+    maxForce = 1000
+    frameReal = 0
+
+  kp = kpMotor
+
+  frame = int(frameReal)
+  frameNext = frame + 1
+  if (frameNext >= numFrames):
+    frameNext = frame
+
+  frameFraction = frameReal - frame
+  #print("frameFraction=",frameFraction)
+  #print("frame=",frame)
+  #print("frameNext=", frameNext)
+
+  #getQuaternionSlerp
+
+  frameData = motion_dict['Frames'][frame]
+  frameDataNext = motion_dict['Frames'][frameNext]
+
+  #print("duration=",frameData[0])
+  #print(pos=[frameData])
+
+  basePos1Start = [frameData[1], frameData[2], frameData[3]]
+  basePos1End = [frameDataNext[1], frameDataNext[2], frameDataNext[3]]
+  basePos1 = [
+      basePos1Start[0] + frameFraction * (basePos1End[0] - basePos1Start[0]),
+      basePos1Start[1] + frameFraction * (basePos1End[1] - basePos1Start[1]),
+      basePos1Start[2] + frameFraction * (basePos1End[2] - basePos1Start[2])
+  ]
+  baseOrn1Start = [frameData[5], frameData[6], frameData[7], frameData[4]]
+  baseOrn1Next = [frameDataNext[5], frameDataNext[6], frameDataNext[7], frameDataNext[4]]
+  baseOrn1 = p.getQuaternionSlerp(baseOrn1Start, baseOrn1Next, frameFraction)
+  #pre-rotate	to make	z-up
+  if (useZUp):
+    y2zPos = [0, 0, 0.0]
+    y2zOrn = p.getQuaternionFromEuler([1.57, 0, 0])
+    basePos, baseOrn = p.multiplyTransforms(y2zPos, y2zOrn, basePos1, baseOrn1)
+    p.resetBasePositionAndOrientation(humanoid, basePos, baseOrn)
+
+    y2zPos = [0, 2, 0.0]
+    y2zOrn = p.getQuaternionFromEuler([1.57, 0, 0])
+    basePos, baseOrn = p.multiplyTransforms(y2zPos, y2zOrn, basePos1, baseOrn1)
+    p.resetBasePositionAndOrientation(humanoid2, basePos, baseOrn)
+
+  chestRotStart = [frameData[9], frameData[10], frameData[11], frameData[8]]
+  chestRotEnd = [frameDataNext[9], frameDataNext[10], frameDataNext[11], frameDataNext[8]]
+  chestRot = p.getQuaternionSlerp(chestRotStart, chestRotEnd, frameFraction)
+
+  neckRotStart = [frameData[13], frameData[14], frameData[15], frameData[12]]
+  neckRotEnd = [frameDataNext[13], frameDataNext[14], frameDataNext[15], frameDataNext[12]]
+  neckRot = p.getQuaternionSlerp(neckRotStart, neckRotEnd, frameFraction)
+
+  rightHipRotStart = [frameData[17], frameData[18], frameData[19], frameData[16]]
+  rightHipRotEnd = [frameDataNext[17], frameDataNext[18], frameDataNext[19], frameDataNext[16]]
+  rightHipRot = p.getQuaternionSlerp(rightHipRotStart, rightHipRotEnd, frameFraction)
+
+  rightKneeRotStart = [frameData[20]]
+  rightKneeRotEnd = [frameDataNext[20]]
+  rightKneeRot = [
+      rightKneeRotStart[0] + frameFraction * (rightKneeRotEnd[0] - rightKneeRotStart[0])
+  ]
+
+  rightAnkleRotStart = [frameData[22], frameData[23], frameData[24], frameData[21]]
+  rightAnkleRotEnd = [frameDataNext[22], frameDataNext[23], frameDataNext[24], frameDataNext[21]]
+  rightAnkleRot = p.getQuaternionSlerp(rightAnkleRotStart, rightAnkleRotEnd, frameFraction)
+
+  rightShoulderRotStart = [frameData[26], frameData[27], frameData[28], frameData[25]]
+  rightShoulderRotEnd = [
+      frameDataNext[26], frameDataNext[27], frameDataNext[28], frameDataNext[25]
+  ]
+  rightShoulderRot = p.getQuaternionSlerp(rightShoulderRotStart, rightShoulderRotEnd,
+                                          frameFraction)
+
+  rightElbowRotStart = [frameData[29]]
+  rightElbowRotEnd = [frameDataNext[29]]
+  rightElbowRot = [
+      rightElbowRotStart[0] + frameFraction * (rightElbowRotEnd[0] - rightElbowRotStart[0])
+  ]
+
+  leftHipRotStart = [frameData[31], frameData[32], frameData[33], frameData[30]]
+  leftHipRotEnd = [frameDataNext[31], frameDataNext[32], frameDataNext[33], frameDataNext[30]]
+  leftHipRot = p.getQuaternionSlerp(leftHipRotStart, leftHipRotEnd, frameFraction)
+
+  leftKneeRotStart = [frameData[34]]
+  leftKneeRotEnd = [frameDataNext[34]]
+  leftKneeRot = [leftKneeRotStart[0] + frameFraction * (leftKneeRotEnd[0] - leftKneeRotStart[0])]
+
+  leftAnkleRotStart = [frameData[36], frameData[37], frameData[38], frameData[35]]
+  leftAnkleRotEnd = [frameDataNext[36], frameDataNext[37], frameDataNext[38], frameDataNext[35]]
+  leftAnkleRot = p.getQuaternionSlerp(leftAnkleRotStart, leftAnkleRotEnd, frameFraction)
+
+  leftShoulderRotStart = [frameData[40], frameData[41], frameData[42], frameData[39]]
+  leftShoulderRotEnd = [frameDataNext[40], frameDataNext[41], frameDataNext[42], frameDataNext[39]]
+  leftShoulderRot = p.getQuaternionSlerp(leftShoulderRotStart, leftShoulderRotEnd, frameFraction)
+  leftElbowRotStart = [frameData[43]]
+  leftElbowRotEnd = [frameDataNext[43]]
+  leftElbowRot = [
+      leftElbowRotStart[0] + frameFraction * (leftElbowRotEnd[0] - leftElbowRotStart[0])
+  ]
+
+  if (0):  #if (once):
+    p.resetJointStateMultiDof(humanoid, chest, chestRot)
+    p.resetJointStateMultiDof(humanoid, neck, neckRot)
+    p.resetJointStateMultiDof(humanoid, rightHip, rightHipRot)
+    p.resetJointStateMultiDof(humanoid, rightKnee, rightKneeRot)
+    p.resetJointStateMultiDof(humanoid, rightAnkle, rightAnkleRot)
+    p.resetJointStateMultiDof(humanoid, rightShoulder, rightShoulderRot)
+    p.resetJointStateMultiDof(humanoid, rightElbow, rightElbowRot)
+    p.resetJointStateMultiDof(humanoid, leftHip, leftHipRot)
+    p.resetJointStateMultiDof(humanoid, leftKnee, leftKneeRot)
+    p.resetJointStateMultiDof(humanoid, leftAnkle, leftAnkleRot)
+    p.resetJointStateMultiDof(humanoid, leftShoulder, leftShoulderRot)
+    p.resetJointStateMultiDof(humanoid, leftElbow, leftElbowRot)
+  once = False
+  #print("chestRot=",chestRot)
+  p.setGravity(0, 0, -10)
+
+  kp = kpMotor
+  if (useExplicitPD):
+    jointDofCounts = [4, 4, 4, 1, 4, 4, 1, 4, 1, 4, 4, 1]
+    #[x,y,z] base	position and [x,y,z,w] base	orientation!
+    totalDofs = 7
+    for dof in jointDofCounts:
+      totalDofs += dof
+
+    jointIndicesAll = [
+        chest, neck, rightHip, rightKnee, rightAnkle, rightShoulder, rightElbow, leftHip, leftKnee,
+        leftAnkle, leftShoulder, leftElbow
+    ]
+    basePos, baseOrn = p.getBasePositionAndOrientation(humanoid)
+    pose = [
+        basePos[0], basePos[1], basePos[2], baseOrn[0], baseOrn[1], baseOrn[2], baseOrn[3],
+        chestRot[0], chestRot[1], chestRot[2], chestRot[3], neckRot[0], neckRot[1], neckRot[2],
+        neckRot[3], rightHipRot[0], rightHipRot[1], rightHipRot[2], rightHipRot[3],
+        rightKneeRot[0], rightAnkleRot[0], rightAnkleRot[1], rightAnkleRot[2], rightAnkleRot[3],
+        rightShoulderRot[0], rightShoulderRot[1], rightShoulderRot[2], rightShoulderRot[3],
+        rightElbowRot[0], leftHipRot[0], leftHipRot[1], leftHipRot[2], leftHipRot[3],
+        leftKneeRot[0], leftAnkleRot[0], leftAnkleRot[1], leftAnkleRot[2], leftAnkleRot[3],
+        leftShoulderRot[0], leftShoulderRot[1], leftShoulderRot[2], leftShoulderRot[3],
+        leftElbowRot[0]
+    ]
+
+    #print("pose=")
+    #for po	in pose:
+    #	print(po)
+
+    taus = stablePD.computePD(bodyUniqueId=humanoid,
+                              jointIndices=jointIndicesAll,
+                              desiredPositions=pose,
+                              desiredVelocities=[0] * totalDofs,
+                              kps=kpOrg,
+                              kds=kdOrg,
+                              maxForces=[maxForce] * totalDofs,
+                              timeStep=timeStep)
+
+    taus3 = explicitPD.computePD(bodyUniqueId=humanoid3,
+                                 jointIndices=jointIndicesAll,
+                                 desiredPositions=pose,
+                                 desiredVelocities=[0] * totalDofs,
+                                 kps=kpOrg,
+                                 kds=kdOrg,
+                                 maxForces=[maxForce * 0.05] * totalDofs,
+                                 timeStep=timeStep)
+
+    #taus=[0]*43
+    dofIndex = 7
+    for index in range(len(jointIndicesAll)):
+      jointIndex = jointIndicesAll[index]
+      if jointDofCounts[index] == 4:
+
+        p.setJointMotorControlMultiDof(
+            humanoid,
+            jointIndex,
+            p.TORQUE_CONTROL,
+            force=[taus[dofIndex + 0], taus[dofIndex + 1], taus[dofIndex + 2]])
+        p.setJointMotorControlMultiDof(
+            humanoid3,
+            jointIndex,
+            p.TORQUE_CONTROL,
+            force=[taus3[dofIndex + 0], taus3[dofIndex + 1], taus3[dofIndex + 2]])
+
+      if jointDofCounts[index] == 1:
+
+        p.setJointMotorControlMultiDof(humanoid,
+                                       jointIndex,
+                                       controlMode=p.TORQUE_CONTROL,
+                                       force=[taus[dofIndex]])
+        p.setJointMotorControlMultiDof(humanoid3,
+                                       jointIndex,
+                                       controlMode=p.TORQUE_CONTROL,
+                                       force=[taus3[dofIndex]])
+
+      dofIndex += jointDofCounts[index]
+
+    #print("len(taus)=",len(taus))
+    #print("taus=",taus)
+
+  p.setJointMotorControlMultiDof(humanoid2,
+                                 chest,
+                                 p.POSITION_CONTROL,
+                                 targetPosition=chestRot,
+                                 positionGain=kp,
+                                 force=[maxForce])
+  p.setJointMotorControlMultiDof(humanoid2,
+                                 neck,
+                                 p.POSITION_CONTROL,
+                                 targetPosition=neckRot,
+                                 positionGain=kp,
+                                 force=[maxForce])
+  p.setJointMotorControlMultiDof(humanoid2,
+                                 rightHip,
+                                 p.POSITION_CONTROL,
+                                 targetPosition=rightHipRot,
+                                 positionGain=kp,
+                                 force=[maxForce])
+  p.setJointMotorControlMultiDof(humanoid2,
+                                 rightKnee,
+                                 p.POSITION_CONTROL,
+                                 targetPosition=rightKneeRot,
+                                 positionGain=kp,
+                                 force=[maxForce])
+  p.setJointMotorControlMultiDof(humanoid2,
+                                 rightAnkle,
+                                 p.POSITION_CONTROL,
+                                 targetPosition=rightAnkleRot,
+                                 positionGain=kp,
+                                 force=[maxForce])
+  p.setJointMotorControlMultiDof(humanoid2,
+                                 rightShoulder,
+                                 p.POSITION_CONTROL,
+                                 targetPosition=rightShoulderRot,
+                                 positionGain=kp,
+                                 force=[maxForce])
+  p.setJointMotorControlMultiDof(humanoid2,
+                                 rightElbow,
+                                 p.POSITION_CONTROL,
+                                 targetPosition=rightElbowRot,
+                                 positionGain=kp,
+                                 force=[maxForce])
+  p.setJointMotorControlMultiDof(humanoid2,
+                                 leftHip,
+                                 p.POSITION_CONTROL,
+                                 targetPosition=leftHipRot,
+                                 positionGain=kp,
+                                 force=[maxForce])
+  p.setJointMotorControlMultiDof(humanoid2,
+                                 leftKnee,
+                                 p.POSITION_CONTROL,
+                                 targetPosition=leftKneeRot,
+                                 positionGain=kp,
+                                 force=[maxForce])
+  p.setJointMotorControlMultiDof(humanoid2,
+                                 leftAnkle,
+                                 p.POSITION_CONTROL,
+                                 targetPosition=leftAnkleRot,
+                                 positionGain=kp,
+                                 force=[maxForce])
+  p.setJointMotorControlMultiDof(humanoid2,
+                                 leftShoulder,
+                                 p.POSITION_CONTROL,
+                                 targetPosition=leftShoulderRot,
+                                 positionGain=kp,
+                                 force=[maxForce])
+  p.setJointMotorControlMultiDof(humanoid2,
+                                 leftElbow,
+                                 p.POSITION_CONTROL,
+                                 targetPosition=leftElbowRot,
+                                 positionGain=kp,
+                                 force=[maxForce])
+
+  kinematicHumanoid4 = True
+  if (kinematicHumanoid4):
+    p.resetJointStateMultiDof(humanoid4, chest, chestRot)
+    p.resetJointStateMultiDof(humanoid4, neck, neckRot)
+    p.resetJointStateMultiDof(humanoid4, rightHip, rightHipRot)
+    p.resetJointStateMultiDof(humanoid4, rightKnee, rightKneeRot)
+    p.resetJointStateMultiDof(humanoid4, rightAnkle, rightAnkleRot)
+    p.resetJointStateMultiDof(humanoid4, rightShoulder, rightShoulderRot)
+    p.resetJointStateMultiDof(humanoid4, rightElbow, rightElbowRot)
+    p.resetJointStateMultiDof(humanoid4, leftHip, leftHipRot)
+    p.resetJointStateMultiDof(humanoid4, leftKnee, leftKneeRot)
+    p.resetJointStateMultiDof(humanoid4, leftAnkle, leftAnkleRot)
+    p.resetJointStateMultiDof(humanoid4, leftShoulder, leftShoulderRot)
+    p.resetJointStateMultiDof(humanoid4, leftElbow, leftElbowRot)
+  p.stepSimulation()
+
+  if showJointMotorTorques:
+    for j in range(p.getNumJoints(humanoid2)):
+      jointState = p.getJointStateMultiDof(humanoid2, j)
+      print("jointStateMultiDof[", j, "].pos=", jointState[0])
+      print("jointStateMultiDof[", j, "].vel=", jointState[1])
+      print("jointStateMultiDof[", j, "].jointForces=", jointState[3])
+  time.sleep(timeStep)
diff --git a/examples/pybullet/examples/humanoid_benchmark.py b/examples/pybullet/examples/humanoid_benchmark.py
index 5c056cdaa..c063f9006 100644
--- a/examples/pybullet/examples/humanoid_benchmark.py
+++ b/examples/pybullet/examples/humanoid_benchmark.py
@@ -1,20 +1,25 @@
 import pybullet as p
 import time
 p.connect(p.DIRECT)
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 p.setPhysicsEngineParameter(numSolverIterations=5)
-p.setPhysicsEngineParameter(fixedTimeStep=1./240.)
+p.setPhysicsEngineParameter(fixedTimeStep=1. / 240.)
 p.setPhysicsEngineParameter(numSubSteps=1)
 
 p.loadURDF("plane.urdf")
 
 objects = p.loadMJCF("mjcf/humanoid_symmetric.xml")
 ob = objects[0]
-p.resetBasePositionAndOrientation(ob,[0.789351,0.962124,0.113124],[0.710965,0.218117,0.519402,-0.420923])
-jointPositions=[ -0.200226, 0.123925, 0.000000, -0.224016, 0.000000, -0.022247, 0.099119, -0.041829, 0.000000, -0.344372, 0.000000, 0.000000, 0.090687, -0.578698, 0.044461, 0.000000, -0.185004, 0.000000, 0.000000, 0.039517, -0.131217, 0.000000, 0.083382, 0.000000, -0.165303, -0.140802, 0.000000, -0.007374, 0.000000 ]
-for jointIndex in range (p.getNumJoints(ob)):
-	p.resetJointState(ob,jointIndex,jointPositions[jointIndex])
-
+p.resetBasePositionAndOrientation(ob, [0.789351, 0.962124, 0.113124],
+                                  [0.710965, 0.218117, 0.519402, -0.420923])
+jointPositions = [
+    -0.200226, 0.123925, 0.000000, -0.224016, 0.000000, -0.022247, 0.099119, -0.041829, 0.000000,
+    -0.344372, 0.000000, 0.000000, 0.090687, -0.578698, 0.044461, 0.000000, -0.185004, 0.000000,
+    0.000000, 0.039517, -0.131217, 0.000000, 0.083382, 0.000000, -0.165303, -0.140802, 0.000000,
+    -0.007374, 0.000000
+]
+for jointIndex in range(p.getNumJoints(ob)):
+  p.resetJointState(ob, jointIndex, jointPositions[jointIndex])
 
 #first let the humanoid fall
 #p.setRealTimeSimulation(1)
@@ -26,11 +31,10 @@ p.setRealTimeSimulation(0)
 print("Starting benchmark")
 fileName = "pybullet_humanoid_timings.json"
 
-logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS,fileName)
+logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, fileName)
 for i in range(1000):
-	p.stepSimulation()
+  p.stepSimulation()
 p.stopStateLogging(logId)
 
 print("ended benchmark")
 print("Use Chrome browser, visit about://tracing, and load the %s file" % fileName)
-
diff --git a/examples/pybullet/examples/humanoid_knee_position_control.py b/examples/pybullet/examples/humanoid_knee_position_control.py
index b62bd76a1..f43a7cb1e 100644
--- a/examples/pybullet/examples/humanoid_knee_position_control.py
+++ b/examples/pybullet/examples/humanoid_knee_position_control.py
@@ -2,8 +2,8 @@ import pybullet as p
 import time
 
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	cid = p.connect(p.GUI)
+if (cid < 0):
+  cid = p.connect(p.GUI)
 
 p.resetSimulation()
 
@@ -11,36 +11,41 @@ useRealTime = 0
 
 p.setRealTimeSimulation(useRealTime)
 
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 
 p.loadSDF("stadium.sdf")
 
 obUids = p.loadMJCF("mjcf/humanoid_fixed.xml")
 human = obUids[0]
 
+for i in range(p.getNumJoints(human)):
+  p.setJointMotorControl2(human, i, p.POSITION_CONTROL, targetPosition=0, force=500)
 
+kneeAngleTargetId = p.addUserDebugParameter("kneeAngle", -4, 4, -1)
+maxForceId = p.addUserDebugParameter("maxForce", 0, 500, 10)
 
-for i in range (p.getNumJoints(human)):
-	p.setJointMotorControl2(human,i,p.POSITION_CONTROL,targetPosition=0,force=500)
+kneeAngleTargetLeftId = p.addUserDebugParameter("kneeAngleL", -4, 4, -1)
+maxForceLeftId = p.addUserDebugParameter("maxForceL", 0, 500, 10)
 
-kneeAngleTargetId = p.addUserDebugParameter("kneeAngle",-4,4,-1)
-maxForceId = p.addUserDebugParameter("maxForce",0,500,10)
-
-kneeAngleTargetLeftId = p.addUserDebugParameter("kneeAngleL",-4,4,-1)
-maxForceLeftId = p.addUserDebugParameter("maxForceL",0,500,10)
-
-
-kneeJointIndex=11
-kneeJointIndexLeft=18
+kneeJointIndex = 11
+kneeJointIndexLeft = 18
 
 while (1):
-	time.sleep(0.01)
-	kneeAngleTarget = p.readUserDebugParameter(kneeAngleTargetId)
-	maxForce = p.readUserDebugParameter(maxForceId)
-	p.setJointMotorControl2(human,kneeJointIndex,p.POSITION_CONTROL,targetPosition=kneeAngleTarget,force=maxForce)
-	kneeAngleTargetLeft = p.readUserDebugParameter(kneeAngleTargetLeftId)
-	maxForceLeft = p.readUserDebugParameter(maxForceLeftId)
-	p.setJointMotorControl2(human,kneeJointIndexLeft,p.POSITION_CONTROL,targetPosition=kneeAngleTargetLeft,force=maxForceLeft)
-
-	if (useRealTime==0):
-		p.stepSimulation()	
\ No newline at end of file
+  time.sleep(0.01)
+  kneeAngleTarget = p.readUserDebugParameter(kneeAngleTargetId)
+  maxForce = p.readUserDebugParameter(maxForceId)
+  p.setJointMotorControl2(human,
+                          kneeJointIndex,
+                          p.POSITION_CONTROL,
+                          targetPosition=kneeAngleTarget,
+                          force=maxForce)
+  kneeAngleTargetLeft = p.readUserDebugParameter(kneeAngleTargetLeftId)
+  maxForceLeft = p.readUserDebugParameter(maxForceLeftId)
+  p.setJointMotorControl2(human,
+                          kneeJointIndexLeft,
+                          p.POSITION_CONTROL,
+                          targetPosition=kneeAngleTargetLeft,
+                          force=maxForceLeft)
+
+  if (useRealTime == 0):
+    p.stepSimulation()
diff --git a/examples/pybullet/examples/humanoid_manual_control.py b/examples/pybullet/examples/humanoid_manual_control.py
index 20a7c48e9..e6170f43a 100644
--- a/examples/pybullet/examples/humanoid_manual_control.py
+++ b/examples/pybullet/examples/humanoid_manual_control.py
@@ -5,28 +5,28 @@ p.connect(p.GUI)
 obUids = p.loadMJCF("mjcf/humanoid.xml")
 humanoid = obUids[1]
 
-gravId = p.addUserDebugParameter("gravity",-10,10,-10)
-jointIds=[]
-paramIds=[]
+gravId = p.addUserDebugParameter("gravity", -10, 10, -10)
+jointIds = []
+paramIds = []
 
 p.setPhysicsEngineParameter(numSolverIterations=10)
-p.changeDynamics(humanoid,-1,linearDamping=0, angularDamping=0)
+p.changeDynamics(humanoid, -1, linearDamping=0, angularDamping=0)
 
-for j in range (p.getNumJoints(humanoid)):
-	p.changeDynamics(humanoid,j,linearDamping=0, angularDamping=0)
-	info = p.getJointInfo(humanoid,j)
-	#print(info)
-	jointName = info[1]
-	jointType = info[2]
-	if (jointType==p.JOINT_PRISMATIC or jointType==p.JOINT_REVOLUTE):
-		jointIds.append(j)
-		paramIds.append(p.addUserDebugParameter(jointName.decode("utf-8"),-4,4,0))
+for j in range(p.getNumJoints(humanoid)):
+  p.changeDynamics(humanoid, j, linearDamping=0, angularDamping=0)
+  info = p.getJointInfo(humanoid, j)
+  #print(info)
+  jointName = info[1]
+  jointType = info[2]
+  if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE):
+    jointIds.append(j)
+    paramIds.append(p.addUserDebugParameter(jointName.decode("utf-8"), -4, 4, 0))
 
 p.setRealTimeSimulation(1)
-while(1):
-	p.setGravity(0,0,p.readUserDebugParameter(gravId))
-	for i in range(len(paramIds)):
-		c = paramIds[i]
-		targetPos = p.readUserDebugParameter(c)
-		p.setJointMotorControl2(humanoid,jointIds[i],p.POSITION_CONTROL,targetPos, force=5*240.)
-	time.sleep(0.01)
\ No newline at end of file
+while (1):
+  p.setGravity(0, 0, p.readUserDebugParameter(gravId))
+  for i in range(len(paramIds)):
+    c = paramIds[i]
+    targetPos = p.readUserDebugParameter(c)
+    p.setJointMotorControl2(humanoid, jointIds[i], p.POSITION_CONTROL, targetPos, force=5 * 240.)
+  time.sleep(0.01)
diff --git a/examples/pybullet/examples/ik_end_effector_orientation.py b/examples/pybullet/examples/ik_end_effector_orientation.py
index 29c83872d..5163fbad3 100644
--- a/examples/pybullet/examples/ik_end_effector_orientation.py
+++ b/examples/pybullet/examples/ik_end_effector_orientation.py
@@ -5,26 +5,37 @@ from datetime import datetime
 from time import sleep
 
 p.connect(p.GUI)
-p.loadURDF("plane.urdf",[0,0,-0.3])
-kukaId = p.loadURDF("kuka_iiwa/model.urdf",[0,0,0])
-p.resetBasePositionAndOrientation(kukaId,[0,0,0],[0,0,0,1])
+p.loadURDF("plane.urdf", [0, 0, -0.3])
+kukaId = p.loadURDF("kuka_iiwa/model.urdf", [0, 0, 0])
+p.resetBasePositionAndOrientation(kukaId, [0, 0, 0], [0, 0, 0, 1])
 kukaEndEffectorIndex = 6
 numJoints = p.getNumJoints(kukaId)
 
 #Joint damping coefficents. Using large values for the joints that we don't want to move.
-jd=[100.0,100.0,100.0,100.0,100.0,100.0,0.5]
+jd = [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 0.5]
 #jd=[0.5,0.5,0.5,0.5,0.5,0.5,0.5]
 
-p.setGravity(0,0,0)
+p.setGravity(0, 0, 0)
 
 while 1:
-	p.stepSimulation()
-	for i in range (1):
-		pos = [0,0,1.26]
-		orn = p.getQuaternionFromEuler([0,0,3.14])
+  p.stepSimulation()
+  for i in range(1):
+    pos = [0, 0, 1.26]
+    orn = p.getQuaternionFromEuler([0, 0, 3.14])
 
-		jointPoses = p.calculateInverseKinematics(kukaId,kukaEndEffectorIndex,pos,orn,jointDamping=jd)
-	
-	for i in range (numJoints):
-		p.setJointMotorControl2(bodyIndex=kukaId,jointIndex=i,controlMode=p.POSITION_CONTROL,targetPosition=jointPoses[i],targetVelocity=0,force=500,positionGain=0.03,velocityGain=1)
-	sleep(0.05)
+    jointPoses = p.calculateInverseKinematics(kukaId,
+                                              kukaEndEffectorIndex,
+                                              pos,
+                                              orn,
+                                              jointDamping=jd)
+
+  for i in range(numJoints):
+    p.setJointMotorControl2(bodyIndex=kukaId,
+                            jointIndex=i,
+                            controlMode=p.POSITION_CONTROL,
+                            targetPosition=jointPoses[i],
+                            targetVelocity=0,
+                            force=500,
+                            positionGain=0.03,
+                            velocityGain=1)
+  sleep(0.05)
diff --git a/examples/pybullet/examples/integrate.py b/examples/pybullet/examples/integrate.py
index a4e4deaa2..a706dd00e 100644
--- a/examples/pybullet/examples/integrate.py
+++ b/examples/pybullet/examples/integrate.py
@@ -1,13 +1,12 @@
 import pybullet as p
 p.connect(p.GUI)
-cube  = p.loadURDF("cube.urdf")
+cube = p.loadURDF("cube.urdf")
 frequency = 240
-timeStep = 1./frequency
-p.setGravity(0,0,-9.8)
-p.changeDynamics(cube,-1,linearDamping=0,angularDamping=0)
-p.setPhysicsEngineParameter(fixedTimeStep = timeStep)
-for i in range (frequency):
-	p.stepSimulation()
-pos,orn = p.getBasePositionAndOrientation(cube)
+timeStep = 1. / frequency
+p.setGravity(0, 0, -9.8)
+p.changeDynamics(cube, -1, linearDamping=0, angularDamping=0)
+p.setPhysicsEngineParameter(fixedTimeStep=timeStep)
+for i in range(frequency):
+  p.stepSimulation()
+pos, orn = p.getBasePositionAndOrientation(cube)
 print(pos)
-
diff --git a/examples/pybullet/examples/internalEdge.py b/examples/pybullet/examples/internalEdge.py
index 65369e120..fa8dbcb9b 100644
--- a/examples/pybullet/examples/internalEdge.py
+++ b/examples/pybullet/examples/internalEdge.py
@@ -4,39 +4,37 @@ import time
 p.connect(p.GUI)
 
 if (1):
-	box_collision_shape_id = p.createCollisionShape(
-		shapeType=p.GEOM_BOX,
-		halfExtents=[0.01,0.01,0.055])
-	box_mass=0.1
-	box_visual_shape_id = -1
-	box_position=[0,0.1,1]
-	box_orientation=[0,0,0,1]
+  box_collision_shape_id = p.createCollisionShape(shapeType=p.GEOM_BOX,
+                                                  halfExtents=[0.01, 0.01, 0.055])
+  box_mass = 0.1
+  box_visual_shape_id = -1
+  box_position = [0, 0.1, 1]
+  box_orientation = [0, 0, 0, 1]
 
-	p.createMultiBody(
-		box_mass, box_collision_shape_id, box_visual_shape_id,
-		box_position, box_orientation, useMaximalCoordinates=True)
-	
+  p.createMultiBody(box_mass,
+                    box_collision_shape_id,
+                    box_visual_shape_id,
+                    box_position,
+                    box_orientation,
+                    useMaximalCoordinates=True)
 
-terrain_mass=0
-terrain_visual_shape_id=-1
-terrain_position=[0,0,0]
-terrain_orientation=[0,0,0,1]
-terrain_collision_shape_id = p.createCollisionShape(
-	shapeType=p.GEOM_MESH,
-  fileName="terrain.obj",
-  flags=p.GEOM_FORCE_CONCAVE_TRIMESH|p.GEOM_CONCAVE_INTERNAL_EDGE,
-  meshScale=[0.5, 0.5, 0.5])
-p.createMultiBody(
-	terrain_mass, terrain_collision_shape_id, terrain_visual_shape_id,
-	terrain_position, terrain_orientation)
-            
+terrain_mass = 0
+terrain_visual_shape_id = -1
+terrain_position = [0, 0, 0]
+terrain_orientation = [0, 0, 0, 1]
+terrain_collision_shape_id = p.createCollisionShape(shapeType=p.GEOM_MESH,
+                                                    fileName="terrain.obj",
+                                                    flags=p.GEOM_FORCE_CONCAVE_TRIMESH |
+                                                    p.GEOM_CONCAVE_INTERNAL_EDGE,
+                                                    meshScale=[0.5, 0.5, 0.5])
+p.createMultiBody(terrain_mass, terrain_collision_shape_id, terrain_visual_shape_id,
+                  terrain_position, terrain_orientation)
 
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 
 pts = p.getContactPoints()
-print("num points=",len(pts))
+print("num points=", len(pts))
 print(pts)
 while (p.isConnected()):
-	time.sleep(1./240.)
-	p.stepSimulation()
-	
\ No newline at end of file
+  time.sleep(1. / 240.)
+  p.stepSimulation()
diff --git a/examples/pybullet/examples/inverse_dynamics.py b/examples/pybullet/examples/inverse_dynamics.py
index 955ebfee6..d565962c1 100644
--- a/examples/pybullet/examples/inverse_dynamics.py
+++ b/examples/pybullet/examples/inverse_dynamics.py
@@ -3,7 +3,7 @@ plot = True
 import time
 
 if (plot):
-	import matplotlib.pyplot as plt
+  import matplotlib.pyplot as plt
 import math
 verbose = False
 
@@ -19,137 +19,146 @@ bullet.setTimeStep(delta_t)
 # Switch between URDF with/without FIXED joints
 with_fixed_joints = True
 
-
 if with_fixed_joints:
-    id_revolute_joints = [0, 3]
-    id_robot = bullet.loadURDF("TwoJointRobot_w_fixedJoints.urdf",
-                               robot_base, robot_orientation, useFixedBase=True)
+  id_revolute_joints = [0, 3]
+  id_robot = bullet.loadURDF("TwoJointRobot_w_fixedJoints.urdf",
+                             robot_base,
+                             robot_orientation,
+                             useFixedBase=True)
 else:
-    id_revolute_joints = [0, 1]
-    id_robot = bullet.loadURDF("TwoJointRobot_wo_fixedJoints.urdf",
-                               robot_base, robot_orientation, useFixedBase=True)
+  id_revolute_joints = [0, 1]
+  id_robot = bullet.loadURDF("TwoJointRobot_wo_fixedJoints.urdf",
+                             robot_base,
+                             robot_orientation,
+                             useFixedBase=True)
 
+bullet.changeDynamics(id_robot, -1, linearDamping=0, angularDamping=0)
+bullet.changeDynamics(id_robot, 0, linearDamping=0, angularDamping=0)
+bullet.changeDynamics(id_robot, 1, linearDamping=0, angularDamping=0)
 
-bullet.changeDynamics(id_robot,-1,linearDamping=0, angularDamping=0)
-bullet.changeDynamics(id_robot,0,linearDamping=0, angularDamping=0)
-bullet.changeDynamics(id_robot,1,linearDamping=0, angularDamping=0)
+jointTypeNames = [
+    "JOINT_REVOLUTE", "JOINT_PRISMATIC", "JOINT_SPHERICAL", "JOINT_PLANAR", "JOINT_FIXED",
+    "JOINT_POINT2POINT", "JOINT_GEAR"
+]
 
-jointTypeNames = ["JOINT_REVOLUTE", "JOINT_PRISMATIC","JOINT_SPHERICAL","JOINT_PLANAR","JOINT_FIXED","JOINT_POINT2POINT","JOINT_GEAR"]
-    
 # Disable the motors for torque control:
-bullet.setJointMotorControlArray(id_robot, id_revolute_joints, bullet.VELOCITY_CONTROL, forces=[0.0, 0.0])
+bullet.setJointMotorControlArray(id_robot,
+                                 id_revolute_joints,
+                                 bullet.VELOCITY_CONTROL,
+                                 forces=[0.0, 0.0])
 
 # Target Positions:
 start = 0.0
 end = 1.0
 
-steps = int((end-start)/delta_t)
-t = [0]*steps
-q_pos_desired = [[0.]* steps,[0.]* steps]
-q_vel_desired = [[0.]* steps,[0.]* steps]
-q_acc_desired = [[0.]* steps,[0.]* steps]
+steps = int((end - start) / delta_t)
+t = [0] * steps
+q_pos_desired = [[0.] * steps, [0.] * steps]
+q_vel_desired = [[0.] * steps, [0.] * steps]
+q_acc_desired = [[0.] * steps, [0.] * steps]
 
 for s in range(steps):
-	t[s] = start+s*delta_t
-	q_pos_desired[0][s] = 1./(2.*math.pi) * math.sin(2. * math.pi * t[s]) - t[s]
-	q_pos_desired[1][s] = -1./(2.*math.pi) * (math.cos(2. * math.pi * t[s]) - 1.0)
-	 
-	q_vel_desired[0][s] = math.cos(2. * math.pi * t[s]) - 1.
-	q_vel_desired[1][s] = math.sin(2. * math.pi * t[s])
-	
-	q_acc_desired[0][s] = -2. * math.pi * math.sin(2. * math.pi * t[s])
-	q_acc_desired[1][s] =  2. * math.pi * math.cos(2. * math.pi * t[s])
-	 
-
-q_pos = [[0.]* steps,[0.]* steps]
-q_vel = [[0.]* steps,[0.]* steps]
-q_tor = [[0.]* steps,[0.]* steps]
+  t[s] = start + s * delta_t
+  q_pos_desired[0][s] = 1. / (2. * math.pi) * math.sin(2. * math.pi * t[s]) - t[s]
+  q_pos_desired[1][s] = -1. / (2. * math.pi) * (math.cos(2. * math.pi * t[s]) - 1.0)
+
+  q_vel_desired[0][s] = math.cos(2. * math.pi * t[s]) - 1.
+  q_vel_desired[1][s] = math.sin(2. * math.pi * t[s])
+
+  q_acc_desired[0][s] = -2. * math.pi * math.sin(2. * math.pi * t[s])
+  q_acc_desired[1][s] = 2. * math.pi * math.cos(2. * math.pi * t[s])
+
+q_pos = [[0.] * steps, [0.] * steps]
+q_vel = [[0.] * steps, [0.] * steps]
+q_tor = [[0.] * steps, [0.] * steps]
 
 # Do Torque Control:
 for i in range(len(t)):
 
-    # Read Sensor States:
-    joint_states = bullet.getJointStates(id_robot, id_revolute_joints)
-    
-    q_pos[0][i] = joint_states[0][0]
-    a = joint_states[1][0]
-    if (verbose):
-      print("joint_states[1][0]")
-      print(joint_states[1][0])
-    q_pos[1][i] = a
-    
-    q_vel[0][i] = joint_states[0][1]
-    q_vel[1][i] = joint_states[1][1]
-
-    # Computing the torque from inverse dynamics:
-    obj_pos = [q_pos[0][i], q_pos[1][i]]
-    obj_vel = [q_vel[0][i], q_vel[1][i]]
-    obj_acc = [q_acc_desired[0][i], q_acc_desired[1][i]]
-
-    if (verbose):
-	    print("calculateInverseDynamics")
-	    print("id_robot")
-	    print(id_robot)
-	    print("obj_pos")
-	    print(obj_pos)
-	    print("obj_vel")
-	    print(obj_vel)
-	    print("obj_acc")
-	    print(obj_acc)
-    
-    torque = bullet.calculateInverseDynamics(id_robot, obj_pos, obj_vel, obj_acc)
-    q_tor[0][i] = torque[0]
-    q_tor[1][i] = torque[1]
-    if (verbose):
-    	print("torque=")
-    	print(torque)
-
-    # Set the Joint Torques:
-    bullet.setJointMotorControlArray(id_robot, id_revolute_joints, bullet.TORQUE_CONTROL, forces=[torque[0], torque[1]])
-
-    # Step Simulation
-    bullet.stepSimulation()
+  # Read Sensor States:
+  joint_states = bullet.getJointStates(id_robot, id_revolute_joints)
+
+  q_pos[0][i] = joint_states[0][0]
+  a = joint_states[1][0]
+  if (verbose):
+    print("joint_states[1][0]")
+    print(joint_states[1][0])
+  q_pos[1][i] = a
+
+  q_vel[0][i] = joint_states[0][1]
+  q_vel[1][i] = joint_states[1][1]
+
+  # Computing the torque from inverse dynamics:
+  obj_pos = [q_pos[0][i], q_pos[1][i]]
+  obj_vel = [q_vel[0][i], q_vel[1][i]]
+  obj_acc = [q_acc_desired[0][i], q_acc_desired[1][i]]
+
+  if (verbose):
+    print("calculateInverseDynamics")
+    print("id_robot")
+    print(id_robot)
+    print("obj_pos")
+    print(obj_pos)
+    print("obj_vel")
+    print(obj_vel)
+    print("obj_acc")
+    print(obj_acc)
+
+  torque = bullet.calculateInverseDynamics(id_robot, obj_pos, obj_vel, obj_acc)
+  q_tor[0][i] = torque[0]
+  q_tor[1][i] = torque[1]
+  if (verbose):
+    print("torque=")
+    print(torque)
+
+  # Set the Joint Torques:
+  bullet.setJointMotorControlArray(id_robot,
+                                   id_revolute_joints,
+                                   bullet.TORQUE_CONTROL,
+                                   forces=[torque[0], torque[1]])
+
+  # Step Simulation
+  bullet.stepSimulation()
 
 # Plot the Position, Velocity and Acceleration:
 if plot:
-	figure = plt.figure(figsize=[15, 4.5])
-	figure.subplots_adjust(left=0.05, bottom=0.11, right=0.97, top=0.9, wspace=0.4, hspace=0.55)
-	
-	ax_pos = figure.add_subplot(141)
-	ax_pos.set_title("Joint Position")
-	ax_pos.plot(t, q_pos_desired[0], '--r', lw=4, label='Desired q0')
-	ax_pos.plot(t, q_pos_desired[1], '--b', lw=4, label='Desired q1')
-	ax_pos.plot(t, q_pos[0], '-r', lw=1, label='Measured q0')
-	ax_pos.plot(t, q_pos[1], '-b', lw=1, label='Measured q1')
-	ax_pos.set_ylim(-1., 1.)
-	ax_pos.legend()
-	
-	ax_vel = figure.add_subplot(142)
-	ax_vel.set_title("Joint Velocity")
-	ax_vel.plot(t, q_vel_desired[0], '--r', lw=4, label='Desired q0')
-	ax_vel.plot(t, q_vel_desired[1], '--b', lw=4, label='Desired q1')
-	ax_vel.plot(t, q_vel[0], '-r', lw=1, label='Measured q0')
-	ax_vel.plot(t, q_vel[1], '-b', lw=1, label='Measured q1')
-	ax_vel.set_ylim(-2., 2.)
-	ax_vel.legend()
-	
-	ax_acc = figure.add_subplot(143)
-	ax_acc.set_title("Joint Acceleration")
-	ax_acc.plot(t, q_acc_desired[0], '--r', lw=4, label='Desired q0')
-	ax_acc.plot(t, q_acc_desired[1], '--b', lw=4, label='Desired q1')
-	ax_acc.set_ylim(-10., 10.)
-	ax_acc.legend()
-	
-	ax_tor = figure.add_subplot(144)
-	ax_tor.set_title("Executed Torque")
-	ax_tor.plot(t, q_tor[0], '-r', lw=2, label='Torque q0')
-	ax_tor.plot(t, q_tor[1], '-b', lw=2, label='Torque q1')
-	ax_tor.set_ylim(-20., 20.)
-	ax_tor.legend()
-
-	plt.pause(0.01)	
-
+  figure = plt.figure(figsize=[15, 4.5])
+  figure.subplots_adjust(left=0.05, bottom=0.11, right=0.97, top=0.9, wspace=0.4, hspace=0.55)
+
+  ax_pos = figure.add_subplot(141)
+  ax_pos.set_title("Joint Position")
+  ax_pos.plot(t, q_pos_desired[0], '--r', lw=4, label='Desired q0')
+  ax_pos.plot(t, q_pos_desired[1], '--b', lw=4, label='Desired q1')
+  ax_pos.plot(t, q_pos[0], '-r', lw=1, label='Measured q0')
+  ax_pos.plot(t, q_pos[1], '-b', lw=1, label='Measured q1')
+  ax_pos.set_ylim(-1., 1.)
+  ax_pos.legend()
+
+  ax_vel = figure.add_subplot(142)
+  ax_vel.set_title("Joint Velocity")
+  ax_vel.plot(t, q_vel_desired[0], '--r', lw=4, label='Desired q0')
+  ax_vel.plot(t, q_vel_desired[1], '--b', lw=4, label='Desired q1')
+  ax_vel.plot(t, q_vel[0], '-r', lw=1, label='Measured q0')
+  ax_vel.plot(t, q_vel[1], '-b', lw=1, label='Measured q1')
+  ax_vel.set_ylim(-2., 2.)
+  ax_vel.legend()
+
+  ax_acc = figure.add_subplot(143)
+  ax_acc.set_title("Joint Acceleration")
+  ax_acc.plot(t, q_acc_desired[0], '--r', lw=4, label='Desired q0')
+  ax_acc.plot(t, q_acc_desired[1], '--b', lw=4, label='Desired q1')
+  ax_acc.set_ylim(-10., 10.)
+  ax_acc.legend()
+
+  ax_tor = figure.add_subplot(144)
+  ax_tor.set_title("Executed Torque")
+  ax_tor.plot(t, q_tor[0], '-r', lw=2, label='Torque q0')
+  ax_tor.plot(t, q_tor[1], '-b', lw=2, label='Torque q1')
+  ax_tor.set_ylim(-20., 20.)
+  ax_tor.legend()
+
+  plt.pause(0.01)
 
 while (1):
-    bullet.stepSimulation()
-    time.sleep(0.01)	
+  bullet.stepSimulation()
+  time.sleep(0.01)
diff --git a/examples/pybullet/examples/inverse_kinematics.py b/examples/pybullet/examples/inverse_kinematics.py
index 514829a31..d9699a369 100644
--- a/examples/pybullet/examples/inverse_kinematics.py
+++ b/examples/pybullet/examples/inverse_kinematics.py
@@ -4,35 +4,34 @@ import math
 from datetime import datetime
 
 clid = p.connect(p.SHARED_MEMORY)
-if (clid<0):
-	p.connect(p.GUI)
-p.loadURDF("plane.urdf",[0,0,-0.3])
-kukaId = p.loadURDF("kuka_iiwa/model.urdf",[0,0,0])
-p.resetBasePositionAndOrientation(kukaId,[0,0,0],[0,0,0,1])
+if (clid < 0):
+  p.connect(p.GUI)
+p.loadURDF("plane.urdf", [0, 0, -0.3])
+kukaId = p.loadURDF("kuka_iiwa/model.urdf", [0, 0, 0])
+p.resetBasePositionAndOrientation(kukaId, [0, 0, 0], [0, 0, 0, 1])
 kukaEndEffectorIndex = 6
 numJoints = p.getNumJoints(kukaId)
-if (numJoints!=7):
-	exit()
-	
+if (numJoints != 7):
+  exit()
 
 #lower limits for null space
-ll=[-.967,-2	,-2.96,0.19,-2.96,-2.09,-3.05]
+ll = [-.967, -2, -2.96, 0.19, -2.96, -2.09, -3.05]
 #upper limits for null space
-ul=[.967,2	,2.96,2.29,2.96,2.09,3.05]
+ul = [.967, 2, 2.96, 2.29, 2.96, 2.09, 3.05]
 #joint ranges for null space
-jr=[5.8,4,5.8,4,5.8,4,6]
+jr = [5.8, 4, 5.8, 4, 5.8, 4, 6]
 #restposes for null space
-rp=[0,0,0,0.5*math.pi,0,-math.pi*0.5*0.66,0]
+rp = [0, 0, 0, 0.5 * math.pi, 0, -math.pi * 0.5 * 0.66, 0]
 #joint damping coefficents
-jd=[0.1,0.1,0.1,0.1,0.1,0.1,0.1]
+jd = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
 
-for i in range (numJoints):
-	p.resetJointState(kukaId,i,rp[i])
+for i in range(numJoints):
+  p.resetJointState(kukaId, i, rp[i])
 
-p.setGravity(0,0,0)
-t=0.
-prevPose=[0,0,0]
-prevPose1=[0,0,0]
+p.setGravity(0, 0, 0)
+t = 0.
+prevPose = [0, 0, 0]
+prevPose1 = [0, 0, 0]
 hasPrevPose = 0
 useNullSpace = 1
 
@@ -46,46 +45,73 @@ p.setRealTimeSimulation(useRealTimeSimulation)
 #trailDuration is duration (in seconds) after debug lines will be removed automatically
 #use 0 for no-removal
 trailDuration = 15
-	
+
 while 1:
-	p.getCameraImage(320,200, flags=p.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX, renderer=p.ER_BULLET_HARDWARE_OPENGL)
-	if (useRealTimeSimulation):
-		dt = datetime.now()
-		t = (dt.second/60.)*2.*math.pi
-	else:
-		t=t+0.001
-	
-	if (useSimulation and useRealTimeSimulation==0):
-		p.stepSimulation()
-	
-	for i in range (1):
-		pos = [-0.4,0.2*math.cos(t),0.+0.2*math.sin(t)]
-		#end effector points down, not up (in case useOrientation==1)
-		orn = p.getQuaternionFromEuler([0,-math.pi,0])
-		
-		if (useNullSpace==1):
-			if (useOrientation==1):
-				jointPoses = p.calculateInverseKinematics(kukaId,kukaEndEffectorIndex,pos,orn,ll,ul,jr,rp)
-			else:
-				jointPoses = p.calculateInverseKinematics(kukaId,kukaEndEffectorIndex,pos,lowerLimits=ll, upperLimits=ul, jointRanges=jr, restPoses=rp)
-		else:
-			if (useOrientation==1):
-				jointPoses = p.calculateInverseKinematics(kukaId,kukaEndEffectorIndex,pos,orn,jointDamping=jd,solver=ikSolver, maxNumIterations=100, residualThreshold=.01)
-			else:
-				jointPoses = p.calculateInverseKinematics(kukaId,kukaEndEffectorIndex,pos,solver=ikSolver)
-	
-		if (useSimulation):
-			for i in range (numJoints):
-				p.setJointMotorControl2(bodyIndex=kukaId,jointIndex=i,controlMode=p.POSITION_CONTROL,targetPosition=jointPoses[i],targetVelocity=0,force=500,positionGain=0.03,velocityGain=1)
-		else:
-			#reset the joint state (ignoring all dynamics, not recommended to use during simulation)
-			for i in range (numJoints):
-				p.resetJointState(kukaId,i,jointPoses[i])
+  p.getCameraImage(320,
+                   200,
+                   flags=p.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX,
+                   renderer=p.ER_BULLET_HARDWARE_OPENGL)
+  if (useRealTimeSimulation):
+    dt = datetime.now()
+    t = (dt.second / 60.) * 2. * math.pi
+  else:
+    t = t + 0.001
+
+  if (useSimulation and useRealTimeSimulation == 0):
+    p.stepSimulation()
+
+  for i in range(1):
+    pos = [-0.4, 0.2 * math.cos(t), 0. + 0.2 * math.sin(t)]
+    #end effector points down, not up (in case useOrientation==1)
+    orn = p.getQuaternionFromEuler([0, -math.pi, 0])
+
+    if (useNullSpace == 1):
+      if (useOrientation == 1):
+        jointPoses = p.calculateInverseKinematics(kukaId, kukaEndEffectorIndex, pos, orn, ll, ul,
+                                                  jr, rp)
+      else:
+        jointPoses = p.calculateInverseKinematics(kukaId,
+                                                  kukaEndEffectorIndex,
+                                                  pos,
+                                                  lowerLimits=ll,
+                                                  upperLimits=ul,
+                                                  jointRanges=jr,
+                                                  restPoses=rp)
+    else:
+      if (useOrientation == 1):
+        jointPoses = p.calculateInverseKinematics(kukaId,
+                                                  kukaEndEffectorIndex,
+                                                  pos,
+                                                  orn,
+                                                  jointDamping=jd,
+                                                  solver=ikSolver,
+                                                  maxNumIterations=100,
+                                                  residualThreshold=.01)
+      else:
+        jointPoses = p.calculateInverseKinematics(kukaId,
+                                                  kukaEndEffectorIndex,
+                                                  pos,
+                                                  solver=ikSolver)
+
+    if (useSimulation):
+      for i in range(numJoints):
+        p.setJointMotorControl2(bodyIndex=kukaId,
+                                jointIndex=i,
+                                controlMode=p.POSITION_CONTROL,
+                                targetPosition=jointPoses[i],
+                                targetVelocity=0,
+                                force=500,
+                                positionGain=0.03,
+                                velocityGain=1)
+    else:
+      #reset the joint state (ignoring all dynamics, not recommended to use during simulation)
+      for i in range(numJoints):
+        p.resetJointState(kukaId, i, jointPoses[i])
 
-	ls = p.getLinkState(kukaId,kukaEndEffectorIndex)	
-	if (hasPrevPose):
-		p.addUserDebugLine(prevPose,pos,[0,0,0.3],1,trailDuration)
-		p.addUserDebugLine(prevPose1,ls[4],[1,0,0],1,trailDuration)
-	prevPose=pos
-	prevPose1=ls[4]
-	hasPrevPose = 1		
+  ls = p.getLinkState(kukaId, kukaEndEffectorIndex)
+  if (hasPrevPose):
+    p.addUserDebugLine(prevPose, pos, [0, 0, 0.3], 1, trailDuration)
+    p.addUserDebugLine(prevPose1, ls[4], [1, 0, 0], 1, trailDuration)
+  prevPose = pos
+  prevPose1 = ls[4]
+  hasPrevPose = 1
diff --git a/examples/pybullet/examples/inverse_kinematics_husky_kuka.py b/examples/pybullet/examples/inverse_kinematics_husky_kuka.py
index eb28102b8..7b3bb793d 100644
--- a/examples/pybullet/examples/inverse_kinematics_husky_kuka.py
+++ b/examples/pybullet/examples/inverse_kinematics_husky_kuka.py
@@ -5,57 +5,57 @@ from datetime import datetime
 from datetime import datetime
 
 clid = p.connect(p.SHARED_MEMORY)
-if (clid<0):
-	p.connect(p.GUI)
-p.loadURDF("plane.urdf",[0,0,-0.3])
-husky = p.loadURDF("husky/husky.urdf",[0.290388,0.329902,-0.310270],[0.002328,-0.000984,0.996491,0.083659])
-for i in range (p.getNumJoints(husky)):
-	print(p.getJointInfo(husky,i))
-kukaId  = p.loadURDF("kuka_iiwa/model_free_base.urdf", 0.193749,0.345564,0.120208,0.002327,-0.000988,0.996491,0.083659)
+if (clid < 0):
+  p.connect(p.GUI)
+p.loadURDF("plane.urdf", [0, 0, -0.3])
+husky = p.loadURDF("husky/husky.urdf", [0.290388, 0.329902, -0.310270],
+                   [0.002328, -0.000984, 0.996491, 0.083659])
+for i in range(p.getNumJoints(husky)):
+  print(p.getJointInfo(husky, i))
+kukaId = p.loadURDF("kuka_iiwa/model_free_base.urdf", 0.193749, 0.345564, 0.120208, 0.002327,
+                    -0.000988, 0.996491, 0.083659)
 ob = kukaId
-jointPositions=[ 3.559609, 0.411182, 0.862129, 1.744441, 0.077299, -1.129685, 0.006001 ]
-for jointIndex in range (p.getNumJoints(ob)):
-	p.resetJointState(ob,jointIndex,jointPositions[jointIndex])
-
+jointPositions = [3.559609, 0.411182, 0.862129, 1.744441, 0.077299, -1.129685, 0.006001]
+for jointIndex in range(p.getNumJoints(ob)):
+  p.resetJointState(ob, jointIndex, jointPositions[jointIndex])
 
 #put kuka on top of husky
 
-cid = p.createConstraint(husky,-1,kukaId,-1,p.JOINT_FIXED,[0,0,0],[0,0,0],[0.,0.,-.5],[0,0,0,1])
-
+cid = p.createConstraint(husky, -1, kukaId, -1, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0], [0., 0., -.5],
+                         [0, 0, 0, 1])
 
-baseorn = p.getQuaternionFromEuler([3.1415,0,0.3])
-baseorn = [0,0,0,1]
+baseorn = p.getQuaternionFromEuler([3.1415, 0, 0.3])
+baseorn = [0, 0, 0, 1]
 #[0, 0, 0.707, 0.707]
 
 #p.resetBasePositionAndOrientation(kukaId,[0,0,0],baseorn)#[0,0,0,1])
 kukaEndEffectorIndex = 6
 numJoints = p.getNumJoints(kukaId)
-if (numJoints!=7):
-	exit()
-	
+if (numJoints != 7):
+  exit()
 
 #lower limits for null space
-ll=[-.967,-2	,-2.96,0.19,-2.96,-2.09,-3.05]
+ll = [-.967, -2, -2.96, 0.19, -2.96, -2.09, -3.05]
 #upper limits for null space
-ul=[.967,2	,2.96,2.29,2.96,2.09,3.05]
+ul = [.967, 2, 2.96, 2.29, 2.96, 2.09, 3.05]
 #joint ranges for null space
-jr=[5.8,4,5.8,4,5.8,4,6]
+jr = [5.8, 4, 5.8, 4, 5.8, 4, 6]
 #restposes for null space
-rp=[0,0,0,0.5*math.pi,0,-math.pi*0.5*0.66,0]
+rp = [0, 0, 0, 0.5 * math.pi, 0, -math.pi * 0.5 * 0.66, 0]
 #joint damping coefficents
-jd=[0.1,0.1,0.1,0.1,0.1,0.1,0.1]
+jd = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
 
-for i in range (numJoints):
-	p.resetJointState(kukaId,i,rp[i])
+for i in range(numJoints):
+  p.resetJointState(kukaId, i, rp[i])
 
-p.setGravity(0,0,-10)
-t=0.
-prevPose=[0,0,0]
-prevPose1=[0,0,0]
+p.setGravity(0, 0, -10)
+t = 0.
+prevPose = [0, 0, 0]
+prevPose1 = [0, 0, 0]
 hasPrevPose = 0
 useNullSpace = 0
 
-useOrientation =0 
+useOrientation = 0
 #If we set useSimulation=0, it sets the arm pose to be the IK result directly without using dynamic control.
 #This can be used to test the IK result accuracy.
 useSimulation = 0
@@ -64,106 +64,130 @@ p.setRealTimeSimulation(useRealTimeSimulation)
 #trailDuration is duration (in seconds) after debug lines will be removed automatically
 #use 0 for no-removal
 trailDuration = 15
-basepos =[0,0,0]	
+basepos = [0, 0, 0]
+ang = 0
 ang = 0
-ang=0
-
-def accurateCalculateInverseKinematics( kukaId, endEffectorId, targetPos, threshold, maxIter):
-	closeEnough = False
-	iter = 0
-	dist2 = 1e30
-	while (not closeEnough and iter<maxIter):
-		jointPoses = p.calculateInverseKinematics(kukaId,kukaEndEffectorIndex,targetPos)
-		for i in range (numJoints):
-			p.resetJointState(kukaId,i,jointPoses[i])
-		ls = p.getLinkState(kukaId,kukaEndEffectorIndex)	
-		newPos = ls[4]
-		diff = [targetPos[0]-newPos[0],targetPos[1]-newPos[1],targetPos[2]-newPos[2]]
-		dist2 = (diff[0]*diff[0] + diff[1]*diff[1] + diff[2]*diff[2])
-		closeEnough = (dist2 < threshold)
-		iter=iter+1
-	#print ("Num iter: "+str(iter) + "threshold: "+str(dist2))
-	return jointPoses
-
-
-wheels=[2,3,4,5]
+
+
+def accurateCalculateInverseKinematics(kukaId, endEffectorId, targetPos, threshold, maxIter):
+  closeEnough = False
+  iter = 0
+  dist2 = 1e30
+  while (not closeEnough and iter < maxIter):
+    jointPoses = p.calculateInverseKinematics(kukaId, kukaEndEffectorIndex, targetPos)
+    for i in range(numJoints):
+      p.resetJointState(kukaId, i, jointPoses[i])
+    ls = p.getLinkState(kukaId, kukaEndEffectorIndex)
+    newPos = ls[4]
+    diff = [targetPos[0] - newPos[0], targetPos[1] - newPos[1], targetPos[2] - newPos[2]]
+    dist2 = (diff[0] * diff[0] + diff[1] * diff[1] + diff[2] * diff[2])
+    closeEnough = (dist2 < threshold)
+    iter = iter + 1
+  #print ("Num iter: "+str(iter) + "threshold: "+str(dist2))
+  return jointPoses
+
+
+wheels = [2, 3, 4, 5]
 #(2, b'front_left_wheel', 0, 7, 6, 1, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, b'front_left_wheel_link')
 #(3, b'front_right_wheel', 0, 8, 7, 1, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, b'front_right_wheel_link')
 #(4, b'rear_left_wheel', 0, 9, 8, 1, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, b'rear_left_wheel_link')
 #(5, b'rear_right_wheel', 0, 10, 9, 1, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, b'rear_right_wheel_link')
-wheelVelocities=[0,0,0,0]
-wheelDeltasTurn=[1,-1,1,-1]
-wheelDeltasFwd=[1,1,1,1]
+wheelVelocities = [0, 0, 0, 0]
+wheelDeltasTurn = [1, -1, 1, -1]
+wheelDeltasFwd = [1, 1, 1, 1]
 while 1:
-	keys = p.getKeyboardEvents()
-	shift = 0.01
-	wheelVelocities=[0,0,0,0]
-	speed = 1.0
-	for k in keys:
-		if ord('s') in keys:
-				p.saveWorld("state.py")
-		if ord('a') in keys:
-			basepos =  basepos=[basepos[0],basepos[1]-shift,basepos[2]]
-		if ord('d') in keys:
-                        basepos =  basepos=[basepos[0],basepos[1]+shift,basepos[2]]
-
-		if p.B3G_LEFT_ARROW in keys:
-			for i in range(len(wheels)):
-				wheelVelocities[i] =  wheelVelocities[i] - speed*wheelDeltasTurn[i]
-		if p.B3G_RIGHT_ARROW in keys:
-			for i in range(len(wheels)):
-				wheelVelocities[i] =  wheelVelocities[i] +speed*wheelDeltasTurn[i]
-		if p.B3G_UP_ARROW in keys:
-			for i in range(len(wheels)):
-				wheelVelocities[i] = wheelVelocities[i] + speed*wheelDeltasFwd[i]
-		if p.B3G_DOWN_ARROW in keys:
-			for i in range(len(wheels)):
-				wheelVelocities[i] = wheelVelocities[i]  -speed*wheelDeltasFwd[i]
-
-	baseorn = p.getQuaternionFromEuler([0,0,ang])
-	for i in range(len(wheels)):
-		p.setJointMotorControl2(husky,wheels[i],p.VELOCITY_CONTROL,targetVelocity=wheelVelocities[i], force=1000)
-	#p.resetBasePositionAndOrientation(kukaId,basepos,baseorn)#[0,0,0,1])
-	if (useRealTimeSimulation):
-		t = time.time()#(dt, micro) = datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S.%f').split('.')
-		#t = (dt.second/60.)*2.*math.pi
-	else:
-		t=t+0.001
-	
-	if (useSimulation and useRealTimeSimulation==0):
-		p.stepSimulation()
-	
-	for i in range (1):
-		#pos = [-0.4,0.2*math.cos(t),0.+0.2*math.sin(t)]
-		pos = [0.2*math.cos(t),0,0.+0.2*math.sin(t)+0.7]
-		#end effector points down, not up (in case useOrientation==1)
-		orn = p.getQuaternionFromEuler([0,-math.pi,0])
-		
-		if (useNullSpace==1):
-			if (useOrientation==1):
-				jointPoses = p.calculateInverseKinematics(kukaId,kukaEndEffectorIndex,pos,orn,ll,ul,jr,rp)
-			else:
-				jointPoses = p.calculateInverseKinematics(kukaId,kukaEndEffectorIndex,pos,lowerLimits=ll, upperLimits=ul, jointRanges=jr, restPoses=rp)
-		else:
-			if (useOrientation==1):
-				jointPoses = p.calculateInverseKinematics(kukaId,kukaEndEffectorIndex,pos,orn,jointDamping=jd)
-			else:
-				threshold =0.001
-				maxIter = 100
-				jointPoses = accurateCalculateInverseKinematics(kukaId,kukaEndEffectorIndex,pos, threshold, maxIter)
-	
-		if (useSimulation):
-			for i in range (numJoints):
-				p.setJointMotorControl2(bodyIndex=kukaId,jointIndex=i,controlMode=p.POSITION_CONTROL,targetPosition=jointPoses[i],targetVelocity=0,force=500,positionGain=1,velocityGain=0.1)
-		else:
-			#reset the joint state (ignoring all dynamics, not recommended to use during simulation)
-			for i in range (numJoints):
-				p.resetJointState(kukaId,i,jointPoses[i])
-
-	ls = p.getLinkState(kukaId,kukaEndEffectorIndex)	
-	if (hasPrevPose):
-		p.addUserDebugLine(prevPose,pos,[0,0,0.3],1,trailDuration)
-		p.addUserDebugLine(prevPose1,ls[4],[1,0,0],1,trailDuration)
-	prevPose=pos
-	prevPose1=ls[4]
-	hasPrevPose = 1		
+  keys = p.getKeyboardEvents()
+  shift = 0.01
+  wheelVelocities = [0, 0, 0, 0]
+  speed = 1.0
+  for k in keys:
+    if ord('s') in keys:
+      p.saveWorld("state.py")
+    if ord('a') in keys:
+      basepos = basepos = [basepos[0], basepos[1] - shift, basepos[2]]
+    if ord('d') in keys:
+      basepos = basepos = [basepos[0], basepos[1] + shift, basepos[2]]
+
+    if p.B3G_LEFT_ARROW in keys:
+      for i in range(len(wheels)):
+        wheelVelocities[i] = wheelVelocities[i] - speed * wheelDeltasTurn[i]
+    if p.B3G_RIGHT_ARROW in keys:
+      for i in range(len(wheels)):
+        wheelVelocities[i] = wheelVelocities[i] + speed * wheelDeltasTurn[i]
+    if p.B3G_UP_ARROW in keys:
+      for i in range(len(wheels)):
+        wheelVelocities[i] = wheelVelocities[i] + speed * wheelDeltasFwd[i]
+    if p.B3G_DOWN_ARROW in keys:
+      for i in range(len(wheels)):
+        wheelVelocities[i] = wheelVelocities[i] - speed * wheelDeltasFwd[i]
+
+  baseorn = p.getQuaternionFromEuler([0, 0, ang])
+  for i in range(len(wheels)):
+    p.setJointMotorControl2(husky,
+                            wheels[i],
+                            p.VELOCITY_CONTROL,
+                            targetVelocity=wheelVelocities[i],
+                            force=1000)
+  #p.resetBasePositionAndOrientation(kukaId,basepos,baseorn)#[0,0,0,1])
+  if (useRealTimeSimulation):
+    t = time.time()  #(dt, micro) = datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S.%f').split('.')
+    #t = (dt.second/60.)*2.*math.pi
+  else:
+    t = t + 0.001
+
+  if (useSimulation and useRealTimeSimulation == 0):
+    p.stepSimulation()
+
+  for i in range(1):
+    #pos = [-0.4,0.2*math.cos(t),0.+0.2*math.sin(t)]
+    pos = [0.2 * math.cos(t), 0, 0. + 0.2 * math.sin(t) + 0.7]
+    #end effector points down, not up (in case useOrientation==1)
+    orn = p.getQuaternionFromEuler([0, -math.pi, 0])
+
+    if (useNullSpace == 1):
+      if (useOrientation == 1):
+        jointPoses = p.calculateInverseKinematics(kukaId, kukaEndEffectorIndex, pos, orn, ll, ul,
+                                                  jr, rp)
+      else:
+        jointPoses = p.calculateInverseKinematics(kukaId,
+                                                  kukaEndEffectorIndex,
+                                                  pos,
+                                                  lowerLimits=ll,
+                                                  upperLimits=ul,
+                                                  jointRanges=jr,
+                                                  restPoses=rp)
+    else:
+      if (useOrientation == 1):
+        jointPoses = p.calculateInverseKinematics(kukaId,
+                                                  kukaEndEffectorIndex,
+                                                  pos,
+                                                  orn,
+                                                  jointDamping=jd)
+      else:
+        threshold = 0.001
+        maxIter = 100
+        jointPoses = accurateCalculateInverseKinematics(kukaId, kukaEndEffectorIndex, pos,
+                                                        threshold, maxIter)
+
+    if (useSimulation):
+      for i in range(numJoints):
+        p.setJointMotorControl2(bodyIndex=kukaId,
+                                jointIndex=i,
+                                controlMode=p.POSITION_CONTROL,
+                                targetPosition=jointPoses[i],
+                                targetVelocity=0,
+                                force=500,
+                                positionGain=1,
+                                velocityGain=0.1)
+    else:
+      #reset the joint state (ignoring all dynamics, not recommended to use during simulation)
+      for i in range(numJoints):
+        p.resetJointState(kukaId, i, jointPoses[i])
+
+  ls = p.getLinkState(kukaId, kukaEndEffectorIndex)
+  if (hasPrevPose):
+    p.addUserDebugLine(prevPose, pos, [0, 0, 0.3], 1, trailDuration)
+    p.addUserDebugLine(prevPose1, ls[4], [1, 0, 0], 1, trailDuration)
+  prevPose = pos
+  prevPose1 = ls[4]
+  hasPrevPose = 1
diff --git a/examples/pybullet/examples/inverse_kinematics_pole.py b/examples/pybullet/examples/inverse_kinematics_pole.py
index 471523fc0..fec0dbaf6 100755
--- a/examples/pybullet/examples/inverse_kinematics_pole.py
+++ b/examples/pybullet/examples/inverse_kinematics_pole.py
@@ -4,24 +4,23 @@ import math
 from datetime import datetime
 
 clid = p.connect(p.SHARED_MEMORY)
-if (clid<0):
-	p.connect(p.GUI)
-p.loadURDF("plane.urdf",[0,0,-1.3])
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)
-sawyerId = p.loadURDF("pole.urdf",[0,0,0])
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
-p.resetBasePositionAndOrientation(sawyerId,[0,0,0],[0,0,0,1])
-
+if (clid < 0):
+  p.connect(p.GUI)
+p.loadURDF("plane.urdf", [0, 0, -1.3])
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
+sawyerId = p.loadURDF("pole.urdf", [0, 0, 0])
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
+p.resetBasePositionAndOrientation(sawyerId, [0, 0, 0], [0, 0, 0, 1])
 
 sawyerEndEffectorIndex = 3
 numJoints = p.getNumJoints(sawyerId)
 #joint damping coefficents
-jd=[0.1,0.1,0.1,0.1]
+jd = [0.1, 0.1, 0.1, 0.1]
 
-p.setGravity(0,0,0)
-t=0.
-prevPose=[0,0,0]
-prevPose1=[0,0,0]
+p.setGravity(0, 0, 0)
+t = 0.
+prevPose = [0, 0, 0]
+prevPose1 = [0, 0, 0]
 hasPrevPose = 0
 
 ikSolver = 0
@@ -30,30 +29,35 @@ p.setRealTimeSimulation(useRealTimeSimulation)
 #trailDuration is duration (in seconds) after debug lines will be removed automatically
 #use 0 for no-removal
 trailDuration = 1
-	
+
 while 1:
-	if (useRealTimeSimulation):
-		dt = datetime.now()
-		t = (dt.second/60.)*2.*math.pi
-	else:
-		t=t+0.01
-		time.sleep(0.01)
-	
-	for i in range (1):
-		pos = [2.*math.cos(t),2.*math.cos(t),0.+2.*math.sin(t)]
-		jointPoses = p.calculateInverseKinematics(sawyerId,sawyerEndEffectorIndex,pos,jointDamping=jd,solver=ikSolver, maxNumIterations=100)
-
-		#reset the joint state (ignoring all dynamics, not recommended to use during simulation)
-		for i in range (numJoints):
-			jointInfo = p.getJointInfo(sawyerId, i)
-			qIndex = jointInfo[3]
-			if qIndex > -1:
-				p.resetJointState(sawyerId,i,jointPoses[qIndex-7])
-
-	ls = p.getLinkState(sawyerId,sawyerEndEffectorIndex)
-	if (hasPrevPose):
-		p.addUserDebugLine(prevPose,pos,[0,0,0.3],1,trailDuration)
-		p.addUserDebugLine(prevPose1,ls[4],[1,0,0],1,trailDuration)
-	prevPose=pos
-	prevPose1=ls[4]
-	hasPrevPose = 1		
+  if (useRealTimeSimulation):
+    dt = datetime.now()
+    t = (dt.second / 60.) * 2. * math.pi
+  else:
+    t = t + 0.01
+    time.sleep(0.01)
+
+  for i in range(1):
+    pos = [2. * math.cos(t), 2. * math.cos(t), 0. + 2. * math.sin(t)]
+    jointPoses = p.calculateInverseKinematics(sawyerId,
+                                              sawyerEndEffectorIndex,
+                                              pos,
+                                              jointDamping=jd,
+                                              solver=ikSolver,
+                                              maxNumIterations=100)
+
+    #reset the joint state (ignoring all dynamics, not recommended to use during simulation)
+    for i in range(numJoints):
+      jointInfo = p.getJointInfo(sawyerId, i)
+      qIndex = jointInfo[3]
+      if qIndex > -1:
+        p.resetJointState(sawyerId, i, jointPoses[qIndex - 7])
+
+  ls = p.getLinkState(sawyerId, sawyerEndEffectorIndex)
+  if (hasPrevPose):
+    p.addUserDebugLine(prevPose, pos, [0, 0, 0.3], 1, trailDuration)
+    p.addUserDebugLine(prevPose1, ls[4], [1, 0, 0], 1, trailDuration)
+  prevPose = pos
+  prevPose1 = ls[4]
+  hasPrevPose = 1
diff --git a/examples/pybullet/examples/jacobian.py b/examples/pybullet/examples/jacobian.py
index 0596e95b3..3cdb4752e 100644
--- a/examples/pybullet/examples/jacobian.py
+++ b/examples/pybullet/examples/jacobian.py
@@ -2,47 +2,54 @@ import pybullet as p
 
 
 def getJointStates(robot):
-	joint_states = p.getJointStates(robot, range(p.getNumJoints(robot)))
-	joint_positions = [state[0] for state in joint_states]
-	joint_velocities = [state[1] for state in joint_states]
-	joint_torques = [state[3] for state in joint_states]
-	return joint_positions, joint_velocities, joint_torques
+  joint_states = p.getJointStates(robot, range(p.getNumJoints(robot)))
+  joint_positions = [state[0] for state in joint_states]
+  joint_velocities = [state[1] for state in joint_states]
+  joint_torques = [state[3] for state in joint_states]
+  return joint_positions, joint_velocities, joint_torques
+
 
 def getMotorJointStates(robot):
-	joint_states = p.getJointStates(robot, range(p.getNumJoints(robot)))
-	joint_infos = [p.getJointInfo(robot, i) for i in range(p.getNumJoints(robot))]
-	joint_states = [j for j, i in zip(joint_states, joint_infos) if i[3] > -1]
-	joint_positions = [state[0] for state in joint_states]
-	joint_velocities = [state[1] for state in joint_states]
-	joint_torques = [state[3] for state in joint_states]
-	return joint_positions, joint_velocities, joint_torques
+  joint_states = p.getJointStates(robot, range(p.getNumJoints(robot)))
+  joint_infos = [p.getJointInfo(robot, i) for i in range(p.getNumJoints(robot))]
+  joint_states = [j for j, i in zip(joint_states, joint_infos) if i[3] > -1]
+  joint_positions = [state[0] for state in joint_states]
+  joint_velocities = [state[1] for state in joint_states]
+  joint_torques = [state[3] for state in joint_states]
+  return joint_positions, joint_velocities, joint_torques
+
 
 def setJointPosition(robot, position, kp=1.0, kv=0.3):
-	num_joints = p.getNumJoints(robot)
-	zero_vec = [0.0] * num_joints
-	if len(position) == num_joints:
-		p.setJointMotorControlArray(robot, range(num_joints), p.POSITION_CONTROL,
-			targetPositions=position, targetVelocities=zero_vec,
-			positionGains=[kp] * num_joints, velocityGains=[kv] * num_joints)
-	else:
-		print("Not setting torque. "
-			  "Expected torque vector of "
-			  "length {}, got {}".format(num_joints, len(torque)))
+  num_joints = p.getNumJoints(robot)
+  zero_vec = [0.0] * num_joints
+  if len(position) == num_joints:
+    p.setJointMotorControlArray(robot,
+                                range(num_joints),
+                                p.POSITION_CONTROL,
+                                targetPositions=position,
+                                targetVelocities=zero_vec,
+                                positionGains=[kp] * num_joints,
+                                velocityGains=[kv] * num_joints)
+  else:
+    print("Not setting torque. "
+          "Expected torque vector of "
+          "length {}, got {}".format(num_joints, len(torque)))
+
 
 def multiplyJacobian(robot, jacobian, vector):
-	result = [0.0, 0.0, 0.0]
-	i = 0
-	for c in range(len(vector)):
-		if p.getJointInfo(robot, c)[3] > -1:
-			for r in range(3):
-				result[r] += jacobian[r][i] * vector[c]
-			i += 1
-	return result
+  result = [0.0, 0.0, 0.0]
+  i = 0
+  for c in range(len(vector)):
+    if p.getJointInfo(robot, c)[3] > -1:
+      for r in range(3):
+        result[r] += jacobian[r][i] * vector[c]
+      i += 1
+  return result
 
 
 clid = p.connect(p.SHARED_MEMORY)
-if (clid<0):
-	p.connect(p.DIRECT)
+if (clid < 0):
+  p.connect(p.DIRECT)
 
 time_step = 0.001
 gravity_constant = -9.81
@@ -50,14 +57,14 @@ p.resetSimulation()
 p.setTimeStep(time_step)
 p.setGravity(0.0, 0.0, gravity_constant)
 
-p.loadURDF("plane.urdf",[0,0,-0.3])
+p.loadURDF("plane.urdf", [0, 0, -0.3])
 
 kukaId = p.loadURDF("TwoJointRobot_w_fixedJoints.urdf", useFixedBase=True)
 #kukaId = p.loadURDF("TwoJointRobot_w_fixedJoints.urdf",[0,0,0])
 #kukaId = p.loadURDF("kuka_iiwa/model.urdf",[0,0,0])
 #kukaId = p.loadURDF("kuka_lwr/kuka.urdf",[0,0,0])
 #kukaId = p.loadURDF("humanoid/nao.urdf",[0,0,0])
-p.resetBasePositionAndOrientation(kukaId,[0,0,0],[0,0,0,1])
+p.resetBasePositionAndOrientation(kukaId, [0, 0, 0], [0, 0, 0, 1])
 numJoints = p.getNumJoints(kukaId)
 kukaEndEffectorIndex = numJoints - 1
 
@@ -69,7 +76,10 @@ p.stepSimulation()
 pos, vel, torq = getJointStates(kukaId)
 mpos, mvel, mtorq = getMotorJointStates(kukaId)
 
-result = p.getLinkState(kukaId, kukaEndEffectorIndex, computeLinkVelocity=1, computeForwardKinematics=1)
+result = p.getLinkState(kukaId,
+                        kukaEndEffectorIndex,
+                        computeLinkVelocity=1,
+                        computeForwardKinematics=1)
 link_trn, link_rot, com_trn, com_rot, frame_pos, frame_rot, link_vt, link_vr = result
 # Get the Jacobians for the CoM of the end-effector link.
 # Note that in this example com_rot = identity, and we would need to use com_rot.T * com_trn.
@@ -78,11 +88,11 @@ link_trn, link_rot, com_trn, com_rot, frame_pos, frame_rot, link_vt, link_vr = r
 zero_vec = [0.0] * len(mpos)
 jac_t, jac_r = p.calculateJacobian(kukaId, kukaEndEffectorIndex, com_trn, mpos, zero_vec, zero_vec)
 
-print ("Link linear velocity of CoM from getLinkState:")
-print (link_vt)
-print ("Link linear velocity of CoM from linearJacobian * q_dot:")
-print (multiplyJacobian(kukaId, jac_t, vel))
-print ("Link angular velocity of CoM from getLinkState:")
-print (link_vr)
-print ("Link angular velocity of CoM from angularJacobian * q_dot:")
-print (multiplyJacobian(kukaId, jac_r, vel))
+print("Link linear velocity of CoM from getLinkState:")
+print(link_vt)
+print("Link linear velocity of CoM from linearJacobian * q_dot:")
+print(multiplyJacobian(kukaId, jac_t, vel))
+print("Link angular velocity of CoM from getLinkState:")
+print(link_vr)
+print("Link angular velocity of CoM from angularJacobian * q_dot:")
+print(multiplyJacobian(kukaId, jac_r, vel))
diff --git a/examples/pybullet/examples/jointFrictionAndMotor.py b/examples/pybullet/examples/jointFrictionAndMotor.py
index 5ecba00bd..028fac915 100644
--- a/examples/pybullet/examples/jointFrictionAndMotor.py
+++ b/examples/pybullet/examples/jointFrictionAndMotor.py
@@ -2,25 +2,23 @@ import pybullet as p
 import time
 
 p.connect(p.GUI)
-door=p.loadURDF("door.urdf")
+door = p.loadURDF("door.urdf")
 
 #linear/angular damping for base and all children=0
-p.changeDynamics(door,-1,linearDamping=0, angularDamping=0)
-for j in range (p.getNumJoints(door)):
-	p.changeDynamics(door,j,linearDamping=0, angularDamping=0)
-	print(p.getJointInfo(door,j))
+p.changeDynamics(door, -1, linearDamping=0, angularDamping=0)
+for j in range(p.getNumJoints(door)):
+  p.changeDynamics(door, j, linearDamping=0, angularDamping=0)
+  print(p.getJointInfo(door, j))
 
-
-frictionId = p.addUserDebugParameter("jointFriction",0,20,10)
-torqueId = p.addUserDebugParameter("joint torque",0,20,5)
+frictionId = p.addUserDebugParameter("jointFriction", 0, 20, 10)
+torqueId = p.addUserDebugParameter("joint torque", 0, 20, 5)
 
 while (1):
-	frictionForce = p.readUserDebugParameter(frictionId)
-	jointTorque = p.readUserDebugParameter(torqueId)
-	#set the joint friction	
-	p.setJointMotorControl2(door,1,p.VELOCITY_CONTROL,targetVelocity=0,force=frictionForce)
-	#apply a joint torque
-	p.setJointMotorControl2(door,1,p.TORQUE_CONTROL,force=jointTorque)
-	p.stepSimulation()
-	time.sleep(0.01)
-	
\ No newline at end of file
+  frictionForce = p.readUserDebugParameter(frictionId)
+  jointTorque = p.readUserDebugParameter(torqueId)
+  #set the joint friction
+  p.setJointMotorControl2(door, 1, p.VELOCITY_CONTROL, targetVelocity=0, force=frictionForce)
+  #apply a joint torque
+  p.setJointMotorControl2(door, 1, p.TORQUE_CONTROL, force=jointTorque)
+  p.stepSimulation()
+  time.sleep(0.01)
diff --git a/examples/pybullet/examples/jointFrictionDamping.py b/examples/pybullet/examples/jointFrictionDamping.py
index efe34e12f..a69c021d8 100644
--- a/examples/pybullet/examples/jointFrictionDamping.py
+++ b/examples/pybullet/examples/jointFrictionDamping.py
@@ -2,27 +2,34 @@ import pybullet as p
 import time
 
 p.connect(p.GUI)
-p.loadURDF("plane.urdf",[0,0,-0.25])
-minitaur = p.loadURDF("quadruped/minitaur_single_motor.urdf",useFixedBase=True)
+p.loadURDF("plane.urdf", [0, 0, -0.25])
+minitaur = p.loadURDF("quadruped/minitaur_single_motor.urdf", useFixedBase=True)
 print(p.getNumJoints(minitaur))
-p.resetDebugVisualizerCamera(cameraDistance=1, cameraYaw=23.2, cameraPitch=-6.6,cameraTargetPosition=[-0.064,.621,-0.2])
+p.resetDebugVisualizerCamera(cameraDistance=1,
+                             cameraYaw=23.2,
+                             cameraPitch=-6.6,
+                             cameraTargetPosition=[-0.064, .621, -0.2])
 motorJointId = 1
-p.setJointMotorControl2(minitaur,motorJointId,p.VELOCITY_CONTROL,targetVelocity=100000,force=0)
+p.setJointMotorControl2(minitaur, motorJointId, p.VELOCITY_CONTROL, targetVelocity=100000, force=0)
 
-p.resetJointState(minitaur,motorJointId,targetValue=0, targetVelocity=1)
-angularDampingSlider = p.addUserDebugParameter("angularDamping", 0,1,0)
-jointFrictionForceSlider = p.addUserDebugParameter("jointFrictionForce", 0,0.1,0)
+p.resetJointState(minitaur, motorJointId, targetValue=0, targetVelocity=1)
+angularDampingSlider = p.addUserDebugParameter("angularDamping", 0, 1, 0)
+jointFrictionForceSlider = p.addUserDebugParameter("jointFrictionForce", 0, 0.1, 0)
 
-textId = p.addUserDebugText("jointVelocity=0",[0,0,-0.2])
+textId = p.addUserDebugText("jointVelocity=0", [0, 0, -0.2])
 p.setRealTimeSimulation(1)
 while (1):
-    frictionForce = p.readUserDebugParameter(jointFrictionForceSlider)
-    angularDamping = p.readUserDebugParameter(angularDampingSlider)
-    p.setJointMotorControl2(minitaur,motorJointId,p.VELOCITY_CONTROL,targetVelocity=0,force=frictionForce)
-    p.changeDynamics(minitaur,motorJointId,linearDamping=0, angularDamping=angularDamping)
+  frictionForce = p.readUserDebugParameter(jointFrictionForceSlider)
+  angularDamping = p.readUserDebugParameter(angularDampingSlider)
+  p.setJointMotorControl2(minitaur,
+                          motorJointId,
+                          p.VELOCITY_CONTROL,
+                          targetVelocity=0,
+                          force=frictionForce)
+  p.changeDynamics(minitaur, motorJointId, linearDamping=0, angularDamping=angularDamping)
 
-    time.sleep(0.01)
-    txt = "jointVelocity="+str(p.getJointState(minitaur,motorJointId)[1])
-    prevTextId = textId
-    textId = p.addUserDebugText(txt,[0,0,-0.2])
-    p.removeUserDebugItem(prevTextId) 
+  time.sleep(0.01)
+  txt = "jointVelocity=" + str(p.getJointState(minitaur, motorJointId)[1])
+  prevTextId = textId
+  textId = p.addUserDebugText(txt, [0, 0, -0.2])
+  p.removeUserDebugItem(prevTextId)
diff --git a/examples/pybullet/examples/kuka_grasp_block_playback.py b/examples/pybullet/examples/kuka_grasp_block_playback.py
index 6a786ad0d..1d0fbeb8c 100644
--- a/examples/pybullet/examples/kuka_grasp_block_playback.py
+++ b/examples/pybullet/examples/kuka_grasp_block_playback.py
@@ -2,15 +2,15 @@ import pybullet as p
 import struct
 
 
-def readLogFile(filename, verbose = True):
+def readLogFile(filename, verbose=True):
   f = open(filename, 'rb')
-  
+
   print('Opened'),
   print(filename)
 
   keys = f.readline().decode('utf8').rstrip('\n').split(',')
   fmt = f.readline().decode('utf8').rstrip('\n')
-  
+
   # The byte number of one record
   sz = struct.calcsize(fmt)
   # The type number of one record
@@ -41,11 +41,12 @@ def readLogFile(filename, verbose = True):
 
   return log
 
+
 #clid = p.connect(p.SHARED_MEMORY)
 p.connect(p.GUI)
 p.loadSDF("kuka_iiwa/kuka_with_gripper.sdf")
-p.loadURDF("tray/tray.urdf",[0,0,0])
-p.loadURDF("block.urdf",[0,0,2])
+p.loadURDF("tray/tray.urdf", [0, 0, 0])
+p.loadURDF("block.urdf", [0, 0, 2])
 
 log = readLogFile("data/block_grasp_log.bin")
 
@@ -58,26 +59,26 @@ print(recordNum)
 print('item num:'),
 print(itemNum)
 
+
 def Step(stepIndex):
-	for objectId in range(objectNum):
-		record = log[stepIndex*objectNum+objectId]
-		Id = record[2]
-		pos = [record[3],record[4],record[5]]
-		orn = [record[6],record[7],record[8],record[9]]
-		p.resetBasePositionAndOrientation(Id,pos,orn)
-		numJoints = p.getNumJoints(Id)
-		for i in range (numJoints):
-			jointInfo = p.getJointInfo(Id,i)
-			qIndex = jointInfo[3]
-			if qIndex > -1:
-				p.resetJointState(Id,i,record[qIndex-7+17])
-
-
-stepIndexId = p.addUserDebugParameter("stepIndex",0,recordNum/objectNum-1,0)
+  for objectId in range(objectNum):
+    record = log[stepIndex * objectNum + objectId]
+    Id = record[2]
+    pos = [record[3], record[4], record[5]]
+    orn = [record[6], record[7], record[8], record[9]]
+    p.resetBasePositionAndOrientation(Id, pos, orn)
+    numJoints = p.getNumJoints(Id)
+    for i in range(numJoints):
+      jointInfo = p.getJointInfo(Id, i)
+      qIndex = jointInfo[3]
+      if qIndex > -1:
+        p.resetJointState(Id, i, record[qIndex - 7 + 17])
 
-while True:
-	stepIndex = int(p.readUserDebugParameter(stepIndexId))
-	Step(stepIndex)
-	p.stepSimulation()
-	Step(stepIndex)
 
+stepIndexId = p.addUserDebugParameter("stepIndex", 0, recordNum / objectNum - 1, 0)
+
+while True:
+  stepIndex = int(p.readUserDebugParameter(stepIndexId))
+  Step(stepIndex)
+  p.stepSimulation()
+  Step(stepIndex)
diff --git a/examples/pybullet/examples/kuka_with_cube.py b/examples/pybullet/examples/kuka_with_cube.py
index 4f52978e3..49573ea83 100644
--- a/examples/pybullet/examples/kuka_with_cube.py
+++ b/examples/pybullet/examples/kuka_with_cube.py
@@ -5,36 +5,36 @@ from datetime import datetime
 
 #clid = p.connect(p.SHARED_MEMORY)
 p.connect(p.GUI)
-p.loadURDF("plane.urdf",[0,0,-0.3],useFixedBase=True)
-kukaId = p.loadURDF("kuka_iiwa/model.urdf",[0,0,0],useFixedBase=True)
-p.resetBasePositionAndOrientation(kukaId,[0,0,0],[0,0,0,1])
+p.loadURDF("plane.urdf", [0, 0, -0.3], useFixedBase=True)
+kukaId = p.loadURDF("kuka_iiwa/model.urdf", [0, 0, 0], useFixedBase=True)
+p.resetBasePositionAndOrientation(kukaId, [0, 0, 0], [0, 0, 0, 1])
 kukaEndEffectorIndex = 6
 numJoints = p.getNumJoints(kukaId)
-if (numJoints!=7):
-	exit()
+if (numJoints != 7):
+  exit()
 
-p.loadURDF("cube.urdf",[2,2,5])
-p.loadURDF("cube.urdf",[-2,-2,5])
-p.loadURDF("cube.urdf",[2,-2,5])
+p.loadURDF("cube.urdf", [2, 2, 5])
+p.loadURDF("cube.urdf", [-2, -2, 5])
+p.loadURDF("cube.urdf", [2, -2, 5])
 
 #lower limits for null space
-ll=[-.967,-2	,-2.96,0.19,-2.96,-2.09,-3.05]
+ll = [-.967, -2, -2.96, 0.19, -2.96, -2.09, -3.05]
 #upper limits for null space
-ul=[.967,2	,2.96,2.29,2.96,2.09,3.05]
+ul = [.967, 2, 2.96, 2.29, 2.96, 2.09, 3.05]
 #joint ranges for null space
-jr=[5.8,4,5.8,4,5.8,4,6]
+jr = [5.8, 4, 5.8, 4, 5.8, 4, 6]
 #restposes for null space
-rp=[0,0,0,0.5*math.pi,0,-math.pi*0.5*0.66,0]
+rp = [0, 0, 0, 0.5 * math.pi, 0, -math.pi * 0.5 * 0.66, 0]
 #joint damping coefficents
-jd=[0.1,0.1,0.1,0.1,0.1,0.1,0.1]
+jd = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
 
-for i in range (numJoints):
-	p.resetJointState(kukaId,i,rp[i])
+for i in range(numJoints):
+  p.resetJointState(kukaId, i, rp[i])
 
-p.setGravity(0,0,-10)
-t=0.
-prevPose=[0,0,0]
-prevPose1=[0,0,0]
+p.setGravity(0, 0, -10)
+t = 0.
+prevPose = [0, 0, 0]
+prevPose1 = [0, 0, 0]
 hasPrevPose = 0
 useNullSpace = 0
 
@@ -47,50 +47,68 @@ p.setRealTimeSimulation(useRealTimeSimulation)
 #use 0 for no-removal
 trailDuration = 15
 
-logId1 = p.startStateLogging(p.STATE_LOGGING_GENERIC_ROBOT,"LOG0001.txt",[0,1,2])
-logId2 = p.startStateLogging(p.STATE_LOGGING_CONTACT_POINTS,"LOG0002.txt",bodyUniqueIdA=2)
+logId1 = p.startStateLogging(p.STATE_LOGGING_GENERIC_ROBOT, "LOG0001.txt", [0, 1, 2])
+logId2 = p.startStateLogging(p.STATE_LOGGING_CONTACT_POINTS, "LOG0002.txt", bodyUniqueIdA=2)
 
 for i in range(5):
-    print ("Body %d's name is %s." % (i, p.getBodyInfo(i)[1]))
-	
+  print("Body %d's name is %s." % (i, p.getBodyInfo(i)[1]))
+
 while 1:
-	if (useRealTimeSimulation):
-		dt = datetime.now()
-		t = (dt.second/60.)*2.*math.pi
-	else:
-		t=t+0.1
-	
-	if (useSimulation and useRealTimeSimulation==0):
-		p.stepSimulation()
-	
-	for i in range (1):
-		pos = [-0.4,0.2*math.cos(t),0.+0.2*math.sin(t)]
-		#end effector points down, not up (in case useOrientation==1)
-		orn = p.getQuaternionFromEuler([0,-math.pi,0])
-		
-		if (useNullSpace==1):
-			if (useOrientation==1):
-				jointPoses = p.calculateInverseKinematics(kukaId,kukaEndEffectorIndex,pos,orn,ll,ul,jr,rp)
-			else:
-				jointPoses = p.calculateInverseKinematics(kukaId,kukaEndEffectorIndex,pos,lowerLimits=ll, upperLimits=ul, jointRanges=jr, restPoses=rp)
-		else:
-			if (useOrientation==1):
-				jointPoses = p.calculateInverseKinematics(kukaId,kukaEndEffectorIndex,pos,orn,jointDamping=jd)
-			else:
-				jointPoses = p.calculateInverseKinematics(kukaId,kukaEndEffectorIndex,pos)
-	
-		if (useSimulation):
-			for i in range (numJoints):
-				p.setJointMotorControl2(bodyIndex=kukaId,jointIndex=i,controlMode=p.POSITION_CONTROL,targetPosition=jointPoses[i],targetVelocity=0,force=500,positionGain=0.03,velocityGain=1)
-		else:
-			#reset the joint state (ignoring all dynamics, not recommended to use during simulation)
-			for i in range (numJoints):
-				p.resetJointState(kukaId,i,jointPoses[i])
+  if (useRealTimeSimulation):
+    dt = datetime.now()
+    t = (dt.second / 60.) * 2. * math.pi
+  else:
+    t = t + 0.1
+
+  if (useSimulation and useRealTimeSimulation == 0):
+    p.stepSimulation()
+
+  for i in range(1):
+    pos = [-0.4, 0.2 * math.cos(t), 0. + 0.2 * math.sin(t)]
+    #end effector points down, not up (in case useOrientation==1)
+    orn = p.getQuaternionFromEuler([0, -math.pi, 0])
+
+    if (useNullSpace == 1):
+      if (useOrientation == 1):
+        jointPoses = p.calculateInverseKinematics(kukaId, kukaEndEffectorIndex, pos, orn, ll, ul,
+                                                  jr, rp)
+      else:
+        jointPoses = p.calculateInverseKinematics(kukaId,
+                                                  kukaEndEffectorIndex,
+                                                  pos,
+                                                  lowerLimits=ll,
+                                                  upperLimits=ul,
+                                                  jointRanges=jr,
+                                                  restPoses=rp)
+    else:
+      if (useOrientation == 1):
+        jointPoses = p.calculateInverseKinematics(kukaId,
+                                                  kukaEndEffectorIndex,
+                                                  pos,
+                                                  orn,
+                                                  jointDamping=jd)
+      else:
+        jointPoses = p.calculateInverseKinematics(kukaId, kukaEndEffectorIndex, pos)
+
+    if (useSimulation):
+      for i in range(numJoints):
+        p.setJointMotorControl2(bodyIndex=kukaId,
+                                jointIndex=i,
+                                controlMode=p.POSITION_CONTROL,
+                                targetPosition=jointPoses[i],
+                                targetVelocity=0,
+                                force=500,
+                                positionGain=0.03,
+                                velocityGain=1)
+    else:
+      #reset the joint state (ignoring all dynamics, not recommended to use during simulation)
+      for i in range(numJoints):
+        p.resetJointState(kukaId, i, jointPoses[i])
 
-	ls = p.getLinkState(kukaId,kukaEndEffectorIndex)	
-	if (hasPrevPose):
-		p.addUserDebugLine(prevPose,pos,[0,0,0.3],1,trailDuration)
-		p.addUserDebugLine(prevPose1,ls[4],[1,0,0],1,trailDuration)
-	prevPose=pos
-	prevPose1=ls[4]
-	hasPrevPose = 1
+  ls = p.getLinkState(kukaId, kukaEndEffectorIndex)
+  if (hasPrevPose):
+    p.addUserDebugLine(prevPose, pos, [0, 0, 0.3], 1, trailDuration)
+    p.addUserDebugLine(prevPose1, ls[4], [1, 0, 0], 1, trailDuration)
+  prevPose = pos
+  prevPose1 = ls[4]
+  hasPrevPose = 1
diff --git a/examples/pybullet/examples/kuka_with_cube_playback.py b/examples/pybullet/examples/kuka_with_cube_playback.py
index 5b02f1b72..1a79b42d1 100644
--- a/examples/pybullet/examples/kuka_with_cube_playback.py
+++ b/examples/pybullet/examples/kuka_with_cube_playback.py
@@ -10,15 +10,16 @@ import os, fnmatch
 import argparse
 from time import sleep
 
-def readLogFile(filename, verbose = True):
+
+def readLogFile(filename, verbose=True):
   f = open(filename, 'rb')
-  
+
   print('Opened'),
   print(filename)
 
   keys = f.readline().decode('utf8').rstrip('\n').split(',')
   fmt = f.readline().decode('utf8').rstrip('\n')
-  
+
   # The byte number of one record
   sz = struct.calcsize(fmt)
   # The type number of one record
@@ -49,13 +50,14 @@ def readLogFile(filename, verbose = True):
 
   return log
 
+
 #clid = p.connect(p.SHARED_MEMORY)
 p.connect(p.GUI)
-p.loadURDF("plane.urdf",[0,0,-0.3])
-p.loadURDF("kuka_iiwa/model.urdf",[0,0,1])
-p.loadURDF("cube.urdf",[2,2,5])
-p.loadURDF("cube.urdf",[-2,-2,5])
-p.loadURDF("cube.urdf",[2,-2,5])
+p.loadURDF("plane.urdf", [0, 0, -0.3])
+p.loadURDF("kuka_iiwa/model.urdf", [0, 0, 1])
+p.loadURDF("cube.urdf", [2, 2, 5])
+p.loadURDF("cube.urdf", [-2, -2, 5])
+p.loadURDF("cube.urdf", [2, -2, 5])
 
 log = readLogFile("LOG0001.txt")
 
@@ -67,14 +69,14 @@ print('item num:'),
 print(itemNum)
 
 for record in log:
-    Id = record[2]
-    pos = [record[3],record[4],record[5]]
-    orn = [record[6],record[7],record[8],record[9]]
-    p.resetBasePositionAndOrientation(Id,pos,orn)
-    numJoints = p.getNumJoints(Id)
-    for i in range (numJoints):
-        jointInfo = p.getJointInfo(Id,i)
-        qIndex = jointInfo[3]
-        if qIndex > -1:
-            p.resetJointState(Id,i,record[qIndex-7+17])
-    sleep(0.0005)
+  Id = record[2]
+  pos = [record[3], record[4], record[5]]
+  orn = [record[6], record[7], record[8], record[9]]
+  p.resetBasePositionAndOrientation(Id, pos, orn)
+  numJoints = p.getNumJoints(Id)
+  for i in range(numJoints):
+    jointInfo = p.getJointInfo(Id, i)
+    qIndex = jointInfo[3]
+    if qIndex > -1:
+      p.resetJointState(Id, i, record[qIndex - 7 + 17])
+  sleep(0.0005)
diff --git a/examples/pybullet/examples/load_soft_body.py b/examples/pybullet/examples/load_soft_body.py
index 7855d9e16..27eaa52b5 100644
--- a/examples/pybullet/examples/load_soft_body.py
+++ b/examples/pybullet/examples/load_soft_body.py
@@ -3,20 +3,20 @@ from time import sleep
 
 physicsClient = p.connect(p.GUI)
 
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 planeId = p.loadURDF("plane.urdf")
 bunnyId = p.loadSoftBody("bunny.obj")
-p.loadURDF("cube_small.urdf",[1,0,1])
+p.loadURDF("cube_small.urdf", [1, 0, 1])
 useRealTimeSimulation = 1
 
 if (useRealTimeSimulation):
-	p.setRealTimeSimulation(1)
+  p.setRealTimeSimulation(1)
 
 while p.isConnected():
-	p.setGravity(0,0,-10)
-	if (useRealTimeSimulation):
+  p.setGravity(0, 0, -10)
+  if (useRealTimeSimulation):
 
-		sleep(0.01) # Time in seconds.
-		#p.getCameraImage(320,200,renderer=p.ER_BULLET_HARDWARE_OPENGL )
-	else:
-		p.stepSimulation()
+    sleep(0.01)  # Time in seconds.
+    #p.getCameraImage(320,200,renderer=p.ER_BULLET_HARDWARE_OPENGL )
+  else:
+    p.stepSimulation()
diff --git a/examples/pybullet/examples/loadingBench.py b/examples/pybullet/examples/loadingBench.py
index e576e2afe..38a12e12d 100644
--- a/examples/pybullet/examples/loadingBench.py
+++ b/examples/pybullet/examples/loadingBench.py
@@ -4,25 +4,23 @@ p.connect(p.GUI)
 

 p.resetSimulation()

 timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "loadingBenchVR.json")

-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)

+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)

 print("load plane.urdf")

 p.loadURDF("plane.urdf")

 print("load r2d2.urdf")

 

-p.loadURDF("r2d2.urdf",0,0,1)

+p.loadURDF("r2d2.urdf", 0, 0, 1)

 print("load kitchen/1.sdf")

 p.loadSDF("kitchens/1.sdf")

 print("load 100 times plate.urdf")

-for i in range (100):

-	p.loadURDF("dinnerware/plate.urdf",0,i,1)

+for i in range(100):

+  p.loadURDF("dinnerware/plate.urdf", 0, i, 1)

 

-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)

+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)

 

 p.stopStateLogging(timinglog)

 print("stopped state logging")

-p.getCameraImage(320,200)

+p.getCameraImage(320, 200)

 

 while (1):

-	p.stepSimulation()

-

-

+  p.stepSimulation()

diff --git a/examples/pybullet/examples/logMinitaur.py b/examples/pybullet/examples/logMinitaur.py
index b995ee8f7..8338862f7 100644
--- a/examples/pybullet/examples/logMinitaur.py
+++ b/examples/pybullet/examples/logMinitaur.py
@@ -1,11 +1,11 @@
 import pybullet as p
 cid = p.connect(p.SHARED_MEMORY)
-if (cid < 0) :
-	p.connect(p.GUI)
+if (cid < 0):
+  p.connect(p.GUI)
 p.loadURDF("plane.urdf")
 
 quadruped = p.loadURDF("quadruped/quadruped.urdf")
-logId = p.startStateLogging(p.STATE_LOGGING_MINITAUR,"LOG00048.TXT",[quadruped])
+logId = p.startStateLogging(p.STATE_LOGGING_MINITAUR, "LOG00048.TXT", [quadruped])
 p.stepSimulation()
 p.stepSimulation()
 p.stepSimulation()
diff --git a/examples/pybullet/examples/manyspheres.py b/examples/pybullet/examples/manyspheres.py
index f6f435f9a..55af537a8 100644
--- a/examples/pybullet/examples/manyspheres.py
+++ b/examples/pybullet/examples/manyspheres.py
@@ -2,34 +2,34 @@ import pybullet as p
 import time
 
 conid = p.connect(p.SHARED_MEMORY)
-if (conid<0):
-	p.connect(p.GUI)
-	
+if (conid < 0):
+  p.connect(p.GUI)
+
 p.setInternalSimFlags(0)
 p.resetSimulation()
-	
-p.loadURDF("plane.urdf",useMaximalCoordinates=True)
-p.loadURDF("tray/traybox.urdf",useMaximalCoordinates=True)
 
-gravXid = p.addUserDebugParameter("gravityX",-10,10,0)
-gravYid = p.addUserDebugParameter("gravityY",-10,10,0)
-gravZid = p.addUserDebugParameter("gravityZ",-10,10,-10)
+p.loadURDF("plane.urdf", useMaximalCoordinates=True)
+p.loadURDF("tray/traybox.urdf", useMaximalCoordinates=True)
+
+gravXid = p.addUserDebugParameter("gravityX", -10, 10, 0)
+gravYid = p.addUserDebugParameter("gravityY", -10, 10, 0)
+gravZid = p.addUserDebugParameter("gravityZ", -10, 10, -10)
 p.setPhysicsEngineParameter(numSolverIterations=10)
 p.setPhysicsEngineParameter(contactBreakingThreshold=0.001)
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)
-for i in range (10):
-    for j in range (10):
-        for k in range (10):
-            ob = p.loadURDF("sphere_1cm.urdf",[0.02*i,0.02*j,0.2+0.02*k],useMaximalCoordinates=True)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
+for i in range(10):
+  for j in range(10):
+    for k in range(10):
+      ob = p.loadURDF("sphere_1cm.urdf", [0.02 * i, 0.02 * j, 0.2 + 0.02 * k],
+                      useMaximalCoordinates=True)
 
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
-p.setGravity(0,0,-10)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
+p.setGravity(0, 0, -10)
 
 p.setRealTimeSimulation(1)
 while True:
-    gravX = p.readUserDebugParameter(gravXid)
-    gravY = p.readUserDebugParameter(gravYid)
-    gravZ = p.readUserDebugParameter(gravZid)
-    p.setGravity(gravX,gravY,gravZ)
-    time.sleep(0.01)
-
+  gravX = p.readUserDebugParameter(gravXid)
+  gravY = p.readUserDebugParameter(gravYid)
+  gravZ = p.readUserDebugParameter(gravZid)
+  p.setGravity(gravX, gravY, gravZ)
+  time.sleep(0.01)
diff --git a/examples/pybullet/examples/mimicJointConstraint.py b/examples/pybullet/examples/mimicJointConstraint.py
index 968a24354..e6c42d6a2 100644
--- a/examples/pybullet/examples/mimicJointConstraint.py
+++ b/examples/pybullet/examples/mimicJointConstraint.py
@@ -4,26 +4,44 @@
 import pybullet as p
 import time
 p.connect(p.GUI)
-p.loadURDF("plane.urdf",0,0,-2)
-wheelA = p.loadURDF("differential/diff_ring.urdf",[0,0,0])
+p.loadURDF("plane.urdf", 0, 0, -2)
+wheelA = p.loadURDF("differential/diff_ring.urdf", [0, 0, 0])
 for i in range(p.getNumJoints(wheelA)):
-	print(p.getJointInfo(wheelA,i))
-	p.setJointMotorControl2(wheelA,i,p.VELOCITY_CONTROL,targetVelocity=0,force=0)
+  print(p.getJointInfo(wheelA, i))
+  p.setJointMotorControl2(wheelA, i, p.VELOCITY_CONTROL, targetVelocity=0, force=0)
 
+c = p.createConstraint(wheelA,
+                       1,
+                       wheelA,
+                       3,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=1, maxForce=10000)
 
-c = p.createConstraint(wheelA,1,wheelA,3,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=1, maxForce=10000)
-
-c = p.createConstraint(wheelA,2,wheelA,4,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=-1, maxForce=10000)
-
-c = p.createConstraint(wheelA,1,wheelA,4,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=-1, maxForce=10000)
+c = p.createConstraint(wheelA,
+                       2,
+                       wheelA,
+                       4,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=-1, maxForce=10000)
 
+c = p.createConstraint(wheelA,
+                       1,
+                       wheelA,
+                       4,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=-1, maxForce=10000)
 
 p.setRealTimeSimulation(1)
-while(1):
-	p.setGravity(0,0,-10)
-	time.sleep(0.01)
+while (1):
+  p.setGravity(0, 0, -10)
+  time.sleep(0.01)
 #p.removeConstraint(c)
-	
diff --git a/examples/pybullet/examples/minitaur.py b/examples/pybullet/examples/minitaur.py
index 59fab3bf1..664c8ea6b 100644
--- a/examples/pybullet/examples/minitaur.py
+++ b/examples/pybullet/examples/minitaur.py
@@ -1,7 +1,9 @@
 import pybullet as p
 import numpy as np
 
+
 class Minitaur:
+
   def __init__(self, urdfRootPath=''):
     self.urdfRootPath = urdfRootPath
     self.reset()
@@ -26,9 +28,8 @@ class Minitaur:
     self.motorIdList.append(self.jointNameToId['motor_back_rightL_joint'])
     self.motorIdList.append(self.jointNameToId['motor_back_rightR_joint'])
 
-
   def reset(self):
-    self.quadruped = p.loadURDF("%s/quadruped/minitaur.urdf" % self.urdfRootPath,0,0,.2)
+    self.quadruped = p.loadURDF("%s/quadruped/minitaur.urdf" % self.urdfRootPath, 0, 0, .2)
     self.kp = 1
     self.kd = 0.1
     self.maxForce = 3.5
@@ -38,10 +39,14 @@ class Minitaur:
     self.buildJointNameToIdDict()
     self.buildMotorIdList()
 
-
-
   def setMotorAngleById(self, motorId, desiredAngle):
-    p.setJointMotorControl2(bodyIndex=self.quadruped, jointIndex=motorId, controlMode=p.POSITION_CONTROL, targetPosition=desiredAngle, positionGain=self.kp, velocityGain=self.kd, force=self.maxForce)
+    p.setJointMotorControl2(bodyIndex=self.quadruped,
+                            jointIndex=motorId,
+                            controlMode=p.POSITION_CONTROL,
+                            targetPosition=desiredAngle,
+                            positionGain=self.kp,
+                            velocityGain=self.kd,
+                            force=self.maxForce)
 
   def setMotorAngleByName(self, motorName, desiredAngle):
     self.setMotorAngleById(self.jointNameToId[motorName], desiredAngle)
@@ -49,53 +54,107 @@ class Minitaur:
   def resetPose(self):
     kneeFrictionForce = 0
     halfpi = 1.57079632679
-    kneeangle = -2.1834 #halfpi - acos(upper_leg_length / lower_leg_length)
+    kneeangle = -2.1834  #halfpi - acos(upper_leg_length / lower_leg_length)
 
     #left front leg
-    p.resetJointState(self.quadruped,self.jointNameToId['motor_front_leftL_joint'],self.motorDir[0]*halfpi)
-    p.resetJointState(self.quadruped,self.jointNameToId['knee_front_leftL_link'],self.motorDir[0]*kneeangle)
-    p.resetJointState(self.quadruped,self.jointNameToId['motor_front_leftR_joint'],self.motorDir[1]*halfpi)
-    p.resetJointState(self.quadruped,self.jointNameToId['knee_front_leftR_link'],self.motorDir[1]*kneeangle)
-    p.createConstraint(self.quadruped,self.jointNameToId['knee_front_leftR_link'],self.quadruped,self.jointNameToId['knee_front_leftL_link'],p.JOINT_POINT2POINT,[0,0,0],[0,0.005,0.2],[0,0.01,0.2])
-    self.setMotorAngleByName('motor_front_leftL_joint', self.motorDir[0]*halfpi)
-    self.setMotorAngleByName('motor_front_leftR_joint', self.motorDir[1]*halfpi)
-    p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_front_leftL_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
-    p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_front_leftR_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
+    p.resetJointState(self.quadruped, self.jointNameToId['motor_front_leftL_joint'],
+                      self.motorDir[0] * halfpi)
+    p.resetJointState(self.quadruped, self.jointNameToId['knee_front_leftL_link'],
+                      self.motorDir[0] * kneeangle)
+    p.resetJointState(self.quadruped, self.jointNameToId['motor_front_leftR_joint'],
+                      self.motorDir[1] * halfpi)
+    p.resetJointState(self.quadruped, self.jointNameToId['knee_front_leftR_link'],
+                      self.motorDir[1] * kneeangle)
+    p.createConstraint(self.quadruped, self.jointNameToId['knee_front_leftR_link'], self.quadruped,
+                       self.jointNameToId['knee_front_leftL_link'], p.JOINT_POINT2POINT, [0, 0, 0],
+                       [0, 0.005, 0.2], [0, 0.01, 0.2])
+    self.setMotorAngleByName('motor_front_leftL_joint', self.motorDir[0] * halfpi)
+    self.setMotorAngleByName('motor_front_leftR_joint', self.motorDir[1] * halfpi)
+    p.setJointMotorControl2(bodyIndex=self.quadruped,
+                            jointIndex=self.jointNameToId['knee_front_leftL_link'],
+                            controlMode=p.VELOCITY_CONTROL,
+                            targetVelocity=0,
+                            force=kneeFrictionForce)
+    p.setJointMotorControl2(bodyIndex=self.quadruped,
+                            jointIndex=self.jointNameToId['knee_front_leftR_link'],
+                            controlMode=p.VELOCITY_CONTROL,
+                            targetVelocity=0,
+                            force=kneeFrictionForce)
 
     #left back leg
-    p.resetJointState(self.quadruped,self.jointNameToId['motor_back_leftL_joint'],self.motorDir[2]*halfpi)
-    p.resetJointState(self.quadruped,self.jointNameToId['knee_back_leftL_link'],self.motorDir[2]*kneeangle)
-    p.resetJointState(self.quadruped,self.jointNameToId['motor_back_leftR_joint'],self.motorDir[3]*halfpi)
-    p.resetJointState(self.quadruped,self.jointNameToId['knee_back_leftR_link'],self.motorDir[3]*kneeangle)
-    p.createConstraint(self.quadruped,self.jointNameToId['knee_back_leftR_link'],self.quadruped,self.jointNameToId['knee_back_leftL_link'],p.JOINT_POINT2POINT,[0,0,0],[0,0.005,0.2],[0,0.01,0.2])
-    self.setMotorAngleByName('motor_back_leftL_joint',self.motorDir[2]*halfpi)
-    self.setMotorAngleByName('motor_back_leftR_joint',self.motorDir[3]*halfpi)
-    p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_back_leftL_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
-    p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_back_leftR_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
- 
+    p.resetJointState(self.quadruped, self.jointNameToId['motor_back_leftL_joint'],
+                      self.motorDir[2] * halfpi)
+    p.resetJointState(self.quadruped, self.jointNameToId['knee_back_leftL_link'],
+                      self.motorDir[2] * kneeangle)
+    p.resetJointState(self.quadruped, self.jointNameToId['motor_back_leftR_joint'],
+                      self.motorDir[3] * halfpi)
+    p.resetJointState(self.quadruped, self.jointNameToId['knee_back_leftR_link'],
+                      self.motorDir[3] * kneeangle)
+    p.createConstraint(self.quadruped, self.jointNameToId['knee_back_leftR_link'], self.quadruped,
+                       self.jointNameToId['knee_back_leftL_link'], p.JOINT_POINT2POINT, [0, 0, 0],
+                       [0, 0.005, 0.2], [0, 0.01, 0.2])
+    self.setMotorAngleByName('motor_back_leftL_joint', self.motorDir[2] * halfpi)
+    self.setMotorAngleByName('motor_back_leftR_joint', self.motorDir[3] * halfpi)
+    p.setJointMotorControl2(bodyIndex=self.quadruped,
+                            jointIndex=self.jointNameToId['knee_back_leftL_link'],
+                            controlMode=p.VELOCITY_CONTROL,
+                            targetVelocity=0,
+                            force=kneeFrictionForce)
+    p.setJointMotorControl2(bodyIndex=self.quadruped,
+                            jointIndex=self.jointNameToId['knee_back_leftR_link'],
+                            controlMode=p.VELOCITY_CONTROL,
+                            targetVelocity=0,
+                            force=kneeFrictionForce)
+
     #right front leg
-    p.resetJointState(self.quadruped,self.jointNameToId['motor_front_rightL_joint'],self.motorDir[4]*halfpi)
-    p.resetJointState(self.quadruped,self.jointNameToId['knee_front_rightL_link'],self.motorDir[4]*kneeangle)
-    p.resetJointState(self.quadruped,self.jointNameToId['motor_front_rightR_joint'],self.motorDir[5]*halfpi)
-    p.resetJointState(self.quadruped,self.jointNameToId['knee_front_rightR_link'],self.motorDir[5]*kneeangle)
-    p.createConstraint(self.quadruped,self.jointNameToId['knee_front_rightR_link'],self.quadruped,self.jointNameToId['knee_front_rightL_link'],p.JOINT_POINT2POINT,[0,0,0],[0,0.005,0.2],[0,0.01,0.2])
-    self.setMotorAngleByName('motor_front_rightL_joint',self.motorDir[4]*halfpi)
-    self.setMotorAngleByName('motor_front_rightR_joint',self.motorDir[5]*halfpi)
-    p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_front_rightL_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
-    p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_front_rightR_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
- 
+    p.resetJointState(self.quadruped, self.jointNameToId['motor_front_rightL_joint'],
+                      self.motorDir[4] * halfpi)
+    p.resetJointState(self.quadruped, self.jointNameToId['knee_front_rightL_link'],
+                      self.motorDir[4] * kneeangle)
+    p.resetJointState(self.quadruped, self.jointNameToId['motor_front_rightR_joint'],
+                      self.motorDir[5] * halfpi)
+    p.resetJointState(self.quadruped, self.jointNameToId['knee_front_rightR_link'],
+                      self.motorDir[5] * kneeangle)
+    p.createConstraint(self.quadruped, self.jointNameToId['knee_front_rightR_link'],
+                       self.quadruped, self.jointNameToId['knee_front_rightL_link'],
+                       p.JOINT_POINT2POINT, [0, 0, 0], [0, 0.005, 0.2], [0, 0.01, 0.2])
+    self.setMotorAngleByName('motor_front_rightL_joint', self.motorDir[4] * halfpi)
+    self.setMotorAngleByName('motor_front_rightR_joint', self.motorDir[5] * halfpi)
+    p.setJointMotorControl2(bodyIndex=self.quadruped,
+                            jointIndex=self.jointNameToId['knee_front_rightL_link'],
+                            controlMode=p.VELOCITY_CONTROL,
+                            targetVelocity=0,
+                            force=kneeFrictionForce)
+    p.setJointMotorControl2(bodyIndex=self.quadruped,
+                            jointIndex=self.jointNameToId['knee_front_rightR_link'],
+                            controlMode=p.VELOCITY_CONTROL,
+                            targetVelocity=0,
+                            force=kneeFrictionForce)
 
     #right back leg
-    p.resetJointState(self.quadruped,self.jointNameToId['motor_back_rightL_joint'],self.motorDir[6]*halfpi)
-    p.resetJointState(self.quadruped,self.jointNameToId['knee_back_rightL_link'],self.motorDir[6]*kneeangle)
-    p.resetJointState(self.quadruped,self.jointNameToId['motor_back_rightR_joint'],self.motorDir[7]*halfpi)
-    p.resetJointState(self.quadruped,self.jointNameToId['knee_back_rightR_link'],self.motorDir[7]*kneeangle)
-    p.createConstraint(self.quadruped,self.jointNameToId['knee_back_rightR_link'],self.quadruped,self.jointNameToId['knee_back_rightL_link'],p.JOINT_POINT2POINT,[0,0,0],[0,0.005,0.2],[0,0.01,0.2])
-    self.setMotorAngleByName('motor_back_rightL_joint',self.motorDir[6]*halfpi)
-    self.setMotorAngleByName('motor_back_rightR_joint',self.motorDir[7]*halfpi)
-    p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_back_rightL_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
-    p.setJointMotorControl2(bodyIndex=self.quadruped,jointIndex=self.jointNameToId['knee_back_rightR_link'],controlMode=p.VELOCITY_CONTROL,targetVelocity=0,force=kneeFrictionForce)
-
+    p.resetJointState(self.quadruped, self.jointNameToId['motor_back_rightL_joint'],
+                      self.motorDir[6] * halfpi)
+    p.resetJointState(self.quadruped, self.jointNameToId['knee_back_rightL_link'],
+                      self.motorDir[6] * kneeangle)
+    p.resetJointState(self.quadruped, self.jointNameToId['motor_back_rightR_joint'],
+                      self.motorDir[7] * halfpi)
+    p.resetJointState(self.quadruped, self.jointNameToId['knee_back_rightR_link'],
+                      self.motorDir[7] * kneeangle)
+    p.createConstraint(self.quadruped, self.jointNameToId['knee_back_rightR_link'], self.quadruped,
+                       self.jointNameToId['knee_back_rightL_link'], p.JOINT_POINT2POINT, [0, 0, 0],
+                       [0, 0.005, 0.2], [0, 0.01, 0.2])
+    self.setMotorAngleByName('motor_back_rightL_joint', self.motorDir[6] * halfpi)
+    self.setMotorAngleByName('motor_back_rightR_joint', self.motorDir[7] * halfpi)
+    p.setJointMotorControl2(bodyIndex=self.quadruped,
+                            jointIndex=self.jointNameToId['knee_back_rightL_link'],
+                            controlMode=p.VELOCITY_CONTROL,
+                            targetVelocity=0,
+                            force=kneeFrictionForce)
+    p.setJointMotorControl2(bodyIndex=self.quadruped,
+                            jointIndex=self.jointNameToId['knee_back_rightR_link'],
+                            controlMode=p.VELOCITY_CONTROL,
+                            targetVelocity=0,
+                            force=kneeFrictionForce)
 
   def getBasePosition(self):
     position, orientation = p.getBasePositionAndOrientation(self.quadruped)
diff --git a/examples/pybullet/examples/minitaur_evaluate.py b/examples/pybullet/examples/minitaur_evaluate.py
index 0975bef0c..941320e87 100644
--- a/examples/pybullet/examples/minitaur_evaluate.py
+++ b/examples/pybullet/examples/minitaur_evaluate.py
@@ -15,6 +15,7 @@ def current_position():
   position = minitaur.getBasePosition()
   return np.asarray(position)
 
+
 def is_fallen():
   global minitaur
   orientation = minitaur.getBaseOrientation()
@@ -22,13 +23,16 @@ def is_fallen():
   localUp = rotMat[6:]
   return np.dot(np.asarray([0, 0, 1]), np.asarray(localUp)) < 0
 
+
 def evaluate_desired_motorAngle_8Amplitude8Phase(i, params):
   nMotors = 8
   speed = 0.35
   for jthMotor in range(nMotors):
-    joint_values[jthMotor] = math.sin(i*speed + params[nMotors + jthMotor])*params[jthMotor]*+1.57
+    joint_values[jthMotor] = math.sin(i * speed +
+                                      params[nMotors + jthMotor]) * params[jthMotor] * +1.57
   return joint_values
 
+
 def evaluate_desired_motorAngle_2Amplitude4Phase(i, params):
   speed = 0.35
   phaseDiff = params[2]
@@ -43,29 +47,37 @@ def evaluate_desired_motorAngle_2Amplitude4Phase(i, params):
   joint_values = [a0, a1, a2, a3, a4, a5, a6, a7]
   return joint_values
 
+
 def evaluate_desired_motorAngle_hop(i, params):
   amplitude = params[0]
   speed = params[1]
-  a1 = math.sin(i*speed)*amplitude+1.57
-  a2 = math.sin(i*speed+3.14)*amplitude+1.57
+  a1 = math.sin(i * speed) * amplitude + 1.57
+  a2 = math.sin(i * speed + 3.14) * amplitude + 1.57
   joint_values = [a1, 1.57, a2, 1.57, 1.57, a1, 1.57, a2]
   return joint_values
 
 
-evaluate_func_map['evaluate_desired_motorAngle_8Amplitude8Phase'] = evaluate_desired_motorAngle_8Amplitude8Phase
-evaluate_func_map['evaluate_desired_motorAngle_2Amplitude4Phase'] = evaluate_desired_motorAngle_2Amplitude4Phase
+evaluate_func_map[
+    'evaluate_desired_motorAngle_8Amplitude8Phase'] = evaluate_desired_motorAngle_8Amplitude8Phase
+evaluate_func_map[
+    'evaluate_desired_motorAngle_2Amplitude4Phase'] = evaluate_desired_motorAngle_2Amplitude4Phase
 evaluate_func_map['evaluate_desired_motorAngle_hop'] = evaluate_desired_motorAngle_hop
 
 
-
-def evaluate_params(evaluateFunc, params, objectiveParams, urdfRoot='', timeStep=0.01, maxNumSteps=10000, sleepTime=0):
+def evaluate_params(evaluateFunc,
+                    params,
+                    objectiveParams,
+                    urdfRoot='',
+                    timeStep=0.01,
+                    maxNumSteps=10000,
+                    sleepTime=0):
   print('start evaluation')
   beforeTime = time.time()
   p.resetSimulation()
 
   p.setTimeStep(timeStep)
   p.loadURDF("%s/plane.urdf" % urdfRoot)
-  p.setGravity(0,0,-10)
+  p.setGravity(0, 0, -10)
 
   global minitaur
   minitaur = Minitaur(urdfRoot)
@@ -95,5 +107,6 @@ def evaluate_params(evaluateFunc, params, objectiveParams, urdfRoot='', timeStep
   final_distance = np.linalg.norm(start_position - current_position())
   finalReturn = final_distance - alpha * total_energy
   elapsedTime = time.time() - beforeTime
-  print ("trial for ", params, " final_distance", final_distance, "total_energy", total_energy, "finalReturn", finalReturn, "elapsed_time", elapsedTime)
+  print("trial for ", params, " final_distance", final_distance, "total_energy", total_energy,
+        "finalReturn", finalReturn, "elapsed_time", elapsedTime)
   return finalReturn
diff --git a/examples/pybullet/examples/minitaur_test.py b/examples/pybullet/examples/minitaur_test.py
index 1d156e5a3..37c2ff37b 100644
--- a/examples/pybullet/examples/minitaur_test.py
+++ b/examples/pybullet/examples/minitaur_test.py
@@ -10,18 +10,27 @@ import time
 import math
 import numpy as np
 
+
 def main(unused_args):
   timeStep = 0.01
   c = p.connect(p.SHARED_MEMORY)
-  if (c<0):
-      c = p.connect(p.GUI)
+  if (c < 0):
+    c = p.connect(p.GUI)
 
-  params = [0.1903581461951056, 0.0006732219568880068, 0.05018085615283363, 3.219916795483583, 6.2406418167980595, 4.189869754607539]
+  params = [
+      0.1903581461951056, 0.0006732219568880068, 0.05018085615283363, 3.219916795483583,
+      6.2406418167980595, 4.189869754607539
+  ]
   evaluate_func = 'evaluate_desired_motorAngle_2Amplitude4Phase'
   energy_weight = 0.01
 
-  finalReturn = evaluate_params(evaluateFunc = evaluate_func, params=params, objectiveParams=[energy_weight], timeStep=timeStep, sleepTime=timeStep)
+  finalReturn = evaluate_params(evaluateFunc=evaluate_func,
+                                params=params,
+                                objectiveParams=[energy_weight],
+                                timeStep=timeStep,
+                                sleepTime=timeStep)
 
   print(finalReturn)
 
+
 main(0)
diff --git a/examples/pybullet/examples/motorMaxVelocity.py b/examples/pybullet/examples/motorMaxVelocity.py
index fad9e2fd4..315e30d90 100644
--- a/examples/pybullet/examples/motorMaxVelocity.py
+++ b/examples/pybullet/examples/motorMaxVelocity.py
@@ -2,11 +2,19 @@ import pybullet as p
 import time
 
 p.connect(p.GUI)
-cartpole=p.loadURDF("cartpole.urdf")
+cartpole = p.loadURDF("cartpole.urdf")
 p.setRealTimeSimulation(1)
-p.setJointMotorControl2(cartpole,1,p.POSITION_CONTROL,targetPosition=1000,targetVelocity=0,force=1000, positionGain=1, velocityGain=0, maxVelocity=0.5)
+p.setJointMotorControl2(cartpole,
+                        1,
+                        p.POSITION_CONTROL,
+                        targetPosition=1000,
+                        targetVelocity=0,
+                        force=1000,
+                        positionGain=1,
+                        velocityGain=0,
+                        maxVelocity=0.5)
 while (1):
-	p.setGravity(0,0,-10)
-	js = p.getJointState(cartpole,1)
-	print("position=",js[0],"velocity=",js[1])
-	time.sleep(0.01)
\ No newline at end of file
+  p.setGravity(0, 0, -10)
+  js = p.getJointState(cartpole, 1)
+  print("position=", js[0], "velocity=", js[1])
+  time.sleep(0.01)
diff --git a/examples/pybullet/examples/otherPhysicsEngine.py b/examples/pybullet/examples/otherPhysicsEngine.py
index 93f9fbe44..e12bb45d4 100644
--- a/examples/pybullet/examples/otherPhysicsEngine.py
+++ b/examples/pybullet/examples/otherPhysicsEngine.py
@@ -6,43 +6,47 @@ import math
 usePhysX = True
 useMaximalCoordinates = True
 if usePhysX:
-  p.connect(p.PhysX,options="--numCores=8 --solver=pgs")
+  p.connect(p.PhysX, options="--numCores=8 --solver=pgs")
   p.loadPlugin("eglRendererPlugin")
 else:
   p.connect(p.GUI)
 
-p.setPhysicsEngineParameter(fixedTimeStep=1./240.,numSolverIterations=4, minimumSolverIslandSize=1024)
+p.setPhysicsEngineParameter(fixedTimeStep=1. / 240.,
+                            numSolverIterations=4,
+                            minimumSolverIslandSize=1024)
 p.setPhysicsEngineParameter(contactBreakingThreshold=0.01)
 
 p.setAdditionalSearchPath(pd.getDataPath())
 #Always make ground plane maximal coordinates, to avoid performance drop in PhysX
 #See https://github.com/NVIDIAGameWorks/PhysX/issues/71
 
-p.loadURDF("plane.urdf", useMaximalCoordinates=True)#useMaximalCoordinates)
-p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER,0)
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)
-logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS,"physx_create_dominoes.json")
+p.loadURDF("plane.urdf", useMaximalCoordinates=True)  #useMaximalCoordinates)
+p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER, 0)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
+logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "physx_create_dominoes.json")
 jran = 50
 iran = 100
 
-num=64
-radius=0.1
-numDominoes=0
+num = 64
+radius = 0.1
+numDominoes = 0
 
-for i in range (int(num*50)):
-  num=(radius*2*math.pi)/0.08
-  radius += 0.05/float(num)
-  orn = p.getQuaternionFromEuler([0,0,0.5*math.pi+math.pi*2*i/float(num)])
-  pos = [radius*math.cos(2*math.pi*(i/float(num))),radius*math.sin(2*math.pi*(i/float(num))), 0.03]
-  sphere = p.loadURDF("domino/domino.urdf",pos, orn, useMaximalCoordinates=useMaximalCoordinates)
-  numDominoes+=1
-  
-pos=[pos[0],pos[1],pos[2]+0.3]
-orn = p.getQuaternionFromEuler([0,0,-math.pi/4.])
-sphere = p.loadURDF("domino/domino.urdf",pos, orn, useMaximalCoordinates=useMaximalCoordinates)
+for i in range(int(num * 50)):
+  num = (radius * 2 * math.pi) / 0.08
+  radius += 0.05 / float(num)
+  orn = p.getQuaternionFromEuler([0, 0, 0.5 * math.pi + math.pi * 2 * i / float(num)])
+  pos = [
+      radius * math.cos(2 * math.pi * (i / float(num))),
+      radius * math.sin(2 * math.pi * (i / float(num))), 0.03
+  ]
+  sphere = p.loadURDF("domino/domino.urdf", pos, orn, useMaximalCoordinates=useMaximalCoordinates)
+  numDominoes += 1
 
-print("numDominoes=",numDominoes)
+pos = [pos[0], pos[1], pos[2] + 0.3]
+orn = p.getQuaternionFromEuler([0, 0, -math.pi / 4.])
+sphere = p.loadURDF("domino/domino.urdf", pos, orn, useMaximalCoordinates=useMaximalCoordinates)
 
+print("numDominoes=", numDominoes)
 
 #for j in range (20):
 #    for i in range (100):
@@ -51,48 +55,51 @@ print("numDominoes=",numDominoes)
 #        else:
 #          orn = p.getQuaternionFromEuler([0,-3.14*0.24,0])
 #          sphere = p.loadURDF("domino/domino.urdf",[(i-1)*0.04,1+j*.25,0.03], orn, useMaximalCoordinates=useMaximalCoordinates)
-    
-    
-print("loaded!")
 
+print("loaded!")
 
 #p.changeDynamics(sphere ,-1, mass=1000)
 
-door = p.loadURDF("door.urdf",[0,0,-11])
-p.changeDynamics(door ,1, linearDamping=0, angularDamping=0, jointDamping=0, mass=1)
+door = p.loadURDF("door.urdf", [0, 0, -11])
+p.changeDynamics(door, 1, linearDamping=0, angularDamping=0, jointDamping=0, mass=1)
 print("numJoints = ", p.getNumJoints(door))
 
-
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 position_control = True
 
-angle = math.pi*0.25
-p.resetJointState(door,1,angle)  
-angleread = p.getJointState(door,1)
-print("angleread = ",angleread)
+angle = math.pi * 0.25
+p.resetJointState(door, 1, angle)
+angleread = p.getJointState(door, 1)
+print("angleread = ", angleread)
 prevTime = time.time()
 
-angle = math.pi*0.5
+angle = math.pi * 0.5
 
-count=0
+count = 0
 while (1):
-  count+=1
-  if (count==12):
-      p.stopStateLogging(logId)
-      p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
+  count += 1
+  if (count == 12):
+    p.stopStateLogging(logId)
+    p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
 
   curTime = time.time()
-  
+
   diff = curTime - prevTime
   #every second, swap target angle
-  if (diff>1):
-    angle = - angle
+  if (diff > 1):
+    angle = -angle
     prevTime = curTime
-  
+
   if position_control:
-    p.setJointMotorControl2(door,1,p.POSITION_CONTROL, targetPosition = angle, positionGain=10.1, velocityGain=1, force=11.001)
-  else:  
-    p.setJointMotorControl2(door,1,p.VELOCITY_CONTROL, targetVelocity=1, force=1011)
+    p.setJointMotorControl2(door,
+                            1,
+                            p.POSITION_CONTROL,
+                            targetPosition=angle,
+                            positionGain=10.1,
+                            velocityGain=1,
+                            force=11.001)
+  else:
+    p.setJointMotorControl2(door, 1, p.VELOCITY_CONTROL, targetVelocity=1, force=1011)
   #contacts = p.getContactPoints()
   #print("contacts=",contacts)
   p.stepSimulation()
diff --git a/examples/pybullet/examples/pdControl.py b/examples/pybullet/examples/pdControl.py
index 696e99e18..ef0718dfc 100644
--- a/examples/pybullet/examples/pdControl.py
+++ b/examples/pybullet/examples/pdControl.py
@@ -1,4 +1,3 @@
-
 import pybullet as p
 from pdControllerExplicit import PDControllerExplicitMultiDof
 from pdControllerExplicit import PDControllerExplicit
@@ -6,107 +5,145 @@ from pdControllerStable import PDControllerStable
 
 import time
 
-
-useMaximalCoordinates=False
+useMaximalCoordinates = False
 p.connect(p.GUI)
-pole = p.loadURDF("cartpole.urdf", [0,0,0], useMaximalCoordinates=useMaximalCoordinates)
-pole2 = p.loadURDF("cartpole.urdf", [0,1,0], useMaximalCoordinates=useMaximalCoordinates)
-pole3 = p.loadURDF("cartpole.urdf", [0,2,0], useMaximalCoordinates=useMaximalCoordinates)
-pole4 = p.loadURDF("cartpole.urdf", [0,3,0], useMaximalCoordinates=useMaximalCoordinates)
+pole = p.loadURDF("cartpole.urdf", [0, 0, 0], useMaximalCoordinates=useMaximalCoordinates)
+pole2 = p.loadURDF("cartpole.urdf", [0, 1, 0], useMaximalCoordinates=useMaximalCoordinates)
+pole3 = p.loadURDF("cartpole.urdf", [0, 2, 0], useMaximalCoordinates=useMaximalCoordinates)
+pole4 = p.loadURDF("cartpole.urdf", [0, 3, 0], useMaximalCoordinates=useMaximalCoordinates)
 
 exPD = PDControllerExplicitMultiDof(p)
 sPD = PDControllerStable(p)
 
+for i in range(p.getNumJoints(pole2)):
+  #disable default constraint-based motors
+  p.setJointMotorControl2(pole, i, p.POSITION_CONTROL, targetPosition=0, force=0)
+  p.setJointMotorControl2(pole2, i, p.POSITION_CONTROL, targetPosition=0, force=0)
+  p.setJointMotorControl2(pole3, i, p.POSITION_CONTROL, targetPosition=0, force=0)
+  p.setJointMotorControl2(pole4, i, p.POSITION_CONTROL, targetPosition=0, force=0)
 
-for i in range (p.getNumJoints(pole2)):
-	#disable default constraint-based motors
-	p.setJointMotorControl2(pole,i,p.POSITION_CONTROL,targetPosition=0,force=0)
-	p.setJointMotorControl2(pole2,i,p.POSITION_CONTROL,targetPosition=0,force=0)
-	p.setJointMotorControl2(pole3,i,p.POSITION_CONTROL,targetPosition=0,force=0)
-	p.setJointMotorControl2(pole4,i,p.POSITION_CONTROL,targetPosition=0,force=0)
-	
-	#print("joint",i,"=",p.getJointInfo(pole2,i))
-
+  #print("joint",i,"=",p.getJointInfo(pole2,i))
 
-timeStepId = p.addUserDebugParameter("timeStep",0.001,0.1,0.01)
-desiredPosCartId = p.addUserDebugParameter("desiredPosCart",-10,10,2)
-desiredVelCartId = p.addUserDebugParameter("desiredVelCart",-10,10,0)
-kpCartId = p.addUserDebugParameter("kpCart",0,500,1300)
-kdCartId = p.addUserDebugParameter("kdCart",0,300,150)
-maxForceCartId = p.addUserDebugParameter("maxForceCart",0,5000,1000)
+timeStepId = p.addUserDebugParameter("timeStep", 0.001, 0.1, 0.01)
+desiredPosCartId = p.addUserDebugParameter("desiredPosCart", -10, 10, 2)
+desiredVelCartId = p.addUserDebugParameter("desiredVelCart", -10, 10, 0)
+kpCartId = p.addUserDebugParameter("kpCart", 0, 500, 1300)
+kdCartId = p.addUserDebugParameter("kdCart", 0, 300, 150)
+maxForceCartId = p.addUserDebugParameter("maxForceCart", 0, 5000, 1000)
 
-textColor = [1,1,1]
+textColor = [1, 1, 1]
 shift = 0.05
-p.addUserDebugText("explicit PD", [shift,0,.1],textColor,parentObjectUniqueId=pole,parentLinkIndex=1)
-p.addUserDebugText("explicit PD plugin", [shift,0,-.1],textColor,parentObjectUniqueId=pole2,parentLinkIndex=1)
-p.addUserDebugText("stablePD", [shift,0,.1],textColor,parentObjectUniqueId=pole4,parentLinkIndex=1)
-p.addUserDebugText("position constraint", [shift,0,-.1],textColor,parentObjectUniqueId=pole3,parentLinkIndex=1)
-
-desiredPosPoleId = p.addUserDebugParameter("desiredPosPole",-10,10,0)
-desiredVelPoleId = p.addUserDebugParameter("desiredVelPole",-10,10,0)
-kpPoleId = p.addUserDebugParameter("kpPole",0,500,1200)
-kdPoleId = p.addUserDebugParameter("kdPole",0,300,100)
-maxForcePoleId = p.addUserDebugParameter("maxForcePole",0,5000,1000)
+p.addUserDebugText("explicit PD", [shift, 0, .1],
+                   textColor,
+                   parentObjectUniqueId=pole,
+                   parentLinkIndex=1)
+p.addUserDebugText("explicit PD plugin", [shift, 0, -.1],
+                   textColor,
+                   parentObjectUniqueId=pole2,
+                   parentLinkIndex=1)
+p.addUserDebugText("stablePD", [shift, 0, .1],
+                   textColor,
+                   parentObjectUniqueId=pole4,
+                   parentLinkIndex=1)
+p.addUserDebugText("position constraint", [shift, 0, -.1],
+                   textColor,
+                   parentObjectUniqueId=pole3,
+                   parentLinkIndex=1)
+
+desiredPosPoleId = p.addUserDebugParameter("desiredPosPole", -10, 10, 0)
+desiredVelPoleId = p.addUserDebugParameter("desiredVelPole", -10, 10, 0)
+kpPoleId = p.addUserDebugParameter("kpPole", 0, 500, 1200)
+kdPoleId = p.addUserDebugParameter("kdPole", 0, 300, 100)
+maxForcePoleId = p.addUserDebugParameter("maxForcePole", 0, 5000, 1000)
 
 pd = p.loadPlugin("pdControlPlugin")
 
-	
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 
 useRealTimeSim = False
 
 p.setRealTimeSimulation(useRealTimeSim)
-	
+
 timeStep = 0.001
-	
-	
+
 while p.isConnected():
-	#p.getCameraImage(320,200)
-	timeStep = p.readUserDebugParameter(timeStepId)
-	p.setTimeStep(timeStep)
-
-	desiredPosCart = p.readUserDebugParameter(desiredPosCartId)
-	desiredVelCart = p.readUserDebugParameter(desiredVelCartId)
-	kpCart = p.readUserDebugParameter(kpCartId)
-	kdCart = p.readUserDebugParameter(kdCartId)
-	maxForceCart = p.readUserDebugParameter(maxForceCartId)
-
-	desiredPosPole = p.readUserDebugParameter(desiredPosPoleId)
-	desiredVelPole = p.readUserDebugParameter(desiredVelPoleId)
-	kpPole = p.readUserDebugParameter(kpPoleId)
-	kdPole = p.readUserDebugParameter(kdPoleId)
-	maxForcePole = p.readUserDebugParameter(maxForcePoleId)
-	
-	basePos,baseOrn = p.getBasePositionAndOrientation(pole)
-	
-	baseDof=7
-	taus = exPD.computePD(pole, [0,1], [basePos[0],basePos[1],basePos[2],baseOrn[0],baseOrn[1],baseOrn[2],baseOrn[3],desiredPosCart,desiredPosPole],
-			[0,0,0,0,0,0,0,desiredVelCart,desiredVelPole], [0,0,0,0,0,0,0,kpCart,kpPole], [0,0,0,0,0,0,0,kdCart,kdPole],[0,0,0,0,0,0,0,maxForceCart,maxForcePole], timeStep)
-	
-	for j in [0,1]:
-		p.setJointMotorControlMultiDof(pole, j, controlMode=p.TORQUE_CONTROL, force=[taus[j+baseDof]])
-	#p.setJointMotorControlArray(pole, [0,1], controlMode=p.TORQUE_CONTROL, forces=taus)
-		
-	if (pd>=0):
-		link = 0
-		p.setJointMotorControl2(bodyUniqueId=pole2,jointIndex=link,controlMode=p.PD_CONTROL,targetPosition=desiredPosCart,targetVelocity=desiredVelCart,force=maxForceCart, positionGain=kpCart, velocityGain=kdCart)
-		link = 1
-		p.setJointMotorControl2(bodyUniqueId=pole2,jointIndex=link,controlMode=p.PD_CONTROL,targetPosition=desiredPosPole,targetVelocity=desiredVelPole,force=maxForcePole, positionGain=kpPole, velocityGain=kdPole)
-	
-	
-	
-	
-	taus = sPD.computePD(pole4, [0,1], [desiredPosCart,desiredPosPole],[desiredVelCart,desiredVelPole], [kpCart,kpPole], [kdCart,kdPole],[maxForceCart,maxForcePole], timeStep)
-	#p.setJointMotorControlArray(pole4, [0,1], controlMode=p.TORQUE_CONTROL, forces=taus)
-	for j in [0,1]:
-		p.setJointMotorControlMultiDof(pole4, j, controlMode=p.TORQUE_CONTROL, force=[taus[j]])
-
-		
-	p.setJointMotorControl2(pole3,0, p.POSITION_CONTROL, targetPosition=desiredPosCart, targetVelocity=desiredVelCart, positionGain=timeStep*(kpCart/150.), velocityGain=0.5, force=maxForceCart)
-	p.setJointMotorControl2(pole3,1, p.POSITION_CONTROL, targetPosition=desiredPosPole, targetVelocity=desiredVelPole, positionGain=timeStep*(kpPole/150.), velocityGain=0.5, force=maxForcePole)
-	
-	if (not useRealTimeSim):
-		p.stepSimulation()
-		time.sleep(timeStep)
-	
-	
+  #p.getCameraImage(320,200)
+  timeStep = p.readUserDebugParameter(timeStepId)
+  p.setTimeStep(timeStep)
+
+  desiredPosCart = p.readUserDebugParameter(desiredPosCartId)
+  desiredVelCart = p.readUserDebugParameter(desiredVelCartId)
+  kpCart = p.readUserDebugParameter(kpCartId)
+  kdCart = p.readUserDebugParameter(kdCartId)
+  maxForceCart = p.readUserDebugParameter(maxForceCartId)
+
+  desiredPosPole = p.readUserDebugParameter(desiredPosPoleId)
+  desiredVelPole = p.readUserDebugParameter(desiredVelPoleId)
+  kpPole = p.readUserDebugParameter(kpPoleId)
+  kdPole = p.readUserDebugParameter(kdPoleId)
+  maxForcePole = p.readUserDebugParameter(maxForcePoleId)
+
+  basePos, baseOrn = p.getBasePositionAndOrientation(pole)
+
+  baseDof = 7
+  taus = exPD.computePD(pole, [0, 1], [
+      basePos[0], basePos[1], basePos[2], baseOrn[0], baseOrn[1], baseOrn[2], baseOrn[3],
+      desiredPosCart, desiredPosPole
+  ], [0, 0, 0, 0, 0, 0, 0, desiredVelCart, desiredVelPole], [0, 0, 0, 0, 0, 0, 0, kpCart, kpPole],
+                        [0, 0, 0, 0, 0, 0, 0, kdCart, kdPole],
+                        [0, 0, 0, 0, 0, 0, 0, maxForceCart, maxForcePole], timeStep)
+
+  for j in [0, 1]:
+    p.setJointMotorControlMultiDof(pole,
+                                   j,
+                                   controlMode=p.TORQUE_CONTROL,
+                                   force=[taus[j + baseDof]])
+  #p.setJointMotorControlArray(pole, [0,1], controlMode=p.TORQUE_CONTROL, forces=taus)
+
+  if (pd >= 0):
+    link = 0
+    p.setJointMotorControl2(bodyUniqueId=pole2,
+                            jointIndex=link,
+                            controlMode=p.PD_CONTROL,
+                            targetPosition=desiredPosCart,
+                            targetVelocity=desiredVelCart,
+                            force=maxForceCart,
+                            positionGain=kpCart,
+                            velocityGain=kdCart)
+    link = 1
+    p.setJointMotorControl2(bodyUniqueId=pole2,
+                            jointIndex=link,
+                            controlMode=p.PD_CONTROL,
+                            targetPosition=desiredPosPole,
+                            targetVelocity=desiredVelPole,
+                            force=maxForcePole,
+                            positionGain=kpPole,
+                            velocityGain=kdPole)
+
+  taus = sPD.computePD(pole4, [0, 1], [desiredPosCart, desiredPosPole],
+                       [desiredVelCart, desiredVelPole], [kpCart, kpPole], [kdCart, kdPole],
+                       [maxForceCart, maxForcePole], timeStep)
+  #p.setJointMotorControlArray(pole4, [0,1], controlMode=p.TORQUE_CONTROL, forces=taus)
+  for j in [0, 1]:
+    p.setJointMotorControlMultiDof(pole4, j, controlMode=p.TORQUE_CONTROL, force=[taus[j]])
+
+  p.setJointMotorControl2(pole3,
+                          0,
+                          p.POSITION_CONTROL,
+                          targetPosition=desiredPosCart,
+                          targetVelocity=desiredVelCart,
+                          positionGain=timeStep * (kpCart / 150.),
+                          velocityGain=0.5,
+                          force=maxForceCart)
+  p.setJointMotorControl2(pole3,
+                          1,
+                          p.POSITION_CONTROL,
+                          targetPosition=desiredPosPole,
+                          targetVelocity=desiredVelPole,
+                          positionGain=timeStep * (kpPole / 150.),
+                          velocityGain=0.5,
+                          force=maxForcePole)
+
+  if (not useRealTimeSim):
+    p.stepSimulation()
+    time.sleep(timeStep)
diff --git a/examples/pybullet/examples/pdControllerExplicit.py b/examples/pybullet/examples/pdControllerExplicit.py
index e81eda08d..20cd0a65e 100644
--- a/examples/pybullet/examples/pdControllerExplicit.py
+++ b/examples/pybullet/examples/pdControllerExplicit.py
@@ -2,88 +2,97 @@ import numpy as np
 
 
 class PDControllerExplicitMultiDof(object):
-		def __init__(self, pb):
-			self._pb = pb
 
-		def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds, maxForces, timeStep):
-			
-			numJoints = len(jointIndices)#self._pb.getNumJoints(bodyUniqueId)
-			curPos,curOrn = self._pb.getBasePositionAndOrientation(bodyUniqueId)
-			q1 = [curPos[0],curPos[1],curPos[2],curOrn[0],curOrn[1],curOrn[2],curOrn[3]]
-			baseLinVel, baseAngVel = self._pb.getBaseVelocity(bodyUniqueId)
-			qdot1 = [baseLinVel[0],baseLinVel[1],baseLinVel[2],baseAngVel[0],baseAngVel[1],baseAngVel[2],0]
-			qError = [0,0,0, 0,0,0,0]
-			qIndex=7
-			qdotIndex=7
-			zeroAccelerations=[0,0,0,  0,0,0,0]
-			for i in range (numJoints):
-				js = self._pb.getJointStateMultiDof(bodyUniqueId, jointIndices[i])
-				
-				jointPos=js[0]
-				jointVel = js[1]
-				q1+=jointPos
-				
-				if len(js[0])==1:
-					desiredPos=desiredPositions[qIndex]
-					
-					qdiff=desiredPos - jointPos[0]
-					qError.append(qdiff)
-					zeroAccelerations.append(0.)
-					qdot1+=jointVel
-					qIndex+=1
-					qdotIndex+=1
-				if len(js[0])==4:
-					desiredPos=[desiredPositions[qIndex],desiredPositions[qIndex+1],desiredPositions[qIndex+2],desiredPositions[qIndex+3]]
-					axis = self._pb.getAxisDifferenceQuaternion(desiredPos,jointPos)
-					jointVelNew = [jointVel[0],jointVel[1],jointVel[2],0]
-					qdot1+=jointVelNew
-					qError.append(axis[0])
-					qError.append(axis[1])
-					qError.append(axis[2])
-					qError.append(0)
-					desiredVel=[desiredVelocities[qdotIndex],desiredVelocities[qdotIndex+1],desiredVelocities[qdotIndex+2]]
-					zeroAccelerations+=[0.,0.,0.,0.]
-					qIndex+=4
-					qdotIndex+=4
-			
-			q = np.array(q1)
-			qdot=np.array(qdot1)
-			qdotdesired = np.array(desiredVelocities)
-			qdoterr = qdotdesired-qdot
-			Kp = np.diagflat(kps)
-			Kd = np.diagflat(kds)
-			p =  Kp.dot(qError)
-			d = Kd.dot(qdoterr)
-			forces = p + d
-			maxF = np.array(maxForces)
-			forces = np.clip(forces, -maxF , maxF )
-			return forces
+  def __init__(self, pb):
+    self._pb = pb
+
+  def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds,
+                maxForces, timeStep):
+
+    numJoints = len(jointIndices)  #self._pb.getNumJoints(bodyUniqueId)
+    curPos, curOrn = self._pb.getBasePositionAndOrientation(bodyUniqueId)
+    q1 = [curPos[0], curPos[1], curPos[2], curOrn[0], curOrn[1], curOrn[2], curOrn[3]]
+    baseLinVel, baseAngVel = self._pb.getBaseVelocity(bodyUniqueId)
+    qdot1 = [
+        baseLinVel[0], baseLinVel[1], baseLinVel[2], baseAngVel[0], baseAngVel[1], baseAngVel[2], 0
+    ]
+    qError = [0, 0, 0, 0, 0, 0, 0]
+    qIndex = 7
+    qdotIndex = 7
+    zeroAccelerations = [0, 0, 0, 0, 0, 0, 0]
+    for i in range(numJoints):
+      js = self._pb.getJointStateMultiDof(bodyUniqueId, jointIndices[i])
+
+      jointPos = js[0]
+      jointVel = js[1]
+      q1 += jointPos
+
+      if len(js[0]) == 1:
+        desiredPos = desiredPositions[qIndex]
+
+        qdiff = desiredPos - jointPos[0]
+        qError.append(qdiff)
+        zeroAccelerations.append(0.)
+        qdot1 += jointVel
+        qIndex += 1
+        qdotIndex += 1
+      if len(js[0]) == 4:
+        desiredPos = [
+            desiredPositions[qIndex], desiredPositions[qIndex + 1], desiredPositions[qIndex + 2],
+            desiredPositions[qIndex + 3]
+        ]
+        axis = self._pb.getAxisDifferenceQuaternion(desiredPos, jointPos)
+        jointVelNew = [jointVel[0], jointVel[1], jointVel[2], 0]
+        qdot1 += jointVelNew
+        qError.append(axis[0])
+        qError.append(axis[1])
+        qError.append(axis[2])
+        qError.append(0)
+        desiredVel = [
+            desiredVelocities[qdotIndex], desiredVelocities[qdotIndex + 1],
+            desiredVelocities[qdotIndex + 2]
+        ]
+        zeroAccelerations += [0., 0., 0., 0.]
+        qIndex += 4
+        qdotIndex += 4
+
+    q = np.array(q1)
+    qdot = np.array(qdot1)
+    qdotdesired = np.array(desiredVelocities)
+    qdoterr = qdotdesired - qdot
+    Kp = np.diagflat(kps)
+    Kd = np.diagflat(kds)
+    p = Kp.dot(qError)
+    d = Kd.dot(qdoterr)
+    forces = p + d
+    maxF = np.array(maxForces)
+    forces = np.clip(forces, -maxF, maxF)
+    return forces
 
 
-			
 class PDControllerExplicit(object):
-		def __init__(self, pb):
-			self._pb = pb
 
-		def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds, maxForces, timeStep):
-			numJoints = self._pb.getNumJoints(bodyUniqueId)
-			jointStates = self._pb.getJointStates(bodyUniqueId, jointIndices)
-			q1 = []
-			qdot1 = []
-			for i in range (numJoints):
-				q1.append(jointStates[i][0])
-				qdot1.append(jointStates[i][1])
-			q = np.array(q1)
-			qdot=np.array(qdot1)
-			qdes = np.array(desiredPositions)
-			qdotdes = np.array(desiredVelocities)
-			qError = qdes - q
-			qdotError = qdotdes - qdot
-			Kp = np.diagflat(kps)
-			Kd = np.diagflat(kds)
-			forces = Kp.dot(qError) + Kd.dot(qdotError)
-			maxF = np.array(maxForces)
-			forces = np.clip(forces, -maxF , maxF )
-			return forces
-			
-			
\ No newline at end of file
+  def __init__(self, pb):
+    self._pb = pb
+
+  def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds,
+                maxForces, timeStep):
+    numJoints = self._pb.getNumJoints(bodyUniqueId)
+    jointStates = self._pb.getJointStates(bodyUniqueId, jointIndices)
+    q1 = []
+    qdot1 = []
+    for i in range(numJoints):
+      q1.append(jointStates[i][0])
+      qdot1.append(jointStates[i][1])
+    q = np.array(q1)
+    qdot = np.array(qdot1)
+    qdes = np.array(desiredPositions)
+    qdotdes = np.array(desiredVelocities)
+    qError = qdes - q
+    qdotError = qdotdes - qdot
+    Kp = np.diagflat(kps)
+    Kd = np.diagflat(kds)
+    forces = Kp.dot(qError) + Kd.dot(qdotError)
+    maxF = np.array(maxForces)
+    forces = np.clip(forces, -maxF, maxF)
+    return forces
diff --git a/examples/pybullet/examples/pdControllerStable.py b/examples/pybullet/examples/pdControllerStable.py
index 76f13bbf5..250a4f56b 100644
--- a/examples/pybullet/examples/pdControllerStable.py
+++ b/examples/pybullet/examples/pdControllerStable.py
@@ -1,144 +1,148 @@
 import numpy as np
 
 
-
 class PDControllerStableMultiDof(object):
-		def __init__(self, pb):
-			self._pb = pb
-
-		def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds, maxForces, timeStep):
-			
-			numJoints = len(jointIndices)#self._pb.getNumJoints(bodyUniqueId)
-			curPos,curOrn = self._pb.getBasePositionAndOrientation(bodyUniqueId)
-			#q1 = [desiredPositions[0],desiredPositions[1],desiredPositions[2],desiredPositions[3],desiredPositions[4],desiredPositions[5],desiredPositions[6]]
-			q1 = [curPos[0],curPos[1],curPos[2],curOrn[0],curOrn[1],curOrn[2],curOrn[3]]
-			
-			#qdot1 = [0,0,0, 0,0,0,0]
-			baseLinVel, baseAngVel = self._pb.getBaseVelocity(bodyUniqueId)
-			
-			qdot1 = [baseLinVel[0],baseLinVel[1],baseLinVel[2],baseAngVel[0],baseAngVel[1],baseAngVel[2],0]
-			qError = [0,0,0, 0,0,0,0]
-			
-			qIndex=7
-			qdotIndex=7
-			zeroAccelerations=[0,0,0,  0,0,0,0]
-			for i in range (numJoints):
-				js = self._pb.getJointStateMultiDof(bodyUniqueId, jointIndices[i])
-				
-				jointPos=js[0]
-				jointVel = js[1]
-				q1+=jointPos
-				
-				if len(js[0])==1:
-					desiredPos=desiredPositions[qIndex]
-					
-					qdiff=desiredPos - jointPos[0]
-					qError.append(qdiff)
-					zeroAccelerations.append(0.)
-					qdot1+=jointVel
-					qIndex+=1
-					qdotIndex+=1
-				if len(js[0])==4:
-					desiredPos=[desiredPositions[qIndex],desiredPositions[qIndex+1],desiredPositions[qIndex+2],desiredPositions[qIndex+3]]
-					axis = self._pb.getAxisDifferenceQuaternion(desiredPos,jointPos)
-					jointVelNew = [jointVel[0],jointVel[1],jointVel[2],0]
-					qdot1+=jointVelNew
-					qError.append(axis[0])
-					qError.append(axis[1])
-					qError.append(axis[2])
-					qError.append(0)
-					desiredVel=[desiredVelocities[qdotIndex],desiredVelocities[qdotIndex+1],desiredVelocities[qdotIndex+2]]
-					zeroAccelerations+=[0.,0.,0.,0.]
-					qIndex+=4
-					qdotIndex+=4
-			
-			q = np.array(q1)
-			qdot=np.array(qdot1)
-			
-			qdotdesired = np.array(desiredVelocities)
-			qdoterr = qdotdesired-qdot
-			
-			
-			Kp = np.diagflat(kps)
-			Kd = np.diagflat(kds)
-			
-			p =  Kp.dot(qError)
-			
-			#np.savetxt("pb_qError.csv", qError, delimiter=",")
-			#np.savetxt("pb_p.csv", p, delimiter=",")
-			
-			d = Kd.dot(qdoterr)
-
-			#np.savetxt("pb_d.csv", d, delimiter=",")
-			#np.savetxt("pbqdoterr.csv", qdoterr, delimiter=",")
-			
-			
-			M1 = self._pb.calculateMassMatrix(bodyUniqueId,q1, flags=1)
-			
-			
-			M2 = np.array(M1)
-			#np.savetxt("M2.csv", M2, delimiter=",")
-			
-			M = (M2 + Kd * timeStep)
-			
-			#np.savetxt("pbM_tKd.csv",M, delimiter=",")
-			
-		
-			
-			c1 = self._pb.calculateInverseDynamics(bodyUniqueId, q1, qdot1, zeroAccelerations, flags=1)
-			
-			
-			c = np.array(c1)
-			#np.savetxt("pbC.csv",c, delimiter=",")
-			A = M
-			#p = [0]*43
-			b =  p + d -c
-			#np.savetxt("pb_acc.csv",b, delimiter=",")
-			qddot = np.linalg.solve(A, b)
-			tau = p + d - Kd.dot(qddot) * timeStep
-			#print("len(tau)=",len(tau))
-			maxF = np.array(maxForces)
-			forces = np.clip(tau, -maxF , maxF )
-			return forces
-			
-		
-			
+
+  def __init__(self, pb):
+    self._pb = pb
+
+  def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds,
+                maxForces, timeStep):
+
+    numJoints = len(jointIndices)  #self._pb.getNumJoints(bodyUniqueId)
+    curPos, curOrn = self._pb.getBasePositionAndOrientation(bodyUniqueId)
+    #q1 = [desiredPositions[0],desiredPositions[1],desiredPositions[2],desiredPositions[3],desiredPositions[4],desiredPositions[5],desiredPositions[6]]
+    q1 = [curPos[0], curPos[1], curPos[2], curOrn[0], curOrn[1], curOrn[2], curOrn[3]]
+
+    #qdot1 = [0,0,0, 0,0,0,0]
+    baseLinVel, baseAngVel = self._pb.getBaseVelocity(bodyUniqueId)
+
+    qdot1 = [
+        baseLinVel[0], baseLinVel[1], baseLinVel[2], baseAngVel[0], baseAngVel[1], baseAngVel[2], 0
+    ]
+    qError = [0, 0, 0, 0, 0, 0, 0]
+
+    qIndex = 7
+    qdotIndex = 7
+    zeroAccelerations = [0, 0, 0, 0, 0, 0, 0]
+    for i in range(numJoints):
+      js = self._pb.getJointStateMultiDof(bodyUniqueId, jointIndices[i])
+
+      jointPos = js[0]
+      jointVel = js[1]
+      q1 += jointPos
+
+      if len(js[0]) == 1:
+        desiredPos = desiredPositions[qIndex]
+
+        qdiff = desiredPos - jointPos[0]
+        qError.append(qdiff)
+        zeroAccelerations.append(0.)
+        qdot1 += jointVel
+        qIndex += 1
+        qdotIndex += 1
+      if len(js[0]) == 4:
+        desiredPos = [
+            desiredPositions[qIndex], desiredPositions[qIndex + 1], desiredPositions[qIndex + 2],
+            desiredPositions[qIndex + 3]
+        ]
+        axis = self._pb.getAxisDifferenceQuaternion(desiredPos, jointPos)
+        jointVelNew = [jointVel[0], jointVel[1], jointVel[2], 0]
+        qdot1 += jointVelNew
+        qError.append(axis[0])
+        qError.append(axis[1])
+        qError.append(axis[2])
+        qError.append(0)
+        desiredVel = [
+            desiredVelocities[qdotIndex], desiredVelocities[qdotIndex + 1],
+            desiredVelocities[qdotIndex + 2]
+        ]
+        zeroAccelerations += [0., 0., 0., 0.]
+        qIndex += 4
+        qdotIndex += 4
+
+    q = np.array(q1)
+    qdot = np.array(qdot1)
+
+    qdotdesired = np.array(desiredVelocities)
+    qdoterr = qdotdesired - qdot
+
+    Kp = np.diagflat(kps)
+    Kd = np.diagflat(kds)
+
+    p = Kp.dot(qError)
+
+    #np.savetxt("pb_qError.csv", qError, delimiter=",")
+    #np.savetxt("pb_p.csv", p, delimiter=",")
+
+    d = Kd.dot(qdoterr)
+
+    #np.savetxt("pb_d.csv", d, delimiter=",")
+    #np.savetxt("pbqdoterr.csv", qdoterr, delimiter=",")
+
+    M1 = self._pb.calculateMassMatrix(bodyUniqueId, q1, flags=1)
+
+    M2 = np.array(M1)
+    #np.savetxt("M2.csv", M2, delimiter=",")
+
+    M = (M2 + Kd * timeStep)
+
+    #np.savetxt("pbM_tKd.csv",M, delimiter=",")
+
+    c1 = self._pb.calculateInverseDynamics(bodyUniqueId, q1, qdot1, zeroAccelerations, flags=1)
+
+    c = np.array(c1)
+    #np.savetxt("pbC.csv",c, delimiter=",")
+    A = M
+    #p = [0]*43
+    b = p + d - c
+    #np.savetxt("pb_acc.csv",b, delimiter=",")
+    qddot = np.linalg.solve(A, b)
+    tau = p + d - Kd.dot(qddot) * timeStep
+    #print("len(tau)=",len(tau))
+    maxF = np.array(maxForces)
+    forces = np.clip(tau, -maxF, maxF)
+    return forces
+
+
 class PDControllerStable(object):
-		def __init__(self, pb):
-			self._pb = pb
-
-		def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds, maxForces, timeStep):
-			numJoints = self._pb.getNumJoints(bodyUniqueId)
-			jointStates = self._pb.getJointStates(bodyUniqueId, jointIndices)
-			q1 = []
-			qdot1 = []
-			zeroAccelerations = []
-			for i in range (numJoints):
-				q1.append(jointStates[i][0])
-				qdot1.append(jointStates[i][1])
-				zeroAccelerations.append(0)
-			q = np.array(q1)
-			qdot=np.array(qdot1)
-			qdes = np.array(desiredPositions)
-			qdotdes = np.array(desiredVelocities)
-			qError = qdes - q
-			qdotError = qdotdes - qdot
-			Kp = np.diagflat(kps)
-			Kd = np.diagflat(kds)
-			p =  Kp.dot(qError)
-			d = Kd.dot(qdotError)
-			forces = p + d
-			
-			M1 = self._pb.calculateMassMatrix(bodyUniqueId,q1)
-			M2 = np.array(M1)
-			M = (M2 + Kd * timeStep)
-			c1 = self._pb.calculateInverseDynamics(bodyUniqueId, q1, qdot1, zeroAccelerations)
-			c = np.array(c1)
-			A = M
-			b = -c + p + d
-			qddot = np.linalg.solve(A, b)
-			tau = p + d - Kd.dot(qddot) * timeStep
-			maxF = np.array(maxForces)
-			forces = np.clip(tau, -maxF , maxF )
-			#print("c=",c)
-			return tau
\ No newline at end of file
+
+  def __init__(self, pb):
+    self._pb = pb
+
+  def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds,
+                maxForces, timeStep):
+    numJoints = self._pb.getNumJoints(bodyUniqueId)
+    jointStates = self._pb.getJointStates(bodyUniqueId, jointIndices)
+    q1 = []
+    qdot1 = []
+    zeroAccelerations = []
+    for i in range(numJoints):
+      q1.append(jointStates[i][0])
+      qdot1.append(jointStates[i][1])
+      zeroAccelerations.append(0)
+    q = np.array(q1)
+    qdot = np.array(qdot1)
+    qdes = np.array(desiredPositions)
+    qdotdes = np.array(desiredVelocities)
+    qError = qdes - q
+    qdotError = qdotdes - qdot
+    Kp = np.diagflat(kps)
+    Kd = np.diagflat(kds)
+    p = Kp.dot(qError)
+    d = Kd.dot(qdotError)
+    forces = p + d
+
+    M1 = self._pb.calculateMassMatrix(bodyUniqueId, q1)
+    M2 = np.array(M1)
+    M = (M2 + Kd * timeStep)
+    c1 = self._pb.calculateInverseDynamics(bodyUniqueId, q1, qdot1, zeroAccelerations)
+    c = np.array(c1)
+    A = M
+    b = -c + p + d
+    qddot = np.linalg.solve(A, b)
+    tau = p + d - Kd.dot(qddot) * timeStep
+    maxF = np.array(maxForces)
+    forces = np.clip(tau, -maxF, maxF)
+    #print("c=",c)
+    return tau
diff --git a/examples/pybullet/examples/pointCloudFromCameraImage.py b/examples/pybullet/examples/pointCloudFromCameraImage.py
index fee30d8e4..e7aa33fad 100644
--- a/examples/pybullet/examples/pointCloudFromCameraImage.py
+++ b/examples/pybullet/examples/pointCloudFromCameraImage.py
@@ -1,123 +1,136 @@
-
 import pybullet as p
 import math
 import numpy as np
 
-
-
 p.connect(p.GUI)
 plane = p.loadURDF("plane100.urdf")
-cube = p.loadURDF("cube.urdf",[0,0,1])
-
-def getRayFromTo(mouseX,mouseY):
-        width, height, viewMat, projMat, cameraUp, camForward, horizon,vertical, _,_,dist, camTarget = p.getDebugVisualizerCamera()
-        camPos = [camTarget[0] - dist*camForward[0],camTarget[1] - dist*camForward[1],camTarget[2] - dist*camForward[2]]
-        farPlane = 10000
-        rayForward = [(camTarget[0]-camPos[0]),(camTarget[1]-camPos[1]),(camTarget[2]-camPos[2])]
-        lenFwd = math.sqrt(rayForward[0]*rayForward[0]+rayForward[1]*rayForward[1]+rayForward[2]*rayForward[2])
-        invLen = farPlane*1./lenFwd
-        rayForward = [invLen*rayForward[0],invLen*rayForward[1],invLen*rayForward[2]]
-        rayFrom = camPos
-        oneOverWidth = float(1)/float(width)
-        oneOverHeight = float(1)/float(height)
-
-        dHor = [horizon[0] * oneOverWidth,horizon[1] * oneOverWidth,horizon[2] * oneOverWidth]
-        dVer = [vertical[0] * oneOverHeight,vertical[1] * oneOverHeight,vertical[2] * oneOverHeight]
-        rayToCenter=[rayFrom[0]+rayForward[0],rayFrom[1]+rayForward[1],rayFrom[2]+rayForward[2]]
-        ortho=[- 0.5 * horizon[0] + 0.5 * vertical[0]+float(mouseX)*dHor[0]-float(mouseY)*dVer[0],
-             - 0.5 * horizon[1] + 0.5 * vertical[1]+float(mouseX)*dHor[1]-float(mouseY)*dVer[1],
-             - 0.5 * horizon[2] + 0.5 * vertical[2]+float(mouseX)*dHor[2]-float(mouseY)*dVer[2]]
-
-        rayTo = [rayFrom[0]+rayForward[0]  +ortho[0],
-                                        rayFrom[1]+rayForward[1]  +ortho[1],
-                                        rayFrom[2]+rayForward[2]  +ortho[2]]
-        lenOrtho = math.sqrt(ortho[0]*ortho[0]+ortho[1]*ortho[1]+ortho[2]*ortho[2])
-        alpha = math.atan(lenOrtho/farPlane)
-        return rayFrom,rayTo, alpha
-
-
-width, height, viewMat, projMat, cameraUp, camForward, horizon,vertical, _,_,dist, camTarget = p.getDebugVisualizerCamera()
-camPos = [camTarget[0] - dist*camForward[0],camTarget[1] - dist*camForward[1],camTarget[2] - dist*camForward[2]]
+cube = p.loadURDF("cube.urdf", [0, 0, 1])
+
+
+def getRayFromTo(mouseX, mouseY):
+  width, height, viewMat, projMat, cameraUp, camForward, horizon, vertical, _, _, dist, camTarget = p.getDebugVisualizerCamera(
+  )
+  camPos = [
+      camTarget[0] - dist * camForward[0], camTarget[1] - dist * camForward[1],
+      camTarget[2] - dist * camForward[2]
+  ]
+  farPlane = 10000
+  rayForward = [(camTarget[0] - camPos[0]), (camTarget[1] - camPos[1]), (camTarget[2] - camPos[2])]
+  lenFwd = math.sqrt(rayForward[0] * rayForward[0] + rayForward[1] * rayForward[1] +
+                     rayForward[2] * rayForward[2])
+  invLen = farPlane * 1. / lenFwd
+  rayForward = [invLen * rayForward[0], invLen * rayForward[1], invLen * rayForward[2]]
+  rayFrom = camPos
+  oneOverWidth = float(1) / float(width)
+  oneOverHeight = float(1) / float(height)
+
+  dHor = [horizon[0] * oneOverWidth, horizon[1] * oneOverWidth, horizon[2] * oneOverWidth]
+  dVer = [vertical[0] * oneOverHeight, vertical[1] * oneOverHeight, vertical[2] * oneOverHeight]
+  rayToCenter = [
+      rayFrom[0] + rayForward[0], rayFrom[1] + rayForward[1], rayFrom[2] + rayForward[2]
+  ]
+  ortho = [
+      -0.5 * horizon[0] + 0.5 * vertical[0] + float(mouseX) * dHor[0] - float(mouseY) * dVer[0],
+      -0.5 * horizon[1] + 0.5 * vertical[1] + float(mouseX) * dHor[1] - float(mouseY) * dVer[1],
+      -0.5 * horizon[2] + 0.5 * vertical[2] + float(mouseX) * dHor[2] - float(mouseY) * dVer[2]
+  ]
+
+  rayTo = [
+      rayFrom[0] + rayForward[0] + ortho[0], rayFrom[1] + rayForward[1] + ortho[1],
+      rayFrom[2] + rayForward[2] + ortho[2]
+  ]
+  lenOrtho = math.sqrt(ortho[0] * ortho[0] + ortho[1] * ortho[1] + ortho[2] * ortho[2])
+  alpha = math.atan(lenOrtho / farPlane)
+  return rayFrom, rayTo, alpha
+
+
+width, height, viewMat, projMat, cameraUp, camForward, horizon, vertical, _, _, dist, camTarget = p.getDebugVisualizerCamera(
+)
+camPos = [
+    camTarget[0] - dist * camForward[0], camTarget[1] - dist * camForward[1],
+    camTarget[2] - dist * camForward[2]
+]
 farPlane = 10000
-rayForward = [(camTarget[0]-camPos[0]),(camTarget[1]-camPos[1]),(camTarget[2]-camPos[2])]
-lenFwd = math.sqrt(rayForward[0]*rayForward[0]+rayForward[1]*rayForward[1]+rayForward[2]*rayForward[2])
-oneOverWidth = float(1)/float(width)
-oneOverHeight = float(1)/float(height)
-dHor = [horizon[0] * oneOverWidth,horizon[1] * oneOverWidth,horizon[2] * oneOverWidth]
-dVer = [vertical[0] * oneOverHeight,vertical[1] * oneOverHeight,vertical[2] * oneOverHeight]
-        
-lendHor = math.sqrt(dHor[0]*dHor[0]+dHor[1]*dHor[1]+dHor[2]*dHor[2]) 
-lendVer = math.sqrt(dVer[0]*dVer[0]+dVer[1]*dVer[1]+dVer[2]*dVer[2])
-
-cornersX = [0,width,width,0]
-cornersY = [0,0,height,height]
-corners3D=[]
-
-imgW = int(width/10)
-imgH = int(height/10)
-
-img = p.getCameraImage(imgW,imgH, renderer=p.ER_BULLET_HARDWARE_OPENGL)
-rgbBuffer=img[2]
+rayForward = [(camTarget[0] - camPos[0]), (camTarget[1] - camPos[1]), (camTarget[2] - camPos[2])]
+lenFwd = math.sqrt(rayForward[0] * rayForward[0] + rayForward[1] * rayForward[1] +
+                   rayForward[2] * rayForward[2])
+oneOverWidth = float(1) / float(width)
+oneOverHeight = float(1) / float(height)
+dHor = [horizon[0] * oneOverWidth, horizon[1] * oneOverWidth, horizon[2] * oneOverWidth]
+dVer = [vertical[0] * oneOverHeight, vertical[1] * oneOverHeight, vertical[2] * oneOverHeight]
+
+lendHor = math.sqrt(dHor[0] * dHor[0] + dHor[1] * dHor[1] + dHor[2] * dHor[2])
+lendVer = math.sqrt(dVer[0] * dVer[0] + dVer[1] * dVer[1] + dVer[2] * dVer[2])
+
+cornersX = [0, width, width, 0]
+cornersY = [0, 0, height, height]
+corners3D = []
+
+imgW = int(width / 10)
+imgH = int(height / 10)
+
+img = p.getCameraImage(imgW, imgH, renderer=p.ER_BULLET_HARDWARE_OPENGL)
+rgbBuffer = img[2]
 depthBuffer = img[3]
-print("rgbBuffer.shape=",rgbBuffer.shape)
-print("depthBuffer.shape=",depthBuffer.shape)
+print("rgbBuffer.shape=", rgbBuffer.shape)
+print("depthBuffer.shape=", depthBuffer.shape)
 
 #disable rendering temporary makes adding objects faster
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)
-p.configureDebugVisualizer(p.COV_ENABLE_GUI,0)
-p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER,0)
-visualShapeId = p.createVisualShape(shapeType=p.GEOM_SPHERE, rgbaColor=[1,1,1,1], radius=0.03 )
-collisionShapeId = -1 #p.createCollisionShape(shapeType=p.GEOM_MESH, fileName="duck_vhacd.obj", collisionFramePosition=shift,meshScale=meshScale)
-
-for i in range (4):
-	w = cornersX[i]
-	h = cornersY[i]
-	rayFrom,rayTo, _= getRayFromTo(w,h)
-	rf = np.array(rayFrom)
-	rt = np.array(rayTo)
-	vec = rt-rf
-	l = np.sqrt(np.dot(vec,vec))
-	newTo = (0.01/l)*vec+rf
-	#print("len vec=",np.sqrt(np.dot(vec,vec)))
-			
-	p.addUserDebugLine(rayFrom,newTo,[1,0,0])
-	corners3D.append(newTo)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
+p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0)
+p.configureDebugVisualizer(p.COV_ENABLE_TINY_RENDERER, 0)
+visualShapeId = p.createVisualShape(shapeType=p.GEOM_SPHERE, rgbaColor=[1, 1, 1, 1], radius=0.03)
+collisionShapeId = -1  #p.createCollisionShape(shapeType=p.GEOM_MESH, fileName="duck_vhacd.obj", collisionFramePosition=shift,meshScale=meshScale)
+
+for i in range(4):
+  w = cornersX[i]
+  h = cornersY[i]
+  rayFrom, rayTo, _ = getRayFromTo(w, h)
+  rf = np.array(rayFrom)
+  rt = np.array(rayTo)
+  vec = rt - rf
+  l = np.sqrt(np.dot(vec, vec))
+  newTo = (0.01 / l) * vec + rf
+  #print("len vec=",np.sqrt(np.dot(vec,vec)))
+
+  p.addUserDebugLine(rayFrom, newTo, [1, 0, 0])
+  corners3D.append(newTo)
 count = 0
 
-stepX=5
-stepY=5
-for w in range(0,imgW,stepX):
-	for h in range (0,imgH,stepY):
-		count+=1
-		if ((count % 100)==0):
-			print(count,"out of ", imgW*imgH/(stepX*stepY))
-		rayFrom,rayTo, alpha = getRayFromTo(w*(width/imgW),h*(height/imgH))
-		rf = np.array(rayFrom)
-		rt = np.array(rayTo)
-		vec = rt-rf
-		l = np.sqrt(np.dot(vec,vec))
-		depthImg = float(depthBuffer[h,w])
-		far=1000.
-		near=0.01
-		depth = far * near / (far - (far - near) * depthImg)
-		depth/=math.cos(alpha)
-		newTo = (depth/l)*vec+rf
-		p.addUserDebugLine(rayFrom,newTo,[1,0,0])
-		mb = p.createMultiBody(baseMass=0,baseCollisionShapeIndex=collisionShapeId, baseVisualShapeIndex = visualShapeId, basePosition = newTo, useMaximalCoordinates=True)
-		color = rgbBuffer[h,w]
-		color=[color[0]/255.,color[1]/255.,color[2]/255.,1]
-		p.changeVisualShape(mb,-1,rgbaColor=color)
-p.addUserDebugLine(corners3D[0],corners3D[1],[1,0,0])
-p.addUserDebugLine(corners3D[1],corners3D[2],[1,0,0])
-p.addUserDebugLine(corners3D[2],corners3D[3],[1,0,0])
-p.addUserDebugLine(corners3D[3],corners3D[0],[1,0,0])
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
+stepX = 5
+stepY = 5
+for w in range(0, imgW, stepX):
+  for h in range(0, imgH, stepY):
+    count += 1
+    if ((count % 100) == 0):
+      print(count, "out of ", imgW * imgH / (stepX * stepY))
+    rayFrom, rayTo, alpha = getRayFromTo(w * (width / imgW), h * (height / imgH))
+    rf = np.array(rayFrom)
+    rt = np.array(rayTo)
+    vec = rt - rf
+    l = np.sqrt(np.dot(vec, vec))
+    depthImg = float(depthBuffer[h, w])
+    far = 1000.
+    near = 0.01
+    depth = far * near / (far - (far - near) * depthImg)
+    depth /= math.cos(alpha)
+    newTo = (depth / l) * vec + rf
+    p.addUserDebugLine(rayFrom, newTo, [1, 0, 0])
+    mb = p.createMultiBody(baseMass=0,
+                           baseCollisionShapeIndex=collisionShapeId,
+                           baseVisualShapeIndex=visualShapeId,
+                           basePosition=newTo,
+                           useMaximalCoordinates=True)
+    color = rgbBuffer[h, w]
+    color = [color[0] / 255., color[1] / 255., color[2] / 255., 1]
+    p.changeVisualShape(mb, -1, rgbaColor=color)
+p.addUserDebugLine(corners3D[0], corners3D[1], [1, 0, 0])
+p.addUserDebugLine(corners3D[1], corners3D[2], [1, 0, 0])
+p.addUserDebugLine(corners3D[2], corners3D[3], [1, 0, 0])
+p.addUserDebugLine(corners3D[3], corners3D[0], [1, 0, 0])
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
 print("ready\n")
 #p.removeBody(plane)
 #p.removeBody(cube)
 while (1):
-	p.setGravity(0,0,-10)
-
-		
-
-
+  p.setGravity(0, 0, -10)
diff --git a/examples/pybullet/examples/profileTiming.py b/examples/pybullet/examples/profileTiming.py
index 7b761fc9b..32854e7bf 100644
--- a/examples/pybullet/examples/profileTiming.py
+++ b/examples/pybullet/examples/profileTiming.py
@@ -3,10 +3,9 @@ import time
 
 p.connect(p.GUI)
 
-t = time.time()+0.1
+t = time.time() + 0.1
 
 logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "haha")
-while (time.time()<t):
-	p.submitProfileTiming("pythontest")
+while (time.time() < t):
+  p.submitProfileTiming("pythontest")
 p.stopStateLogging(logId)
-		
diff --git a/examples/pybullet/examples/projective_texture.py b/examples/pybullet/examples/projective_texture.py
index 5c3f2ffb5..1f38eb000 100644
--- a/examples/pybullet/examples/projective_texture.py
+++ b/examples/pybullet/examples/projective_texture.py
@@ -5,31 +5,35 @@ import numpy as np
 
 physicsClient = p.connect(p.GUI)
 
-p.setGravity(0,0,0)
-bearStartPos1 = [-3.3,0,0]
-bearStartOrientation1 = p.getQuaternionFromEuler([0,0,0])
+p.setGravity(0, 0, 0)
+bearStartPos1 = [-3.3, 0, 0]
+bearStartOrientation1 = p.getQuaternionFromEuler([0, 0, 0])
 bearId1 = p.loadURDF("plane.urdf", bearStartPos1, bearStartOrientation1)
-bearStartPos2 = [0,0,0]
-bearStartOrientation2 = p.getQuaternionFromEuler([0,0,0])
-bearId2 = p.loadURDF("teddy_large.urdf",bearStartPos2, bearStartOrientation2)
+bearStartPos2 = [0, 0, 0]
+bearStartOrientation2 = p.getQuaternionFromEuler([0, 0, 0])
+bearId2 = p.loadURDF("teddy_large.urdf", bearStartPos2, bearStartOrientation2)
 textureId = p.loadTexture("checker_grid.jpg")
 #p.changeVisualShape(objectUniqueId=0, linkIndex=-1, textureUniqueId=textureId)
 #p.changeVisualShape(objectUniqueId=1, linkIndex=-1, textureUniqueId=textureId)
 
-
 useRealTimeSimulation = 1
 
 if (useRealTimeSimulation):
-	p.setRealTimeSimulation(1)
+  p.setRealTimeSimulation(1)
 
 while 1:
-	if (useRealTimeSimulation):
-		camera = p.getDebugVisualizerCamera()
-		viewMat = camera[2]
-		projMat = camera[3]
-		#An example of setting the view matrix for the projective texture.
-		#viewMat = p.computeViewMatrix(cameraEyePosition=[7,0,0], cameraTargetPosition=[0,0,0], cameraUpVector=[0,0,1])
-		p.getCameraImage(300, 300, renderer=p.ER_BULLET_HARDWARE_OPENGL, flags=p.ER_USE_PROJECTIVE_TEXTURE, projectiveTextureView=viewMat, projectiveTextureProj=projMat)
-		p.setGravity(0,0,0)
-	else:
-		p.stepSimulation()
+  if (useRealTimeSimulation):
+    camera = p.getDebugVisualizerCamera()
+    viewMat = camera[2]
+    projMat = camera[3]
+    #An example of setting the view matrix for the projective texture.
+    #viewMat = p.computeViewMatrix(cameraEyePosition=[7,0,0], cameraTargetPosition=[0,0,0], cameraUpVector=[0,0,1])
+    p.getCameraImage(300,
+                     300,
+                     renderer=p.ER_BULLET_HARDWARE_OPENGL,
+                     flags=p.ER_USE_PROJECTIVE_TEXTURE,
+                     projectiveTextureView=viewMat,
+                     projectiveTextureProj=projMat)
+    p.setGravity(0, 0, 0)
+  else:
+    p.stepSimulation()
diff --git a/examples/pybullet/examples/quadruped.py b/examples/pybullet/examples/quadruped.py
index c325ff22b..a9352fd08 100644
--- a/examples/pybullet/examples/quadruped.py
+++ b/examples/pybullet/examples/quadruped.py
@@ -4,43 +4,102 @@ import math
 
 
 def drawInertiaBox(parentUid, parentLinkIndex, color):
-	dyn = p.getDynamicsInfo(parentUid, parentLinkIndex)
-	mass=dyn[0]
-	frictionCoeff=dyn[1]
-	inertia = dyn[2]
-	if (mass>0):
-		Ixx = inertia[0]
-		Iyy = inertia[1]
-		Izz = inertia[2]
-		boxScaleX = 0.5*math.sqrt(6*(Izz + Iyy - Ixx) / mass);
-		boxScaleY = 0.5*math.sqrt(6*(Izz + Ixx - Iyy) / mass);
-		boxScaleZ = 0.5*math.sqrt(6*(Ixx + Iyy - Izz) / mass);
-  
-		halfExtents = [boxScaleX,boxScaleY,boxScaleZ]
-		pts = [[halfExtents[0],halfExtents[1],halfExtents[2]],
-				 [-halfExtents[0],halfExtents[1],halfExtents[2]],
-				 [halfExtents[0],-halfExtents[1],halfExtents[2]],
-				 [-halfExtents[0],-halfExtents[1],halfExtents[2]],
-				 [halfExtents[0],halfExtents[1],-halfExtents[2]],
-				 [-halfExtents[0],halfExtents[1],-halfExtents[2]],
-				 [halfExtents[0],-halfExtents[1],-halfExtents[2]],
-				 [-halfExtents[0],-halfExtents[1],-halfExtents[2]]]
-	
-		
-		p.addUserDebugLine(pts[0],pts[1],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-		p.addUserDebugLine(pts[1],pts[3],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-		p.addUserDebugLine(pts[3],pts[2],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-		p.addUserDebugLine(pts[2],pts[0],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-	
-		p.addUserDebugLine(pts[0],pts[4],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-		p.addUserDebugLine(pts[1],pts[5],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-		p.addUserDebugLine(pts[2],pts[6],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-		p.addUserDebugLine(pts[3],pts[7],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-	
-		p.addUserDebugLine(pts[4+0],pts[4+1],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-		p.addUserDebugLine(pts[4+1],pts[4+3],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-		p.addUserDebugLine(pts[4+3],pts[4+2],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-		p.addUserDebugLine(pts[4+2],pts[4+0],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
+  dyn = p.getDynamicsInfo(parentUid, parentLinkIndex)
+  mass = dyn[0]
+  frictionCoeff = dyn[1]
+  inertia = dyn[2]
+  if (mass > 0):
+    Ixx = inertia[0]
+    Iyy = inertia[1]
+    Izz = inertia[2]
+    boxScaleX = 0.5 * math.sqrt(6 * (Izz + Iyy - Ixx) / mass)
+    boxScaleY = 0.5 * math.sqrt(6 * (Izz + Ixx - Iyy) / mass)
+    boxScaleZ = 0.5 * math.sqrt(6 * (Ixx + Iyy - Izz) / mass)
+
+    halfExtents = [boxScaleX, boxScaleY, boxScaleZ]
+    pts = [[halfExtents[0], halfExtents[1], halfExtents[2]],
+           [-halfExtents[0], halfExtents[1], halfExtents[2]],
+           [halfExtents[0], -halfExtents[1], halfExtents[2]],
+           [-halfExtents[0], -halfExtents[1], halfExtents[2]],
+           [halfExtents[0], halfExtents[1], -halfExtents[2]],
+           [-halfExtents[0], halfExtents[1], -halfExtents[2]],
+           [halfExtents[0], -halfExtents[1], -halfExtents[2]],
+           [-halfExtents[0], -halfExtents[1], -halfExtents[2]]]
+
+    p.addUserDebugLine(pts[0],
+                       pts[1],
+                       color,
+                       1,
+                       parentObjectUniqueId=parentUid,
+                       parentLinkIndex=parentLinkIndex)
+    p.addUserDebugLine(pts[1],
+                       pts[3],
+                       color,
+                       1,
+                       parentObjectUniqueId=parentUid,
+                       parentLinkIndex=parentLinkIndex)
+    p.addUserDebugLine(pts[3],
+                       pts[2],
+                       color,
+                       1,
+                       parentObjectUniqueId=parentUid,
+                       parentLinkIndex=parentLinkIndex)
+    p.addUserDebugLine(pts[2],
+                       pts[0],
+                       color,
+                       1,
+                       parentObjectUniqueId=parentUid,
+                       parentLinkIndex=parentLinkIndex)
+
+    p.addUserDebugLine(pts[0],
+                       pts[4],
+                       color,
+                       1,
+                       parentObjectUniqueId=parentUid,
+                       parentLinkIndex=parentLinkIndex)
+    p.addUserDebugLine(pts[1],
+                       pts[5],
+                       color,
+                       1,
+                       parentObjectUniqueId=parentUid,
+                       parentLinkIndex=parentLinkIndex)
+    p.addUserDebugLine(pts[2],
+                       pts[6],
+                       color,
+                       1,
+                       parentObjectUniqueId=parentUid,
+                       parentLinkIndex=parentLinkIndex)
+    p.addUserDebugLine(pts[3],
+                       pts[7],
+                       color,
+                       1,
+                       parentObjectUniqueId=parentUid,
+                       parentLinkIndex=parentLinkIndex)
+
+    p.addUserDebugLine(pts[4 + 0],
+                       pts[4 + 1],
+                       color,
+                       1,
+                       parentObjectUniqueId=parentUid,
+                       parentLinkIndex=parentLinkIndex)
+    p.addUserDebugLine(pts[4 + 1],
+                       pts[4 + 3],
+                       color,
+                       1,
+                       parentObjectUniqueId=parentUid,
+                       parentLinkIndex=parentLinkIndex)
+    p.addUserDebugLine(pts[4 + 3],
+                       pts[4 + 2],
+                       color,
+                       1,
+                       parentObjectUniqueId=parentUid,
+                       parentLinkIndex=parentLinkIndex)
+    p.addUserDebugLine(pts[4 + 2],
+                       pts[4 + 0],
+                       color,
+                       1,
+                       parentObjectUniqueId=parentUid,
+                       parentLinkIndex=parentLinkIndex)
 
 
 toeConstraint = True
@@ -48,12 +107,12 @@ useMaximalCoordinates = False
 useRealTime = 1
 
 #the fixedTimeStep and numSolverIterations are the most important parameters to trade-off quality versus performance
-fixedTimeStep = 1./100
+fixedTimeStep = 1. / 100
 numSolverIterations = 50
 
 if (useMaximalCoordinates):
-	fixedTimeStep = 1./500
-	numSolverIterations = 200
+  fixedTimeStep = 1. / 500
+  numSolverIterations = 200
 
 speed = 10
 amplitude = 0.8
@@ -64,32 +123,37 @@ kp = 1
 kd = .5
 maxKneeForce = 1000
 
-
 physId = p.connect(p.SHARED_MEMORY)
-if (physId<0):
-	p.connect(p.GUI)
+if (physId < 0):
+  p.connect(p.GUI)
 #p.resetSimulation()
 
-angle = 0 # pick in range 0..0.2 radians
-orn = p.getQuaternionFromEuler([0,angle,0])
-p.loadURDF("plane.urdf",[0,0,0],orn)
+angle = 0  # pick in range 0..0.2 radians
+orn = p.getQuaternionFromEuler([0, angle, 0])
+p.loadURDF("plane.urdf", [0, 0, 0], orn)
 p.setPhysicsEngineParameter(numSolverIterations=numSolverIterations)
-p.startStateLogging(p.STATE_LOGGING_GENERIC_ROBOT, "genericlogdata.bin", maxLogDof = 16, logFlags = p.STATE_LOG_JOINT_TORQUES)
+p.startStateLogging(p.STATE_LOGGING_GENERIC_ROBOT,
+                    "genericlogdata.bin",
+                    maxLogDof=16,
+                    logFlags=p.STATE_LOG_JOINT_TORQUES)
 p.setTimeOut(4000000)
 
-p.setGravity(0,0,0)
+p.setGravity(0, 0, 0)
 p.setTimeStep(fixedTimeStep)
 
-orn = p.getQuaternionFromEuler([0,0,0.4])
+orn = p.getQuaternionFromEuler([0, 0, 0.4])
 p.setRealTimeSimulation(0)
-quadruped = p.loadURDF("quadruped/minitaur_v1.urdf",[1,-1,.3],orn,useFixedBase=False, useMaximalCoordinates=useMaximalCoordinates, flags=p.URDF_USE_IMPLICIT_CYLINDER)
+quadruped = p.loadURDF("quadruped/minitaur_v1.urdf", [1, -1, .3],
+                       orn,
+                       useFixedBase=False,
+                       useMaximalCoordinates=useMaximalCoordinates,
+                       flags=p.URDF_USE_IMPLICIT_CYLINDER)
 nJoints = p.getNumJoints(quadruped)
 
 jointNameToId = {}
 for i in range(nJoints):
-	jointInfo = p.getJointInfo(quadruped, i)
-	jointNameToId[jointInfo[1].decode('UTF-8')] = jointInfo[0]
-
+  jointInfo = p.getJointInfo(quadruped, i)
+  jointNameToId[jointInfo[1].decode('UTF-8')] = jointInfo[0]
 
 motor_front_rightR_joint = jointNameToId['motor_front_rightR_joint']
 motor_front_rightL_joint = jointNameToId['motor_front_rightL_joint']
@@ -116,183 +180,286 @@ motor_back_leftL_joint = jointNameToId['motor_back_leftL_joint']
 motor_back_leftL_link = jointNameToId['motor_back_leftL_link']
 knee_back_leftL_link = jointNameToId['knee_back_leftL_link']
 
-
-
-
 #fixtorso = p.createConstraint(-1,-1,quadruped,-1,p.JOINT_FIXED,[0,0,0],[0,0,0],[0,0,0])
 
-motordir=[-1,-1,-1,-1,1,1,1,1]
+motordir = [-1, -1, -1, -1, 1, 1, 1, 1]
 halfpi = 1.57079632679
-twopi = 4*halfpi
+twopi = 4 * halfpi
 kneeangle = -2.1834
 
-dyn = p.getDynamicsInfo(quadruped,-1)
-mass=dyn[0]
-friction=dyn[1]
+dyn = p.getDynamicsInfo(quadruped, -1)
+mass = dyn[0]
+friction = dyn[1]
 localInertiaDiagonal = dyn[2]
 
-print("localInertiaDiagonal",localInertiaDiagonal)
+print("localInertiaDiagonal", localInertiaDiagonal)
 
 #this is a no-op, just to show the API
-p.changeDynamics(quadruped,-1,localInertiaDiagonal=localInertiaDiagonal)
+p.changeDynamics(quadruped, -1, localInertiaDiagonal=localInertiaDiagonal)
 
 #for i in range (nJoints):
 #	p.changeDynamics(quadruped,i,localInertiaDiagonal=[0.000001,0.000001,0.000001])
 
-
-drawInertiaBox(quadruped,-1, [1,0,0])
+drawInertiaBox(quadruped, -1, [1, 0, 0])
 #drawInertiaBox(quadruped,motor_front_rightR_joint, [1,0,0])
 
-for i in range (nJoints):
-	drawInertiaBox(quadruped,i, [0,1,0])
-
+for i in range(nJoints):
+  drawInertiaBox(quadruped, i, [0, 1, 0])
 
 if (useMaximalCoordinates):
-	steps = 400
-	for aa in range (steps):
-		p.setJointMotorControl2(quadruped,motor_front_leftL_joint,p.POSITION_CONTROL,motordir[0]*halfpi*float(aa)/steps)
-		p.setJointMotorControl2(quadruped,motor_front_leftR_joint,p.POSITION_CONTROL,motordir[1]*halfpi*float(aa)/steps)
-		p.setJointMotorControl2(quadruped,motor_back_leftL_joint,p.POSITION_CONTROL,motordir[2]*halfpi*float(aa)/steps)
-		p.setJointMotorControl2(quadruped,motor_back_leftR_joint,p.POSITION_CONTROL,motordir[3]*halfpi*float(aa)/steps)
-		p.setJointMotorControl2(quadruped,motor_front_rightL_joint,p.POSITION_CONTROL,motordir[4]*halfpi*float(aa)/steps)
-		p.setJointMotorControl2(quadruped,motor_front_rightR_joint,p.POSITION_CONTROL,motordir[5]*halfpi*float(aa)/steps)
-		p.setJointMotorControl2(quadruped,motor_back_rightL_joint,p.POSITION_CONTROL,motordir[6]*halfpi*float(aa)/steps)
-		p.setJointMotorControl2(quadruped,motor_back_rightR_joint,p.POSITION_CONTROL,motordir[7]*halfpi*float(aa)/steps)
-		
-		p.setJointMotorControl2(quadruped,knee_front_leftL_link,p.POSITION_CONTROL,motordir[0]*(kneeangle+twopi)*float(aa)/steps)
-		p.setJointMotorControl2(quadruped,knee_front_leftR_link,p.POSITION_CONTROL,motordir[1]*kneeangle*float(aa)/steps)
-		p.setJointMotorControl2(quadruped,knee_back_leftL_link,p.POSITION_CONTROL,motordir[2]*kneeangle*float(aa)/steps)
-		p.setJointMotorControl2(quadruped,knee_back_leftR_link,p.POSITION_CONTROL,motordir[3]*(kneeangle+twopi)*float(aa)/steps)
-		p.setJointMotorControl2(quadruped,knee_front_rightL_link,p.POSITION_CONTROL,motordir[4]*(kneeangle)*float(aa)/steps)
-		p.setJointMotorControl2(quadruped,knee_front_rightR_link,p.POSITION_CONTROL,motordir[5]*(kneeangle+twopi)*float(aa)/steps)
-		p.setJointMotorControl2(quadruped,knee_back_rightL_link,p.POSITION_CONTROL,motordir[6]*(kneeangle+twopi)*float(aa)/steps)
-		p.setJointMotorControl2(quadruped,knee_back_rightR_link,p.POSITION_CONTROL,motordir[7]*kneeangle*float(aa)/steps)
-				
-		p.stepSimulation()
-		#time.sleep(fixedTimeStep)
+  steps = 400
+  for aa in range(steps):
+    p.setJointMotorControl2(quadruped, motor_front_leftL_joint, p.POSITION_CONTROL,
+                            motordir[0] * halfpi * float(aa) / steps)
+    p.setJointMotorControl2(quadruped, motor_front_leftR_joint, p.POSITION_CONTROL,
+                            motordir[1] * halfpi * float(aa) / steps)
+    p.setJointMotorControl2(quadruped, motor_back_leftL_joint, p.POSITION_CONTROL,
+                            motordir[2] * halfpi * float(aa) / steps)
+    p.setJointMotorControl2(quadruped, motor_back_leftR_joint, p.POSITION_CONTROL,
+                            motordir[3] * halfpi * float(aa) / steps)
+    p.setJointMotorControl2(quadruped, motor_front_rightL_joint, p.POSITION_CONTROL,
+                            motordir[4] * halfpi * float(aa) / steps)
+    p.setJointMotorControl2(quadruped, motor_front_rightR_joint, p.POSITION_CONTROL,
+                            motordir[5] * halfpi * float(aa) / steps)
+    p.setJointMotorControl2(quadruped, motor_back_rightL_joint, p.POSITION_CONTROL,
+                            motordir[6] * halfpi * float(aa) / steps)
+    p.setJointMotorControl2(quadruped, motor_back_rightR_joint, p.POSITION_CONTROL,
+                            motordir[7] * halfpi * float(aa) / steps)
+
+    p.setJointMotorControl2(quadruped, knee_front_leftL_link, p.POSITION_CONTROL,
+                            motordir[0] * (kneeangle + twopi) * float(aa) / steps)
+    p.setJointMotorControl2(quadruped, knee_front_leftR_link, p.POSITION_CONTROL,
+                            motordir[1] * kneeangle * float(aa) / steps)
+    p.setJointMotorControl2(quadruped, knee_back_leftL_link, p.POSITION_CONTROL,
+                            motordir[2] * kneeangle * float(aa) / steps)
+    p.setJointMotorControl2(quadruped, knee_back_leftR_link, p.POSITION_CONTROL,
+                            motordir[3] * (kneeangle + twopi) * float(aa) / steps)
+    p.setJointMotorControl2(quadruped, knee_front_rightL_link, p.POSITION_CONTROL,
+                            motordir[4] * (kneeangle) * float(aa) / steps)
+    p.setJointMotorControl2(quadruped, knee_front_rightR_link, p.POSITION_CONTROL,
+                            motordir[5] * (kneeangle + twopi) * float(aa) / steps)
+    p.setJointMotorControl2(quadruped, knee_back_rightL_link, p.POSITION_CONTROL,
+                            motordir[6] * (kneeangle + twopi) * float(aa) / steps)
+    p.setJointMotorControl2(quadruped, knee_back_rightR_link, p.POSITION_CONTROL,
+                            motordir[7] * kneeangle * float(aa) / steps)
+
+    p.stepSimulation()
+    #time.sleep(fixedTimeStep)
 else:
-	
-	p.resetJointState(quadruped,motor_front_leftL_joint,motordir[0]*halfpi)
-	p.resetJointState(quadruped,knee_front_leftL_link,motordir[0]*kneeangle)
-	p.resetJointState(quadruped,motor_front_leftR_joint,motordir[1]*halfpi)
-	p.resetJointState(quadruped,knee_front_leftR_link,motordir[1]*kneeangle)
-		
-	p.resetJointState(quadruped,motor_back_leftL_joint,motordir[2]*halfpi)
-	p.resetJointState(quadruped,knee_back_leftL_link,motordir[2]*kneeangle)
-	p.resetJointState(quadruped,motor_back_leftR_joint,motordir[3]*halfpi)
-	p.resetJointState(quadruped,knee_back_leftR_link,motordir[3]*kneeangle)
-	
-	p.resetJointState(quadruped,motor_front_rightL_joint,motordir[4]*halfpi)
-	p.resetJointState(quadruped,knee_front_rightL_link,motordir[4]*kneeangle)
-	p.resetJointState(quadruped,motor_front_rightR_joint,motordir[5]*halfpi)
-	p.resetJointState(quadruped,knee_front_rightR_link,motordir[5]*kneeangle)
-	
-	p.resetJointState(quadruped,motor_back_rightL_joint,motordir[6]*halfpi)
-	p.resetJointState(quadruped,knee_back_rightL_link,motordir[6]*kneeangle)
-	p.resetJointState(quadruped,motor_back_rightR_joint,motordir[7]*halfpi)
-	p.resetJointState(quadruped,knee_back_rightR_link,motordir[7]*kneeangle)
 
-#p.getNumJoints(1)
+  p.resetJointState(quadruped, motor_front_leftL_joint, motordir[0] * halfpi)
+  p.resetJointState(quadruped, knee_front_leftL_link, motordir[0] * kneeangle)
+  p.resetJointState(quadruped, motor_front_leftR_joint, motordir[1] * halfpi)
+  p.resetJointState(quadruped, knee_front_leftR_link, motordir[1] * kneeangle)
+
+  p.resetJointState(quadruped, motor_back_leftL_joint, motordir[2] * halfpi)
+  p.resetJointState(quadruped, knee_back_leftL_link, motordir[2] * kneeangle)
+  p.resetJointState(quadruped, motor_back_leftR_joint, motordir[3] * halfpi)
+  p.resetJointState(quadruped, knee_back_leftR_link, motordir[3] * kneeangle)
+
+  p.resetJointState(quadruped, motor_front_rightL_joint, motordir[4] * halfpi)
+  p.resetJointState(quadruped, knee_front_rightL_link, motordir[4] * kneeangle)
+  p.resetJointState(quadruped, motor_front_rightR_joint, motordir[5] * halfpi)
+  p.resetJointState(quadruped, knee_front_rightR_link, motordir[5] * kneeangle)
 
+  p.resetJointState(quadruped, motor_back_rightL_joint, motordir[6] * halfpi)
+  p.resetJointState(quadruped, knee_back_rightL_link, motordir[6] * kneeangle)
+  p.resetJointState(quadruped, motor_back_rightR_joint, motordir[7] * halfpi)
+  p.resetJointState(quadruped, knee_back_rightR_link, motordir[7] * kneeangle)
+
+#p.getNumJoints(1)
 
 if (toeConstraint):
-	cid = p.createConstraint(quadruped,knee_front_leftR_link,quadruped,knee_front_leftL_link,p.JOINT_POINT2POINT,[0,0,0],[0,0.005,0.1],[0,0.01,0.1])
-	p.changeConstraint(cid,maxForce=maxKneeForce)
-	cid = p.createConstraint(quadruped,knee_front_rightR_link,quadruped,knee_front_rightL_link,p.JOINT_POINT2POINT,[0,0,0],[0,0.005,0.1],[0,0.01,0.1])
-	p.changeConstraint(cid,maxForce=maxKneeForce)
-	cid = p.createConstraint(quadruped,knee_back_leftR_link,quadruped,knee_back_leftL_link,p.JOINT_POINT2POINT,[0,0,0],[0,0.005,0.1],[0,0.01,0.1])
-	p.changeConstraint(cid,maxForce=maxKneeForce)
-	cid = p.createConstraint(quadruped,knee_back_rightR_link,quadruped,knee_back_rightL_link,p.JOINT_POINT2POINT,[0,0,0],[0,0.005,0.1],[0,0.01,0.1])
-	p.changeConstraint(cid,maxForce=maxKneeForce)
-	
+  cid = p.createConstraint(quadruped, knee_front_leftR_link, quadruped, knee_front_leftL_link,
+                           p.JOINT_POINT2POINT, [0, 0, 0], [0, 0.005, 0.1], [0, 0.01, 0.1])
+  p.changeConstraint(cid, maxForce=maxKneeForce)
+  cid = p.createConstraint(quadruped, knee_front_rightR_link, quadruped, knee_front_rightL_link,
+                           p.JOINT_POINT2POINT, [0, 0, 0], [0, 0.005, 0.1], [0, 0.01, 0.1])
+  p.changeConstraint(cid, maxForce=maxKneeForce)
+  cid = p.createConstraint(quadruped, knee_back_leftR_link, quadruped, knee_back_leftL_link,
+                           p.JOINT_POINT2POINT, [0, 0, 0], [0, 0.005, 0.1], [0, 0.01, 0.1])
+  p.changeConstraint(cid, maxForce=maxKneeForce)
+  cid = p.createConstraint(quadruped, knee_back_rightR_link, quadruped, knee_back_rightL_link,
+                           p.JOINT_POINT2POINT, [0, 0, 0], [0, 0.005, 0.1], [0, 0.01, 0.1])
+  p.changeConstraint(cid, maxForce=maxKneeForce)
+
 if (1):
-	p.setJointMotorControl(quadruped,knee_front_leftL_link,p.VELOCITY_CONTROL,0,kneeFrictionForce)
-	p.setJointMotorControl(quadruped,knee_front_leftR_link,p.VELOCITY_CONTROL,0,kneeFrictionForce)
-	p.setJointMotorControl(quadruped,knee_front_rightL_link,p.VELOCITY_CONTROL,0,kneeFrictionForce)
-	p.setJointMotorControl(quadruped,knee_front_rightR_link,p.VELOCITY_CONTROL,0,kneeFrictionForce)
-	p.setJointMotorControl(quadruped,knee_back_leftL_link,p.VELOCITY_CONTROL,0,kneeFrictionForce)
-	p.setJointMotorControl(quadruped,knee_back_leftR_link,p.VELOCITY_CONTROL,0,kneeFrictionForce)
-	p.setJointMotorControl(quadruped,knee_back_leftL_link,p.VELOCITY_CONTROL,0,kneeFrictionForce)
-	p.setJointMotorControl(quadruped,knee_back_leftR_link,p.VELOCITY_CONTROL,0,kneeFrictionForce)
-	p.setJointMotorControl(quadruped,knee_back_rightL_link,p.VELOCITY_CONTROL,0,kneeFrictionForce)
-	p.setJointMotorControl(quadruped,knee_back_rightR_link,p.VELOCITY_CONTROL,0,kneeFrictionForce)
-
-p.setGravity(0,0,-10)
-
-
-
-legnumbering=[
-	motor_front_leftL_joint,
-	motor_front_leftR_joint,
-	motor_back_leftL_joint,
-	motor_back_leftR_joint,
-	motor_front_rightL_joint,
-	motor_front_rightR_joint,
-	motor_back_rightL_joint,
-	motor_back_rightR_joint]
-
-for i in range (8):
-	print (legnumbering[i])
+  p.setJointMotorControl(quadruped, knee_front_leftL_link, p.VELOCITY_CONTROL, 0,
+                         kneeFrictionForce)
+  p.setJointMotorControl(quadruped, knee_front_leftR_link, p.VELOCITY_CONTROL, 0,
+                         kneeFrictionForce)
+  p.setJointMotorControl(quadruped, knee_front_rightL_link, p.VELOCITY_CONTROL, 0,
+                         kneeFrictionForce)
+  p.setJointMotorControl(quadruped, knee_front_rightR_link, p.VELOCITY_CONTROL, 0,
+                         kneeFrictionForce)
+  p.setJointMotorControl(quadruped, knee_back_leftL_link, p.VELOCITY_CONTROL, 0, kneeFrictionForce)
+  p.setJointMotorControl(quadruped, knee_back_leftR_link, p.VELOCITY_CONTROL, 0, kneeFrictionForce)
+  p.setJointMotorControl(quadruped, knee_back_leftL_link, p.VELOCITY_CONTROL, 0, kneeFrictionForce)
+  p.setJointMotorControl(quadruped, knee_back_leftR_link, p.VELOCITY_CONTROL, 0, kneeFrictionForce)
+  p.setJointMotorControl(quadruped, knee_back_rightL_link, p.VELOCITY_CONTROL, 0,
+                         kneeFrictionForce)
+  p.setJointMotorControl(quadruped, knee_back_rightR_link, p.VELOCITY_CONTROL, 0,
+                         kneeFrictionForce)
+
+p.setGravity(0, 0, -10)
+
+legnumbering = [
+    motor_front_leftL_joint, motor_front_leftR_joint, motor_back_leftL_joint,
+    motor_back_leftR_joint, motor_front_rightL_joint, motor_front_rightR_joint,
+    motor_back_rightL_joint, motor_back_rightR_joint
+]
+
+for i in range(8):
+  print(legnumbering[i])
 #use the Minitaur leg numbering
-p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[0],controlMode=p.POSITION_CONTROL,targetPosition=motordir[0]*1.57,positionGain=kp, velocityGain=kd, force=maxForce)
-p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[1],controlMode=p.POSITION_CONTROL,targetPosition=motordir[1]*1.57,positionGain=kp, velocityGain=kd, force=maxForce)
-p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[2],controlMode=p.POSITION_CONTROL,targetPosition=motordir[2]*1.57,positionGain=kp, velocityGain=kd, force=maxForce)
-p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[3],controlMode=p.POSITION_CONTROL,targetPosition=motordir[3]*1.57,positionGain=kp, velocityGain=kd, force=maxForce)
-p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[4],controlMode=p.POSITION_CONTROL,targetPosition=motordir[4]*1.57,positionGain=kp, velocityGain=kd, force=maxForce)
-p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[5],controlMode=p.POSITION_CONTROL,targetPosition=motordir[5]*1.57,positionGain=kp, velocityGain=kd, force=maxForce)
-p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[6],controlMode=p.POSITION_CONTROL,targetPosition=motordir[6]*1.57,positionGain=kp, velocityGain=kd, force=maxForce)
-p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[7],controlMode=p.POSITION_CONTROL,targetPosition=motordir[7]*1.57,positionGain=kp, velocityGain=kd, force=maxForce)
+p.setJointMotorControl2(bodyIndex=quadruped,
+                        jointIndex=legnumbering[0],
+                        controlMode=p.POSITION_CONTROL,
+                        targetPosition=motordir[0] * 1.57,
+                        positionGain=kp,
+                        velocityGain=kd,
+                        force=maxForce)
+p.setJointMotorControl2(bodyIndex=quadruped,
+                        jointIndex=legnumbering[1],
+                        controlMode=p.POSITION_CONTROL,
+                        targetPosition=motordir[1] * 1.57,
+                        positionGain=kp,
+                        velocityGain=kd,
+                        force=maxForce)
+p.setJointMotorControl2(bodyIndex=quadruped,
+                        jointIndex=legnumbering[2],
+                        controlMode=p.POSITION_CONTROL,
+                        targetPosition=motordir[2] * 1.57,
+                        positionGain=kp,
+                        velocityGain=kd,
+                        force=maxForce)
+p.setJointMotorControl2(bodyIndex=quadruped,
+                        jointIndex=legnumbering[3],
+                        controlMode=p.POSITION_CONTROL,
+                        targetPosition=motordir[3] * 1.57,
+                        positionGain=kp,
+                        velocityGain=kd,
+                        force=maxForce)
+p.setJointMotorControl2(bodyIndex=quadruped,
+                        jointIndex=legnumbering[4],
+                        controlMode=p.POSITION_CONTROL,
+                        targetPosition=motordir[4] * 1.57,
+                        positionGain=kp,
+                        velocityGain=kd,
+                        force=maxForce)
+p.setJointMotorControl2(bodyIndex=quadruped,
+                        jointIndex=legnumbering[5],
+                        controlMode=p.POSITION_CONTROL,
+                        targetPosition=motordir[5] * 1.57,
+                        positionGain=kp,
+                        velocityGain=kd,
+                        force=maxForce)
+p.setJointMotorControl2(bodyIndex=quadruped,
+                        jointIndex=legnumbering[6],
+                        controlMode=p.POSITION_CONTROL,
+                        targetPosition=motordir[6] * 1.57,
+                        positionGain=kp,
+                        velocityGain=kd,
+                        force=maxForce)
+p.setJointMotorControl2(bodyIndex=quadruped,
+                        jointIndex=legnumbering[7],
+                        controlMode=p.POSITION_CONTROL,
+                        targetPosition=motordir[7] * 1.57,
+                        positionGain=kp,
+                        velocityGain=kd,
+                        force=maxForce)
 #stand still
 p.setRealTimeSimulation(useRealTime)
 
-
 t = 0.0
 t_end = t + 15
 ref_time = time.time()
-while (t<t_end):
-	p.setGravity(0,0,-10)
-	if (useRealTime):
-		t = time.time()-ref_time
-	else:
-		t = t+fixedTimeStep
-	if (useRealTime==0):
-		p.stepSimulation()
-		time.sleep(fixedTimeStep)
+while (t < t_end):
+  p.setGravity(0, 0, -10)
+  if (useRealTime):
+    t = time.time() - ref_time
+  else:
+    t = t + fixedTimeStep
+  if (useRealTime == 0):
+    p.stepSimulation()
+    time.sleep(fixedTimeStep)
 
 print("quadruped Id = ")
 print(quadruped)
 p.saveWorld("quadru.py")
-logId = p.startStateLogging(p.STATE_LOGGING_MINITAUR,"quadrupedLog.bin",[quadruped])
-
-
-
-
-
+logId = p.startStateLogging(p.STATE_LOGGING_MINITAUR, "quadrupedLog.bin", [quadruped])
 
 #jump
 t = 0.0
 t_end = t + 100
-i=0
+i = 0
 ref_time = time.time()
 
 while (1):
-	if (useRealTime):
-		t = time.time()-ref_time
-	else:
-		t = t+fixedTimeStep
-	if (True):
-	
-		target = math.sin(t*speed)*jump_amp+1.57;
-		p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[0],controlMode=p.POSITION_CONTROL,targetPosition=motordir[0]*target,positionGain=kp, velocityGain=kd, force=maxForce)
-		p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[1],controlMode=p.POSITION_CONTROL,targetPosition=motordir[1]*target,positionGain=kp, velocityGain=kd, force=maxForce)
-		p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[2],controlMode=p.POSITION_CONTROL,targetPosition=motordir[2]*target,positionGain=kp, velocityGain=kd, force=maxForce)
-		p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[3],controlMode=p.POSITION_CONTROL,targetPosition=motordir[3]*target,positionGain=kp, velocityGain=kd, force=maxForce)
-		p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[4],controlMode=p.POSITION_CONTROL,targetPosition=motordir[4]*target,positionGain=kp, velocityGain=kd, force=maxForce)
-		p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[5],controlMode=p.POSITION_CONTROL,targetPosition=motordir[5]*target,positionGain=kp, velocityGain=kd, force=maxForce)
-		p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[6],controlMode=p.POSITION_CONTROL,targetPosition=motordir[6]*target,positionGain=kp, velocityGain=kd, force=maxForce)
-		p.setJointMotorControl2(bodyIndex=quadruped,jointIndex=legnumbering[7],controlMode=p.POSITION_CONTROL,targetPosition=motordir[7]*target,positionGain=kp, velocityGain=kd, force=maxForce)
-		
-	if (useRealTime==0):	
-		p.stepSimulation()
-		time.sleep(fixedTimeStep)
-
+  if (useRealTime):
+    t = time.time() - ref_time
+  else:
+    t = t + fixedTimeStep
+  if (True):
+
+    target = math.sin(t * speed) * jump_amp + 1.57
+    p.setJointMotorControl2(bodyIndex=quadruped,
+                            jointIndex=legnumbering[0],
+                            controlMode=p.POSITION_CONTROL,
+                            targetPosition=motordir[0] * target,
+                            positionGain=kp,
+                            velocityGain=kd,
+                            force=maxForce)
+    p.setJointMotorControl2(bodyIndex=quadruped,
+                            jointIndex=legnumbering[1],
+                            controlMode=p.POSITION_CONTROL,
+                            targetPosition=motordir[1] * target,
+                            positionGain=kp,
+                            velocityGain=kd,
+                            force=maxForce)
+    p.setJointMotorControl2(bodyIndex=quadruped,
+                            jointIndex=legnumbering[2],
+                            controlMode=p.POSITION_CONTROL,
+                            targetPosition=motordir[2] * target,
+                            positionGain=kp,
+                            velocityGain=kd,
+                            force=maxForce)
+    p.setJointMotorControl2(bodyIndex=quadruped,
+                            jointIndex=legnumbering[3],
+                            controlMode=p.POSITION_CONTROL,
+                            targetPosition=motordir[3] * target,
+                            positionGain=kp,
+                            velocityGain=kd,
+                            force=maxForce)
+    p.setJointMotorControl2(bodyIndex=quadruped,
+                            jointIndex=legnumbering[4],
+                            controlMode=p.POSITION_CONTROL,
+                            targetPosition=motordir[4] * target,
+                            positionGain=kp,
+                            velocityGain=kd,
+                            force=maxForce)
+    p.setJointMotorControl2(bodyIndex=quadruped,
+                            jointIndex=legnumbering[5],
+                            controlMode=p.POSITION_CONTROL,
+                            targetPosition=motordir[5] * target,
+                            positionGain=kp,
+                            velocityGain=kd,
+                            force=maxForce)
+    p.setJointMotorControl2(bodyIndex=quadruped,
+                            jointIndex=legnumbering[6],
+                            controlMode=p.POSITION_CONTROL,
+                            targetPosition=motordir[6] * target,
+                            positionGain=kp,
+                            velocityGain=kd,
+                            force=maxForce)
+    p.setJointMotorControl2(bodyIndex=quadruped,
+                            jointIndex=legnumbering[7],
+                            controlMode=p.POSITION_CONTROL,
+                            targetPosition=motordir[7] * target,
+                            positionGain=kp,
+                            velocityGain=kd,
+                            force=maxForce)
+
+  if (useRealTime == 0):
+    p.stepSimulation()
+    time.sleep(fixedTimeStep)
diff --git a/examples/pybullet/examples/quadruped_playback.py b/examples/pybullet/examples/quadruped_playback.py
index 7aaefda2e..544009745 100644
--- a/examples/pybullet/examples/quadruped_playback.py
+++ b/examples/pybullet/examples/quadruped_playback.py
@@ -10,15 +10,16 @@ import os, fnmatch
 import argparse
 from time import sleep
 
-def readLogFile(filename, verbose = True):
+
+def readLogFile(filename, verbose=True):
   f = open(filename, 'rb')
-  
+
   print('Opened'),
   print(filename)
 
   keys = f.readline().decode('utf8').rstrip('\n').split(',')
   fmt = f.readline().decode('utf8').rstrip('\n')
-  
+
   # The byte number of one record
   sz = struct.calcsize(fmt)
   # The type number of one record
@@ -38,9 +39,9 @@ def readLogFile(filename, verbose = True):
   wholeFile = f.read()
   # split by alignment word
   chunks = wholeFile.split(b'\xaa\xbb')
-  print ("num chunks")
-  print (len(chunks))
-  
+  print("num chunks")
+  print(len(chunks))
+
   log = list()
   for chunk in chunks:
     if len(chunk) == sz:
@@ -52,9 +53,10 @@ def readLogFile(filename, verbose = True):
 
   return log
 
+
 clid = p.connect(p.SHARED_MEMORY)
 
-log = readLogFile("LOG00076.TXT");
+log = readLogFile("LOG00076.TXT")
 
 recordNum = len(log)
 print('record num:'),
@@ -72,8 +74,6 @@ maxForce = 3.5
 kp = .05
 kd = .5
 
-
-
 quadruped = 1
 nJoints = p.getNumJoints(quadruped)
 jointNameToId = {}
@@ -106,21 +106,71 @@ motor_back_leftL_joint = jointNameToId['motor_back_leftL_joint']
 motor_back_leftL_link = jointNameToId['motor_back_leftL_link']
 knee_back_leftL_link = jointNameToId['knee_back_leftL_link']
 
-motorDir = [1, 1, 1, 1, 1, 1, 1, 1];
-legnumbering=[motor_front_leftR_joint,motor_front_leftL_joint,motor_back_leftR_joint,motor_back_leftL_joint,motor_front_rightR_joint,motor_front_rightL_joint,motor_back_rightR_joint,motor_back_rightL_joint]
-
+motorDir = [1, 1, 1, 1, 1, 1, 1, 1]
+legnumbering = [
+    motor_front_leftR_joint, motor_front_leftL_joint, motor_back_leftR_joint,
+    motor_back_leftL_joint, motor_front_rightR_joint, motor_front_rightL_joint,
+    motor_back_rightR_joint, motor_back_rightL_joint
+]
 
 for record in log:
-  p.setJointMotorControl2(bodyIndex=quadruped, jointIndex=legnumbering[0], controlMode=p.POSITION_CONTROL, targetPosition=motorDir[0]*record[7], positionGain=kp, velocityGain=kd, force=maxForce)
-  p.setJointMotorControl2(bodyIndex=quadruped, jointIndex=legnumbering[1], controlMode=p.POSITION_CONTROL, targetPosition=motorDir[1]*record[8], positionGain=kp, velocityGain=kd, force=maxForce)
-  p.setJointMotorControl2(bodyIndex=quadruped, jointIndex=legnumbering[2], controlMode=p.POSITION_CONTROL, targetPosition=motorDir[2]*record[9], positionGain=kp, velocityGain=kd, force=maxForce)
-  p.setJointMotorControl2(bodyIndex=quadruped, jointIndex=legnumbering[3], controlMode=p.POSITION_CONTROL, targetPosition=motorDir[3]*record[10], positionGain=kp, velocityGain=kd, force=maxForce)
-  p.setJointMotorControl2(bodyIndex=quadruped, jointIndex=legnumbering[4], controlMode=p.POSITION_CONTROL, targetPosition=motorDir[4]*record[11], positionGain=kp, velocityGain=kd, force=maxForce)
-  p.setJointMotorControl2(bodyIndex=quadruped, jointIndex=legnumbering[5], controlMode=p.POSITION_CONTROL, targetPosition=motorDir[5]*record[12], positionGain=kp, velocityGain=kd, force=maxForce)
-  p.setJointMotorControl2(bodyIndex=quadruped, jointIndex=legnumbering[6], controlMode=p.POSITION_CONTROL, targetPosition=motorDir[6]*record[13], positionGain=kp, velocityGain=kd, force=maxForce)
-  p.setJointMotorControl2(bodyIndex=quadruped, jointIndex=legnumbering[7], controlMode=p.POSITION_CONTROL, targetPosition=motorDir[7]*record[14], positionGain=kp, velocityGain=kd, force=maxForce)
-  p.setGravity(0.000000,0.000000,-10.000000)
+  p.setJointMotorControl2(bodyIndex=quadruped,
+                          jointIndex=legnumbering[0],
+                          controlMode=p.POSITION_CONTROL,
+                          targetPosition=motorDir[0] * record[7],
+                          positionGain=kp,
+                          velocityGain=kd,
+                          force=maxForce)
+  p.setJointMotorControl2(bodyIndex=quadruped,
+                          jointIndex=legnumbering[1],
+                          controlMode=p.POSITION_CONTROL,
+                          targetPosition=motorDir[1] * record[8],
+                          positionGain=kp,
+                          velocityGain=kd,
+                          force=maxForce)
+  p.setJointMotorControl2(bodyIndex=quadruped,
+                          jointIndex=legnumbering[2],
+                          controlMode=p.POSITION_CONTROL,
+                          targetPosition=motorDir[2] * record[9],
+                          positionGain=kp,
+                          velocityGain=kd,
+                          force=maxForce)
+  p.setJointMotorControl2(bodyIndex=quadruped,
+                          jointIndex=legnumbering[3],
+                          controlMode=p.POSITION_CONTROL,
+                          targetPosition=motorDir[3] * record[10],
+                          positionGain=kp,
+                          velocityGain=kd,
+                          force=maxForce)
+  p.setJointMotorControl2(bodyIndex=quadruped,
+                          jointIndex=legnumbering[4],
+                          controlMode=p.POSITION_CONTROL,
+                          targetPosition=motorDir[4] * record[11],
+                          positionGain=kp,
+                          velocityGain=kd,
+                          force=maxForce)
+  p.setJointMotorControl2(bodyIndex=quadruped,
+                          jointIndex=legnumbering[5],
+                          controlMode=p.POSITION_CONTROL,
+                          targetPosition=motorDir[5] * record[12],
+                          positionGain=kp,
+                          velocityGain=kd,
+                          force=maxForce)
+  p.setJointMotorControl2(bodyIndex=quadruped,
+                          jointIndex=legnumbering[6],
+                          controlMode=p.POSITION_CONTROL,
+                          targetPosition=motorDir[6] * record[13],
+                          positionGain=kp,
+                          velocityGain=kd,
+                          force=maxForce)
+  p.setJointMotorControl2(bodyIndex=quadruped,
+                          jointIndex=legnumbering[7],
+                          controlMode=p.POSITION_CONTROL,
+                          targetPosition=motorDir[7] * record[14],
+                          positionGain=kp,
+                          velocityGain=kd,
+                          force=maxForce)
+  p.setGravity(0.000000, 0.000000, -10.000000)
   p.stepSimulation()
   p.stepSimulation()
   sleep(0.01)
-  
\ No newline at end of file
diff --git a/examples/pybullet/examples/quadruped_setup_playback.py b/examples/pybullet/examples/quadruped_setup_playback.py
index a2b041888..5db18cb07 100644
--- a/examples/pybullet/examples/quadruped_setup_playback.py
+++ b/examples/pybullet/examples/quadruped_setup_playback.py
@@ -1,21 +1,43 @@
 import pybullet as p
 p.connect(p.SHARED_MEMORY)
-objects = [p.loadURDF("plane.urdf", 0.000000,0.000000,-.300000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("quadruped/minitaur.urdf", [-0.000046,-0.000068,0.200774],[-0.000701,0.000387,-0.000252,1.000000],useFixedBase=False)]
+objects = [
+    p.loadURDF("plane.urdf", 0.000000, 0.000000, -.300000, 0.000000, 0.000000, 0.000000, 1.000000)
+]
+objects = [
+    p.loadURDF("quadruped/minitaur.urdf", [-0.000046, -0.000068, 0.200774],
+               [-0.000701, 0.000387, -0.000252, 1.000000],
+               useFixedBase=False)
+]
 ob = objects[0]
-jointPositions=[ 0.000000, 1.531256, 0.000000, -2.240112, 1.527979, 0.000000, -2.240646, 1.533105, 0.000000, -2.238254, 1.530335, 0.000000, -2.238298, 0.000000, -1.528038, 0.000000, 2.242656, -1.525193, 0.000000, 2.244008, -1.530011, 0.000000, 2.240683, -1.528687, 0.000000, 2.240517 ]
-for ji in range (p.getNumJoints(ob)):
-	p.resetJointState(ob,ji,jointPositions[ji])
-	p.setJointMotorControl2(bodyIndex=ob, jointIndex=ji, controlMode=p.VELOCITY_CONTROL, force=0)
+jointPositions = [
+    0.000000, 1.531256, 0.000000, -2.240112, 1.527979, 0.000000, -2.240646, 1.533105, 0.000000,
+    -2.238254, 1.530335, 0.000000, -2.238298, 0.000000, -1.528038, 0.000000, 2.242656, -1.525193,
+    0.000000, 2.244008, -1.530011, 0.000000, 2.240683, -1.528687, 0.000000, 2.240517
+]
+for ji in range(p.getNumJoints(ob)):
+  p.resetJointState(ob, ji, jointPositions[ji])
+  p.setJointMotorControl2(bodyIndex=ob, jointIndex=ji, controlMode=p.VELOCITY_CONTROL, force=0)
 
-cid0 = p.createConstraint(1,3,1,6,p.JOINT_POINT2POINT,[0.000000,0.000000,0.000000],[0.000000,0.005000,0.200000],[0.000000,0.010000,0.200000],[0.000000,0.000000,0.000000,1.000000],[0.000000,0.000000,0.000000,1.000000])
-p.changeConstraint(cid0,maxForce=500.000000)
-cid1 = p.createConstraint(1,16,1,19,p.JOINT_POINT2POINT,[0.000000,0.000000,0.000000],[0.000000,0.005000,0.200000],[0.000000,0.010000,0.200000],[0.000000,0.000000,0.000000,1.000000],[0.000000,0.000000,0.000000,1.000000])
-p.changeConstraint(cid1,maxForce=500.000000)
-cid2 = p.createConstraint(1,9,1,12,p.JOINT_POINT2POINT,[0.000000,0.000000,0.000000],[0.000000,0.005000,0.200000],[0.000000,0.010000,0.200000],[0.000000,0.000000,0.000000,1.000000],[0.000000,0.000000,0.000000,1.000000])
-p.changeConstraint(cid2,maxForce=500.000000)
-cid3 = p.createConstraint(1,22,1,25,p.JOINT_POINT2POINT,[0.000000,0.000000,0.000000],[0.000000,0.005000,0.200000],[0.000000,0.010000,0.200000],[0.000000,0.000000,0.000000,1.000000],[0.000000,0.000000,0.000000,1.000000])
-p.changeConstraint(cid3,maxForce=500.000000)
-p.setGravity(0.000000,0.000000,0.000000)
+cid0 = p.createConstraint(1, 3, 1, 6, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000],
+                          [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000],
+                          [0.000000, 0.000000, 0.000000, 1.000000],
+                          [0.000000, 0.000000, 0.000000, 1.000000])
+p.changeConstraint(cid0, maxForce=500.000000)
+cid1 = p.createConstraint(1, 16, 1, 19, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000],
+                          [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000],
+                          [0.000000, 0.000000, 0.000000, 1.000000],
+                          [0.000000, 0.000000, 0.000000, 1.000000])
+p.changeConstraint(cid1, maxForce=500.000000)
+cid2 = p.createConstraint(1, 9, 1, 12, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000],
+                          [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000],
+                          [0.000000, 0.000000, 0.000000, 1.000000],
+                          [0.000000, 0.000000, 0.000000, 1.000000])
+p.changeConstraint(cid2, maxForce=500.000000)
+cid3 = p.createConstraint(1, 22, 1, 25, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000],
+                          [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000],
+                          [0.000000, 0.000000, 0.000000, 1.000000],
+                          [0.000000, 0.000000, 0.000000, 1.000000])
+p.changeConstraint(cid3, maxForce=500.000000)
+p.setGravity(0.000000, 0.000000, 0.000000)
 p.stepSimulation()
 p.disconnect()
diff --git a/examples/pybullet/examples/racecar.py b/examples/pybullet/examples/racecar.py
index 6489188b8..612959d8d 100644
--- a/examples/pybullet/examples/racecar.py
+++ b/examples/pybullet/examples/racecar.py
@@ -1,9 +1,9 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
-print ("current_dir=" + currentdir)
-parentdir = os.path.join(currentdir,"../gym")
+print("current_dir=" + currentdir)
+parentdir = os.path.join(currentdir, "../gym")
 
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import pybullet as p
 import pybullet_data
@@ -11,48 +11,52 @@ import pybullet_data
 import time
 
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	p.connect(p.GUI)
-	
+if (cid < 0):
+  p.connect(p.GUI)
+
 p.resetSimulation()
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 
 useRealTimeSim = 1
 
 #for video recording (works best on Mac and Linux, not well on Windows)
 #p.startStateLogging(p.STATE_LOGGING_VIDEO_MP4, "racecar.mp4")
-p.setRealTimeSimulation(useRealTimeSim) # either this
+p.setRealTimeSimulation(useRealTimeSim)  # either this
 #p.loadURDF("plane.urdf")
-p.loadSDF(os.path.join(pybullet_data.getDataPath(),"stadium.sdf"))
+p.loadSDF(os.path.join(pybullet_data.getDataPath(), "stadium.sdf"))
 
-car = p.loadURDF(os.path.join(pybullet_data.getDataPath(),"racecar/racecar.urdf"))
-for i in range (p.getNumJoints(car)):
-	print (p.getJointInfo(car,i))
+car = p.loadURDF(os.path.join(pybullet_data.getDataPath(), "racecar/racecar.urdf"))
+for i in range(p.getNumJoints(car)):
+  print(p.getJointInfo(car, i))
 
-inactive_wheels = [3,5,7]
+inactive_wheels = [3, 5, 7]
 wheels = [2]
 
 for wheel in inactive_wheels:
-		p.setJointMotorControl2(car,wheel,p.VELOCITY_CONTROL,targetVelocity=0,force=0)
-	
-steering = [4,6]
+  p.setJointMotorControl2(car, wheel, p.VELOCITY_CONTROL, targetVelocity=0, force=0)
+
+steering = [4, 6]
 
-targetVelocitySlider = p.addUserDebugParameter("wheelVelocity",-10,10,0)
-maxForceSlider = p.addUserDebugParameter("maxForce",0,10,10)
-steeringSlider = p.addUserDebugParameter("steering",-0.5,0.5,0)
+targetVelocitySlider = p.addUserDebugParameter("wheelVelocity", -10, 10, 0)
+maxForceSlider = p.addUserDebugParameter("maxForce", 0, 10, 10)
+steeringSlider = p.addUserDebugParameter("steering", -0.5, 0.5, 0)
 while (True):
-	maxForce = p.readUserDebugParameter(maxForceSlider)
-	targetVelocity = p.readUserDebugParameter(targetVelocitySlider)
-	steeringAngle = p.readUserDebugParameter(steeringSlider)
-	#print(targetVelocity)
-	
-	for wheel in wheels:
-		p.setJointMotorControl2(car,wheel,p.VELOCITY_CONTROL,targetVelocity=targetVelocity,force=maxForce)
-		
-	for steer in steering:
-		p.setJointMotorControl2(car,steer,p.POSITION_CONTROL,targetPosition=steeringAngle)
-		
-	steering
-	if (useRealTimeSim==0):
-		p.stepSimulation()
-	time.sleep(0.01)
+  maxForce = p.readUserDebugParameter(maxForceSlider)
+  targetVelocity = p.readUserDebugParameter(targetVelocitySlider)
+  steeringAngle = p.readUserDebugParameter(steeringSlider)
+  #print(targetVelocity)
+
+  for wheel in wheels:
+    p.setJointMotorControl2(car,
+                            wheel,
+                            p.VELOCITY_CONTROL,
+                            targetVelocity=targetVelocity,
+                            force=maxForce)
+
+  for steer in steering:
+    p.setJointMotorControl2(car, steer, p.POSITION_CONTROL, targetPosition=steeringAngle)
+
+  steering
+  if (useRealTimeSim == 0):
+    p.stepSimulation()
+  time.sleep(0.01)
diff --git a/examples/pybullet/examples/racecar_differential.py b/examples/pybullet/examples/racecar_differential.py
index 9abab35df..1967f3277 100644
--- a/examples/pybullet/examples/racecar_differential.py
+++ b/examples/pybullet/examples/racecar_differential.py
@@ -2,73 +2,131 @@ import pybullet as p
 import time
 
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	p.connect(p.GUI)
-	
+if (cid < 0):
+  p.connect(p.GUI)
+
 p.resetSimulation()
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 useRealTimeSim = 1
 
 #for video recording (works best on Mac and Linux, not well on Windows)
 #p.startStateLogging(p.STATE_LOGGING_VIDEO_MP4, "racecar.mp4")
-p.setRealTimeSimulation(useRealTimeSim) # either this
+p.setRealTimeSimulation(useRealTimeSim)  # either this
 p.loadURDF("plane.urdf")
 #p.loadSDF("stadium.sdf")
 
-car = p.loadURDF("racecar/racecar_differential.urdf") #, [0,0,2],useFixedBase=True)
-for i in range (p.getNumJoints(car)):
-	print (p.getJointInfo(car,i))
+car = p.loadURDF("racecar/racecar_differential.urdf")  #, [0,0,2],useFixedBase=True)
+for i in range(p.getNumJoints(car)):
+  print(p.getJointInfo(car, i))
 for wheel in range(p.getNumJoints(car)):
-		p.setJointMotorControl2(car,wheel,p.VELOCITY_CONTROL,targetVelocity=0,force=0)
-		p.getJointInfo(car,wheel)	
+  p.setJointMotorControl2(car, wheel, p.VELOCITY_CONTROL, targetVelocity=0, force=0)
+  p.getJointInfo(car, wheel)
 
-wheels = [8,15]
+wheels = [8, 15]
 print("----------------")
 
 #p.setJointMotorControl2(car,10,p.VELOCITY_CONTROL,targetVelocity=1,force=10)
-c = p.createConstraint(car,9,car,11,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=1, maxForce=10000)
+c = p.createConstraint(car,
+                       9,
+                       car,
+                       11,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=1, maxForce=10000)
+
+c = p.createConstraint(car,
+                       10,
+                       car,
+                       13,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=-1, maxForce=10000)
 
-c = p.createConstraint(car,10,car,13,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=-1, maxForce=10000)
+c = p.createConstraint(car,
+                       9,
+                       car,
+                       13,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=-1, maxForce=10000)
 
-c = p.createConstraint(car,9,car,13,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=-1, maxForce=10000)
+c = p.createConstraint(car,
+                       16,
+                       car,
+                       18,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=1, maxForce=10000)
 
-c = p.createConstraint(car,16,car,18,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=1, maxForce=10000)
+c = p.createConstraint(car,
+                       16,
+                       car,
+                       19,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=-1, maxForce=10000)
 
+c = p.createConstraint(car,
+                       17,
+                       car,
+                       19,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=-1, maxForce=10000)
 
-c = p.createConstraint(car,16,car,19,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=-1, maxForce=10000)
+c = p.createConstraint(car,
+                       1,
+                       car,
+                       18,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=-1, gearAuxLink=15, maxForce=10000)
+c = p.createConstraint(car,
+                       3,
+                       car,
+                       19,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=-1, gearAuxLink=15, maxForce=10000)
 
-c = p.createConstraint(car,17,car,19,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=-1, maxForce=10000)
+steering = [0, 2]
 
-c = p.createConstraint(car,1,car,18,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=-1, gearAuxLink = 15, maxForce=10000)
-c = p.createConstraint(car,3,car,19,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=-1, gearAuxLink = 15,maxForce=10000)
+targetVelocitySlider = p.addUserDebugParameter("wheelVelocity", -50, 50, 0)
+maxForceSlider = p.addUserDebugParameter("maxForce", 0, 50, 20)
+steeringSlider = p.addUserDebugParameter("steering", -1, 1, 0)
+while (True):
+  maxForce = p.readUserDebugParameter(maxForceSlider)
+  targetVelocity = p.readUserDebugParameter(targetVelocitySlider)
+  steeringAngle = p.readUserDebugParameter(steeringSlider)
+  #print(targetVelocity)
 
+  for wheel in wheels:
+    p.setJointMotorControl2(car,
+                            wheel,
+                            p.VELOCITY_CONTROL,
+                            targetVelocity=targetVelocity,
+                            force=maxForce)
 
-steering = [0,2]
+  for steer in steering:
+    p.setJointMotorControl2(car, steer, p.POSITION_CONTROL, targetPosition=-steeringAngle)
 
-targetVelocitySlider = p.addUserDebugParameter("wheelVelocity",-50,50,0)
-maxForceSlider = p.addUserDebugParameter("maxForce",0,50,20)
-steeringSlider = p.addUserDebugParameter("steering",-1,1,0)
-while (True):
-	maxForce = p.readUserDebugParameter(maxForceSlider)
-	targetVelocity = p.readUserDebugParameter(targetVelocitySlider)
-	steeringAngle = p.readUserDebugParameter(steeringSlider)
-	#print(targetVelocity)
-	
-	for wheel in wheels:
-		p.setJointMotorControl2(car,wheel,p.VELOCITY_CONTROL,targetVelocity=targetVelocity,force=maxForce)
-		
-	for steer in steering:
-		p.setJointMotorControl2(car,steer,p.POSITION_CONTROL,targetPosition=-steeringAngle)
-		
-	steering
-	if (useRealTimeSim==0):
-		p.stepSimulation()
-	time.sleep(0.01)
+  steering
+  if (useRealTimeSim == 0):
+    p.stepSimulation()
+  time.sleep(0.01)
diff --git a/examples/pybullet/examples/renderPlugin.py b/examples/pybullet/examples/renderPlugin.py
index 48dfc8aa6..1f7a1ebfb 100644
--- a/examples/pybullet/examples/renderPlugin.py
+++ b/examples/pybullet/examples/renderPlugin.py
@@ -1,9 +1,9 @@
-
 import pybullet as p
 p.connect(p.GUI)
-plugin = p.loadPlugin("d:/develop/bullet3/bin/pybullet_testplugin_vs2010_x64_debug.dll","_testPlugin")
-print("plugin=",plugin)
+plugin = p.loadPlugin("d:/develop/bullet3/bin/pybullet_testplugin_vs2010_x64_debug.dll",
+                      "_testPlugin")
+print("plugin=", plugin)
 p.loadURDF("r2d2.urdf")
 
 while (1):
-	p.getCameraImage(320,200)
\ No newline at end of file
+  p.getCameraImage(320, 200)
diff --git a/examples/pybullet/examples/rendertest.py b/examples/pybullet/examples/rendertest.py
index dacc4cb04..aa53b61a1 100644
--- a/examples/pybullet/examples/rendertest.py
+++ b/examples/pybullet/examples/rendertest.py
@@ -11,16 +11,16 @@ import numpy as np
 import pybullet

 from multiprocessing import Process

 

-camTargetPos = [0,0,0]

-cameraUp = [0,0,1]

-cameraPos = [1,1,1]

+camTargetPos = [0, 0, 0]

+cameraUp = [0, 0, 1]

+cameraPos = [1, 1, 1]

 

 pitch = -10.0

-roll=0

+roll = 0

 upAxisIndex = 2

 camDistance = 4

-pixelWidth = 84 # 320

-pixelHeight = 84 # 200

+pixelWidth = 84  # 320

+pixelHeight = 84  # 200

 nearPlane = 0.01

 farPlane = 100

 fov = 60

@@ -29,118 +29,122 @@ import matplotlib.pyplot as plt
 

 

 class BulletSim():

-    def __init__(self, connection_mode, *argv):

-        self.connection_mode = connection_mode

-        self.argv = argv

-

-    def __enter__(self):

-        print("connecting")

-        optionstring='--width={} --height={}'.format(pixelWidth,pixelHeight)

-        optionstring += ' --window_backend=2 --render_device=0'

-

-        print(self.connection_mode, optionstring,*self.argv)

-        cid = pybullet.connect(self.connection_mode, options=optionstring,*self.argv)

-        if cid < 0:

-            raise ValueError

-        print("connected")

-        pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_GUI,0)

-        pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_SEGMENTATION_MARK_PREVIEW,0)

-        pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_DEPTH_BUFFER_PREVIEW,0)

-        pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_RGB_BUFFER_PREVIEW,0)

-

-        pybullet.resetSimulation()

-        pybullet.loadURDF("plane.urdf",[0,0,-1])

-        pybullet.loadURDF("r2d2.urdf")

-        pybullet.loadURDF("duck_vhacd.urdf")

-        pybullet.setGravity(0,0,-10)

-

-    def __exit__(self,*_,**__):

-        pybullet.disconnect()

-

-def test(num_runs=300, shadow=1, log=True, plot=False):

-    if log:

-        logId = pybullet.startStateLogging(pybullet.STATE_LOGGING_PROFILE_TIMINGS, "renderTimings")

-

-    if plot:

-        plt.ion()

-

-        img = np.random.rand(200, 320)

-        #img = [tandard_normal((50,100))

-        image = plt.imshow(img,interpolation='none',animated=True,label="blah")

-        ax = plt.gca()

-

-

-    times = np.zeros(num_runs)

-    yaw_gen = itertools.cycle(range(0,360,10))

-    for i, yaw in zip(range(num_runs),yaw_gen):

-        pybullet.stepSimulation()

-        start = time.time()

-        viewMatrix = pybullet.computeViewMatrixFromYawPitchRoll(camTargetPos, camDistance, yaw, pitch, roll, upAxisIndex)

-        aspect = pixelWidth / pixelHeight;

-        projectionMatrix = pybullet.computeProjectionMatrixFOV(fov, aspect, nearPlane, farPlane);

-        img_arr = pybullet.getCameraImage(pixelWidth, pixelHeight, viewMatrix,

-            projectionMatrix, shadow=shadow,lightDirection=[1,1,1],

-            renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)

-            #renderer=pybullet.ER_TINY_RENDERER)

-        stop = time.time()

-        duration = (stop - start)

-        if (duration):

-        	fps = 1./duration

-        	#print("fps=",fps)

-        else:

-        	fps=0

-        	#print("fps=",fps)

-        #print("duraction=",duration)

-        #print("fps=",fps)

-        times[i] = fps

-

-        if plot:

-            rgb = img_arr[2]

-            image.set_data(rgb)#np_img_arr)

-            ax.plot([0])

-            #plt.draw()

-            #plt.show()

-            plt.pause(0.01)

-

-    mean_time = float(np.mean(times))

-    print("mean: {0} for {1} runs".format(mean_time, num_runs))

-    print("")

-    if log:

-        pybullet.stopStateLogging(logId)

-    return mean_time

 

+  def __init__(self, connection_mode, *argv):

+    self.connection_mode = connection_mode

+    self.argv = argv

 

+  def __enter__(self):

+    print("connecting")

+    optionstring = '--width={} --height={}'.format(pixelWidth, pixelHeight)

+    optionstring += ' --window_backend=2 --render_device=0'

 

-if __name__ == "__main__":

+    print(self.connection_mode, optionstring, *self.argv)

+    cid = pybullet.connect(self.connection_mode, options=optionstring, *self.argv)

+    if cid < 0:

+      raise ValueError

+    print("connected")

+    pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_GUI, 0)

+    pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_SEGMENTATION_MARK_PREVIEW, 0)

+    pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_DEPTH_BUFFER_PREVIEW, 0)

+    pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_RGB_BUFFER_PREVIEW, 0)

 

+    pybullet.resetSimulation()

+    pybullet.loadURDF("plane.urdf", [0, 0, -1])

+    pybullet.loadURDF("r2d2.urdf")

+    pybullet.loadURDF("duck_vhacd.urdf")

+    pybullet.setGravity(0, 0, -10)

 

-    res = []

+  def __exit__(self, *_, **__):

+    pybullet.disconnect()

 

-    with BulletSim(pybullet.DIRECT):

-        print("\nTesting DIRECT")

-        mean_time = test(log=False,plot=True)

-        res.append(("tiny",mean_time))

 

+def test(num_runs=300, shadow=1, log=True, plot=False):

+  if log:

+    logId = pybullet.startStateLogging(pybullet.STATE_LOGGING_PROFILE_TIMINGS, "renderTimings")

+

+  if plot:

+    plt.ion()

+

+    img = np.random.rand(200, 320)

+    #img = [tandard_normal((50,100))

+    image = plt.imshow(img, interpolation='none', animated=True, label="blah")

+    ax = plt.gca()

+

+  times = np.zeros(num_runs)

+  yaw_gen = itertools.cycle(range(0, 360, 10))

+  for i, yaw in zip(range(num_runs), yaw_gen):

+    pybullet.stepSimulation()

+    start = time.time()

+    viewMatrix = pybullet.computeViewMatrixFromYawPitchRoll(camTargetPos, camDistance, yaw, pitch,

+                                                            roll, upAxisIndex)

+    aspect = pixelWidth / pixelHeight

+    projectionMatrix = pybullet.computeProjectionMatrixFOV(fov, aspect, nearPlane, farPlane)

+    img_arr = pybullet.getCameraImage(pixelWidth,

+                                      pixelHeight,

+                                      viewMatrix,

+                                      projectionMatrix,

+                                      shadow=shadow,

+                                      lightDirection=[1, 1, 1],

+                                      renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)

+    #renderer=pybullet.ER_TINY_RENDERER)

+    stop = time.time()

+    duration = (stop - start)

+    if (duration):

+      fps = 1. / duration

+      #print("fps=",fps)

+    else:

+      fps = 0

+      #print("fps=",fps)

+    #print("duraction=",duration)

+    #print("fps=",fps)

+    times[i] = fps

 

-    with BulletSim(pybullet.DIRECT):

-        plugin_fn = os.path.join(pybullet.__file__.split("bullet3")[0],"bullet3/build/lib.linux-x86_64-3.5/eglRenderer.cpython-35m-x86_64-linux-gnu.so")

-        plugin = pybullet.loadPlugin(plugin_fn,"_tinyRendererPlugin")

-        if plugin < 0:

-            print("\nPlugin Failed to load!\n")

-            sys.exit()

+    if plot:

+      rgb = img_arr[2]

+      image.set_data(rgb)  #np_img_arr)

+      ax.plot([0])

+      #plt.draw()

+      #plt.show()

+      plt.pause(0.01)

 

-        print("\nTesting DIRECT+OpenGL")

-        mean_time = test(log=True)

-        res.append(("plugin",mean_time))

+  mean_time = float(np.mean(times))

+  print("mean: {0} for {1} runs".format(mean_time, num_runs))

+  print("")

+  if log:

+    pybullet.stopStateLogging(logId)

+  return mean_time

 

-    with BulletSim(pybullet.GUI):

-        print("\nTesting GUI")

-        mean_time = test(log=False)

-        res.append(("egl",mean_time))

 

-    print()

-    print("rendertest.py")

-    print("back nenv fps fps_tot")

-    for r in res:

-        print(r[0],"\t",1,round(r[1]),"\t",round(r[1]))

+if __name__ == "__main__":

 

+  res = []

+

+  with BulletSim(pybullet.DIRECT):

+    print("\nTesting DIRECT")

+    mean_time = test(log=False, plot=True)

+    res.append(("tiny", mean_time))

+

+  with BulletSim(pybullet.DIRECT):

+    plugin_fn = os.path.join(

+        pybullet.__file__.split("bullet3")[0],

+        "bullet3/build/lib.linux-x86_64-3.5/eglRenderer.cpython-35m-x86_64-linux-gnu.so")

+    plugin = pybullet.loadPlugin(plugin_fn, "_tinyRendererPlugin")

+    if plugin < 0:

+      print("\nPlugin Failed to load!\n")

+      sys.exit()

+

+    print("\nTesting DIRECT+OpenGL")

+    mean_time = test(log=True)

+    res.append(("plugin", mean_time))

+

+  with BulletSim(pybullet.GUI):

+    print("\nTesting GUI")

+    mean_time = test(log=False)

+    res.append(("egl", mean_time))

+

+  print()

+  print("rendertest.py")

+  print("back nenv fps fps_tot")

+  for r in res:

+    print(r[0], "\t", 1, round(r[1]), "\t", round(r[1]))

diff --git a/examples/pybullet/examples/rendertest_sync.py b/examples/pybullet/examples/rendertest_sync.py
index f7b99d20e..b7dfd2f3e 100644
--- a/examples/pybullet/examples/rendertest_sync.py
+++ b/examples/pybullet/examples/rendertest_sync.py
@@ -15,11 +15,11 @@ import pybullet as p
 from itertools import cycle
 import numpy as np
 
-camTargetPos = [0,0,0]
-cameraUp = [0,0,1]
-cameraPos = [1,1,1]
+camTargetPos = [0, 0, 0]
+cameraUp = [0, 0, 1]
+cameraPos = [1, 1, 1]
 pitch = -10.0
-roll=0
+roll = 0
 upAxisIndex = 2
 camDistance = 4
 pixelWidth = 320
@@ -30,113 +30,126 @@ fov = 60
 
 
 class TestEnv(gym.Env):
-    def __init__(self,
-        renderer = 'tiny',  # ('tiny', 'egl', 'debug')
-        ):
-        self.action_space = spaces.Discrete(2)
-        self.iter = cycle(range(0,360,10))
-
-        # how we want to show
-        assert renderer in ('tiny', 'egl', 'debug','plugin')
-        self._renderer = renderer
-        self._render_width = 84
-        self._render_height = 84
-        # connecting
-        if self._renderer == "tiny" or self._renderer == "plugin":
-            optionstring='--width={} --height={}'.format(self._render_width,self._render_height)
-            p.connect(p.DIRECT, options=optionstring)
-
-            if self._renderer == "plugin":
-                plugin_fn = os.path.join(p.__file__.split("bullet3")[0],"bullet3/build/lib.linux-x86_64-3.5/eglRenderer.cpython-35m-x86_64-linux-gnu.so")
-                plugin = p.loadPlugin(plugin_fn,"_tinyRendererPlugin")
-                if plugin < 0:
-                    print("\nPlugin Failed to load! Try installing via `pip install -e .`\n")
-                    sys.exit()
-                print("plugin =",plugin)
-
-        elif self._renderer == "egl":
-            optionstring='--width={} --height={}'.format(self._render_width,self._render_height)
-            optionstring += ' --window_backend=2 --render_device=0'
-            p.connect(p.GUI, options=optionstring)
-
-        elif self._renderer == "debug":
-          #print("Connection: SHARED_MEMORY")
-          #cid = p.connect(p.SHARED_MEMORY)
-          #if (cid<0):
-          cid =  p.connect(p.GUI)
-          p.resetDebugVisualizerCamera(1.3,180,-41,[0.52,-0.2,-0.33])
-
-        p.configureDebugVisualizer(p.COV_ENABLE_GUI,0)
-        p.configureDebugVisualizer(p.COV_ENABLE_SEGMENTATION_MARK_PREVIEW,0)
-        p.configureDebugVisualizer(p.COV_ENABLE_DEPTH_BUFFER_PREVIEW,0)
-        p.configureDebugVisualizer(p.COV_ENABLE_RGB_BUFFER_PREVIEW,0)
-
-
-    def __del__(self):
-        p.disconnect()
-
-    def reset(self):
-        pass
-
-    def step(self,action):
-        p.stepSimulation()
-        start = time.time()
-        yaw = next(self.iter)
-        viewMatrix = p.computeViewMatrixFromYawPitchRoll(camTargetPos, camDistance, yaw, pitch, roll, upAxisIndex)
-        aspect = pixelWidth / pixelHeight;
-        projectionMatrix = p.computeProjectionMatrixFOV(fov, aspect, nearPlane, farPlane);
-        img_arr = p.getCameraImage(pixelWidth, pixelHeight, viewMatrix,
-            projectionMatrix, shadow=1,lightDirection=[1,1,1],
-            renderer=p.ER_BULLET_HARDWARE_OPENGL)
-            #renderer=pybullet.ER_TINY_RENDERER)
-        self._observation = img_arr[2]
-        return np.array(self._observation), 0, 0, {}
-
-    def seed(self, seed=None):
-      pass
-
-def train(env_id, num_timesteps=300, seed=0,num_env=2,renderer='tiny'):
-    def make_env(rank):
-        def _thunk():
-            if env_id == "TestEnv":
-                env = TestEnv(renderer=renderer)  #gym.make(env_id)
-            else:
-                env = gym.make(env_id)
-            env.seed(seed + rank)
-            env = bench.Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)))
-            gym.logger.setLevel(logging.WARN)
-            # only clip rewards when not evaluating
-            return env
-        return _thunk
-    set_global_seeds(seed)
-    env = SubprocVecEnv([make_env(i) for i in range(num_env)])
-
-    env.reset()
+
+  def __init__(
+      self,
+      renderer='tiny',  # ('tiny', 'egl', 'debug')
+  ):
+    self.action_space = spaces.Discrete(2)
+    self.iter = cycle(range(0, 360, 10))
+
+    # how we want to show
+    assert renderer in ('tiny', 'egl', 'debug', 'plugin')
+    self._renderer = renderer
+    self._render_width = 84
+    self._render_height = 84
+    # connecting
+    if self._renderer == "tiny" or self._renderer == "plugin":
+      optionstring = '--width={} --height={}'.format(self._render_width, self._render_height)
+      p.connect(p.DIRECT, options=optionstring)
+
+      if self._renderer == "plugin":
+        plugin_fn = os.path.join(
+            p.__file__.split("bullet3")[0],
+            "bullet3/build/lib.linux-x86_64-3.5/eglRenderer.cpython-35m-x86_64-linux-gnu.so")
+        plugin = p.loadPlugin(plugin_fn, "_tinyRendererPlugin")
+        if plugin < 0:
+          print("\nPlugin Failed to load! Try installing via `pip install -e .`\n")
+          sys.exit()
+        print("plugin =", plugin)
+
+    elif self._renderer == "egl":
+      optionstring = '--width={} --height={}'.format(self._render_width, self._render_height)
+      optionstring += ' --window_backend=2 --render_device=0'
+      p.connect(p.GUI, options=optionstring)
+
+    elif self._renderer == "debug":
+      #print("Connection: SHARED_MEMORY")
+      #cid = p.connect(p.SHARED_MEMORY)
+      #if (cid<0):
+      cid = p.connect(p.GUI)
+      p.resetDebugVisualizerCamera(1.3, 180, -41, [0.52, -0.2, -0.33])
+
+    p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0)
+    p.configureDebugVisualizer(p.COV_ENABLE_SEGMENTATION_MARK_PREVIEW, 0)
+    p.configureDebugVisualizer(p.COV_ENABLE_DEPTH_BUFFER_PREVIEW, 0)
+    p.configureDebugVisualizer(p.COV_ENABLE_RGB_BUFFER_PREVIEW, 0)
+
+  def __del__(self):
+    p.disconnect()
+
+  def reset(self):
+    pass
+
+  def step(self, action):
+    p.stepSimulation()
     start = time.time()
-    for i in range(num_timesteps):
-        action = [env.action_space.sample() for _ in range(num_env)]
-        env.step(action)
-    stop = time.time()
-    duration = (stop - start)
-    if (duration):
-            fps = num_timesteps/duration
-    else:
-            fps=0
-    env.close()
-    return num_env, fps
+    yaw = next(self.iter)
+    viewMatrix = p.computeViewMatrixFromYawPitchRoll(camTargetPos, camDistance, yaw, pitch, roll,
+                                                     upAxisIndex)
+    aspect = pixelWidth / pixelHeight
+    projectionMatrix = p.computeProjectionMatrixFOV(fov, aspect, nearPlane, farPlane)
+    img_arr = p.getCameraImage(pixelWidth,
+                               pixelHeight,
+                               viewMatrix,
+                               projectionMatrix,
+                               shadow=1,
+                               lightDirection=[1, 1, 1],
+                               renderer=p.ER_BULLET_HARDWARE_OPENGL)
+    #renderer=pybullet.ER_TINY_RENDERER)
+    self._observation = img_arr[2]
+    return np.array(self._observation), 0, 0, {}
+
+  def seed(self, seed=None):
+    pass
+
+
+def train(env_id, num_timesteps=300, seed=0, num_env=2, renderer='tiny'):
+
+  def make_env(rank):
+
+    def _thunk():
+      if env_id == "TestEnv":
+        env = TestEnv(renderer=renderer)  #gym.make(env_id)
+      else:
+        env = gym.make(env_id)
+      env.seed(seed + rank)
+      env = bench.Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)))
+      gym.logger.setLevel(logging.WARN)
+      # only clip rewards when not evaluating
+      return env
+
+    return _thunk
+
+  set_global_seeds(seed)
+  env = SubprocVecEnv([make_env(i) for i in range(num_env)])
+
+  env.reset()
+  start = time.time()
+  for i in range(num_timesteps):
+    action = [env.action_space.sample() for _ in range(num_env)]
+    env.step(action)
+  stop = time.time()
+  duration = (stop - start)
+  if (duration):
+    fps = num_timesteps / duration
+  else:
+    fps = 0
+  env.close()
+  return num_env, fps
 
 
 if __name__ == "__main__":
-    env_id = "TestEnv"
-    res = []
-
-    for renderer in ('tiny','plugin', 'egl'):
-        for i in (1,8):
-            tmp = train(env_id,num_env=i,renderer=renderer)
-            print(renderer,tmp)
-            res.append((renderer,tmp))
-    print()
-    print("rendertest_sync.py")
-    print("back nenv fps fps_tot")
-    for renderer,i in res:
-        print(renderer,'\t', i[0],round(i[1]),'\t',round(i[0]*i[1]))
+  env_id = "TestEnv"
+  res = []
+
+  for renderer in ('tiny', 'plugin', 'egl'):
+    for i in (1, 8):
+      tmp = train(env_id, num_env=i, renderer=renderer)
+      print(renderer, tmp)
+      res.append((renderer, tmp))
+  print()
+  print("rendertest_sync.py")
+  print("back nenv fps fps_tot")
+  for renderer, i in res:
+    print(renderer, '\t', i[0], round(i[1]), '\t', round(i[0] * i[1]))
diff --git a/examples/pybullet/examples/reset_dynamic_info.py b/examples/pybullet/examples/reset_dynamic_info.py
index eb87358af..1092496f8 100644
--- a/examples/pybullet/examples/reset_dynamic_info.py
+++ b/examples/pybullet/examples/reset_dynamic_info.py
@@ -3,64 +3,124 @@ import time
 import math
 
 p.connect(p.GUI)
-planeId = p.loadURDF(fileName="plane.urdf",baseOrientation=[0.25882,0,0,0.96593])
-p.loadURDF(fileName="cube.urdf",basePosition=[0,0,2])
-cubeId = p.loadURDF(fileName="cube.urdf",baseOrientation=[0,0,0,1],basePosition=[0,0,4])
+planeId = p.loadURDF(fileName="plane.urdf", baseOrientation=[0.25882, 0, 0, 0.96593])
+p.loadURDF(fileName="cube.urdf", basePosition=[0, 0, 2])
+cubeId = p.loadURDF(fileName="cube.urdf", baseOrientation=[0, 0, 0, 1], basePosition=[0, 0, 4])
 #p.changeDynamics(bodyUniqueId=2,linkIndex=-1,mass=0.1)
-p.changeDynamics(bodyUniqueId=2,linkIndex=-1,mass=1.0)
-p.setGravity(0,0,-10)
+p.changeDynamics(bodyUniqueId=2, linkIndex=-1, mass=1.0)
+p.setGravity(0, 0, -10)
 p.setRealTimeSimulation(0)
 
+
 def drawInertiaBox(parentUid, parentLinkIndex):
-	mass,frictionCoeff, inertia =p.getDynamicsInfo(bodyUniqueId=parentUid,linkIndex=parentLinkIndex, flags = p.DYNAMICS_INFO_REPORT_INERTIA)
-	Ixx = inertia[0]
-	Iyy = inertia[1]
-	Izz = inertia[2]
-	boxScaleX = 0.5*math.sqrt(6*(Izz + Iyy - Ixx) / mass);
-	boxScaleY = 0.5*math.sqrt(6*(Izz + Ixx - Iyy) / mass);
-	boxScaleZ = 0.5*math.sqrt(6*(Ixx + Iyy - Izz) / mass);
-  
-	halfExtents = [boxScaleX,boxScaleY,boxScaleZ]
-	pts = [[halfExtents[0],halfExtents[1],halfExtents[2]],
-				 [-halfExtents[0],halfExtents[1],halfExtents[2]],
-				 [halfExtents[0],-halfExtents[1],halfExtents[2]],
-				 [-halfExtents[0],-halfExtents[1],halfExtents[2]],
-				 [halfExtents[0],halfExtents[1],-halfExtents[2]],
-				 [-halfExtents[0],halfExtents[1],-halfExtents[2]],
-				 [halfExtents[0],-halfExtents[1],-halfExtents[2]],
-				 [-halfExtents[0],-halfExtents[1],-halfExtents[2]]]
-	
-	color=[1,0,0]
-	p.addUserDebugLine(pts[0],pts[1],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-	p.addUserDebugLine(pts[1],pts[3],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-	p.addUserDebugLine(pts[3],pts[2],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-	p.addUserDebugLine(pts[2],pts[0],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-	
-	p.addUserDebugLine(pts[0],pts[4],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-	p.addUserDebugLine(pts[1],pts[5],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-	p.addUserDebugLine(pts[2],pts[6],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-	p.addUserDebugLine(pts[3],pts[7],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-	
-	p.addUserDebugLine(pts[4+0],pts[4+1],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-	p.addUserDebugLine(pts[4+1],pts[4+3],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-	p.addUserDebugLine(pts[4+3],pts[4+2],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-	p.addUserDebugLine(pts[4+2],pts[4+0],color,1, parentObjectUniqueId=parentUid, parentLinkIndex = parentLinkIndex)
-	
-	
-	
-  
-drawInertiaBox(cubeId,-1)
+  mass, frictionCoeff, inertia = p.getDynamicsInfo(bodyUniqueId=parentUid,
+                                                   linkIndex=parentLinkIndex,
+                                                   flags=p.DYNAMICS_INFO_REPORT_INERTIA)
+  Ixx = inertia[0]
+  Iyy = inertia[1]
+  Izz = inertia[2]
+  boxScaleX = 0.5 * math.sqrt(6 * (Izz + Iyy - Ixx) / mass)
+  boxScaleY = 0.5 * math.sqrt(6 * (Izz + Ixx - Iyy) / mass)
+  boxScaleZ = 0.5 * math.sqrt(6 * (Ixx + Iyy - Izz) / mass)
+
+  halfExtents = [boxScaleX, boxScaleY, boxScaleZ]
+  pts = [[halfExtents[0], halfExtents[1], halfExtents[2]],
+         [-halfExtents[0], halfExtents[1], halfExtents[2]],
+         [halfExtents[0], -halfExtents[1], halfExtents[2]],
+         [-halfExtents[0], -halfExtents[1], halfExtents[2]],
+         [halfExtents[0], halfExtents[1], -halfExtents[2]],
+         [-halfExtents[0], halfExtents[1], -halfExtents[2]],
+         [halfExtents[0], -halfExtents[1], -halfExtents[2]],
+         [-halfExtents[0], -halfExtents[1], -halfExtents[2]]]
+
+  color = [1, 0, 0]
+  p.addUserDebugLine(pts[0],
+                     pts[1],
+                     color,
+                     1,
+                     parentObjectUniqueId=parentUid,
+                     parentLinkIndex=parentLinkIndex)
+  p.addUserDebugLine(pts[1],
+                     pts[3],
+                     color,
+                     1,
+                     parentObjectUniqueId=parentUid,
+                     parentLinkIndex=parentLinkIndex)
+  p.addUserDebugLine(pts[3],
+                     pts[2],
+                     color,
+                     1,
+                     parentObjectUniqueId=parentUid,
+                     parentLinkIndex=parentLinkIndex)
+  p.addUserDebugLine(pts[2],
+                     pts[0],
+                     color,
+                     1,
+                     parentObjectUniqueId=parentUid,
+                     parentLinkIndex=parentLinkIndex)
+
+  p.addUserDebugLine(pts[0],
+                     pts[4],
+                     color,
+                     1,
+                     parentObjectUniqueId=parentUid,
+                     parentLinkIndex=parentLinkIndex)
+  p.addUserDebugLine(pts[1],
+                     pts[5],
+                     color,
+                     1,
+                     parentObjectUniqueId=parentUid,
+                     parentLinkIndex=parentLinkIndex)
+  p.addUserDebugLine(pts[2],
+                     pts[6],
+                     color,
+                     1,
+                     parentObjectUniqueId=parentUid,
+                     parentLinkIndex=parentLinkIndex)
+  p.addUserDebugLine(pts[3],
+                     pts[7],
+                     color,
+                     1,
+                     parentObjectUniqueId=parentUid,
+                     parentLinkIndex=parentLinkIndex)
 
-t=0
+  p.addUserDebugLine(pts[4 + 0],
+                     pts[4 + 1],
+                     color,
+                     1,
+                     parentObjectUniqueId=parentUid,
+                     parentLinkIndex=parentLinkIndex)
+  p.addUserDebugLine(pts[4 + 1],
+                     pts[4 + 3],
+                     color,
+                     1,
+                     parentObjectUniqueId=parentUid,
+                     parentLinkIndex=parentLinkIndex)
+  p.addUserDebugLine(pts[4 + 3],
+                     pts[4 + 2],
+                     color,
+                     1,
+                     parentObjectUniqueId=parentUid,
+                     parentLinkIndex=parentLinkIndex)
+  p.addUserDebugLine(pts[4 + 2],
+                     pts[4 + 0],
+                     color,
+                     1,
+                     parentObjectUniqueId=parentUid,
+                     parentLinkIndex=parentLinkIndex)
+
+
+drawInertiaBox(cubeId, -1)
+
+t = 0
 while 1:
-	t=t+1
-	if t > 400:
-		p.changeDynamics(bodyUniqueId=0,linkIndex=-1,lateralFriction=0.01)
-	mass1,frictionCoeff1 =p.getDynamicsInfo(bodyUniqueId=planeId,linkIndex=-1)
-	mass2,frictionCoeff2 =p.getDynamicsInfo(bodyUniqueId=cubeId,linkIndex=-1)
-	
-			
-	print (mass1,frictionCoeff1)
-	print (mass2,frictionCoeff2)
-	time.sleep(1./240.)
-	p.stepSimulation()
+  t = t + 1
+  if t > 400:
+    p.changeDynamics(bodyUniqueId=0, linkIndex=-1, lateralFriction=0.01)
+  mass1, frictionCoeff1 = p.getDynamicsInfo(bodyUniqueId=planeId, linkIndex=-1)
+  mass2, frictionCoeff2 = p.getDynamicsInfo(bodyUniqueId=cubeId, linkIndex=-1)
+
+  print(mass1, frictionCoeff1)
+  print(mass2, frictionCoeff2)
+  time.sleep(1. / 240.)
+  p.stepSimulation()
diff --git a/examples/pybullet/examples/restitution.py b/examples/pybullet/examples/restitution.py
index 3b12185a8..dc169fe48 100644
--- a/examples/pybullet/examples/restitution.py
+++ b/examples/pybullet/examples/restitution.py
@@ -5,36 +5,36 @@ import pybullet as p
 import time
 
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	cid = p.connect(p.GUI)
-restitutionId = p.addUserDebugParameter("restitution",0,1,1)
-restitutionVelocityThresholdId = p.addUserDebugParameter("res. vel. threshold",0,3,0.2)
+if (cid < 0):
+  cid = p.connect(p.GUI)
+restitutionId = p.addUserDebugParameter("restitution", 0, 1, 1)
+restitutionVelocityThresholdId = p.addUserDebugParameter("res. vel. threshold", 0, 3, 0.2)
 
-lateralFrictionId = p.addUserDebugParameter("lateral friction",0,1,0.5)
-spinningFrictionId = p.addUserDebugParameter("spinning friction",0,1,0.03)
-rollingFrictionId = p.addUserDebugParameter("rolling friction",0,1,0.03)
+lateralFrictionId = p.addUserDebugParameter("lateral friction", 0, 1, 0.5)
+spinningFrictionId = p.addUserDebugParameter("spinning friction", 0, 1, 0.03)
+rollingFrictionId = p.addUserDebugParameter("rolling friction", 0, 1, 0.03)
 
 plane = p.loadURDF("plane_with_restitution.urdf")
-sphere = p.loadURDF("sphere_with_restitution.urdf",[0,0,2])
+sphere = p.loadURDF("sphere_with_restitution.urdf", [0, 0, 2])
 
 p.setRealTimeSimulation(1)
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 while (1):
-	restitution = p.readUserDebugParameter(restitutionId)
-	restitutionVelocityThreshold = p.readUserDebugParameter(restitutionVelocityThresholdId)
-	p.setPhysicsEngineParameter(restitutionVelocityThreshold=restitutionVelocityThreshold)
+  restitution = p.readUserDebugParameter(restitutionId)
+  restitutionVelocityThreshold = p.readUserDebugParameter(restitutionVelocityThresholdId)
+  p.setPhysicsEngineParameter(restitutionVelocityThreshold=restitutionVelocityThreshold)
 
-	lateralFriction = 	p.readUserDebugParameter(lateralFrictionId)
-	spinningFriction = 	p.readUserDebugParameter(spinningFrictionId)
-	rollingFriction = 	p.readUserDebugParameter(rollingFrictionId)
-	p.changeDynamics(plane,-1,lateralFriction=1)
-	p.changeDynamics(sphere,-1,lateralFriction=lateralFriction)
-	p.changeDynamics(sphere,-1,spinningFriction=spinningFriction)
-	p.changeDynamics(sphere,-1,rollingFriction=rollingFriction)
-	
-	p.changeDynamics(plane,-1,restitution=restitution)
-	p.changeDynamics(sphere,-1,restitution=restitution)
-	pos,orn=p.getBasePositionAndOrientation(sphere)
-	#print("pos=")
-	#print(pos)
-	time.sleep(0.01)
\ No newline at end of file
+  lateralFriction = p.readUserDebugParameter(lateralFrictionId)
+  spinningFriction = p.readUserDebugParameter(spinningFrictionId)
+  rollingFriction = p.readUserDebugParameter(rollingFrictionId)
+  p.changeDynamics(plane, -1, lateralFriction=1)
+  p.changeDynamics(sphere, -1, lateralFriction=lateralFriction)
+  p.changeDynamics(sphere, -1, spinningFriction=spinningFriction)
+  p.changeDynamics(sphere, -1, rollingFriction=rollingFriction)
+
+  p.changeDynamics(plane, -1, restitution=restitution)
+  p.changeDynamics(sphere, -1, restitution=restitution)
+  pos, orn = p.getBasePositionAndOrientation(sphere)
+  #print("pos=")
+  #print(pos)
+  time.sleep(0.01)
diff --git a/examples/pybullet/examples/robotcontrol.py b/examples/pybullet/examples/robotcontrol.py
index 3541b9d9e..7633baf18 100644
--- a/examples/pybullet/examples/robotcontrol.py
+++ b/examples/pybullet/examples/robotcontrol.py
@@ -1,30 +1,28 @@
 import pybullet as p
-p.connect(p.GUI) #or p.SHARED_MEMORY or p.DIRECT
+p.connect(p.GUI)  #or p.SHARED_MEMORY or p.DIRECT
 
 p.loadURDF("plane.urdf")
-p.setGravity(0,0,-10)
-huskypos = [0,0,0.1]
+p.setGravity(0, 0, -10)
+huskypos = [0, 0, 0.1]
 
-husky = p.loadURDF("husky/husky.urdf",huskypos[0],huskypos[1],huskypos[2])
+husky = p.loadURDF("husky/husky.urdf", huskypos[0], huskypos[1], huskypos[2])
 numJoints = p.getNumJoints(husky)
-for joint in range (numJoints) :
-	print (p.getJointInfo(husky,joint))
-targetVel = 10 #rad/s
-maxForce = 100 #Newton
-
-for joint in range (2,6) :
-	p.setJointMotorControl(husky,joint,p.VELOCITY_CONTROL,targetVel,maxForce)
-for step in range (300):
-	p.stepSimulation()
-
-targetVel=-10
-for joint in range (2,6) :
-        p.setJointMotorControl(husky,joint,p.VELOCITY_CONTROL,targetVel,maxForce)
-for step in range (400):
-        p.stepSimulation()
+for joint in range(numJoints):
+  print(p.getJointInfo(husky, joint))
+targetVel = 10  #rad/s
+maxForce = 100  #Newton
+
+for joint in range(2, 6):
+  p.setJointMotorControl(husky, joint, p.VELOCITY_CONTROL, targetVel, maxForce)
+for step in range(300):
+  p.stepSimulation()
+
+targetVel = -10
+for joint in range(2, 6):
+  p.setJointMotorControl(husky, joint, p.VELOCITY_CONTROL, targetVel, maxForce)
+for step in range(400):
+  p.stepSimulation()
 
 p.getContactPoints(husky)
 
 p.disconnect()
-
-
diff --git a/examples/pybullet/examples/rollPitchYaw.py b/examples/pybullet/examples/rollPitchYaw.py
index d807fb845..5ab2793ae 100644
--- a/examples/pybullet/examples/rollPitchYaw.py
+++ b/examples/pybullet/examples/rollPitchYaw.py
@@ -2,25 +2,24 @@ import pybullet as p
 import time
 
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	p.connect(p.GUI)
-q = p.loadURDF("quadruped/quadruped.urdf",useFixedBase=True)
-rollId = p.addUserDebugParameter("roll",-1.5,1.5,0)
-pitchId = p.addUserDebugParameter("pitch",-1.5,1.5,0)
-yawId = p.addUserDebugParameter("yaw",-1.5,1.5,0)
-fwdxId = p.addUserDebugParameter("fwd_x",-1,1,0)
-fwdyId = p.addUserDebugParameter("fwd_y",-1,1,0)
-fwdzId = p.addUserDebugParameter("fwd_z",-1,1,0)
+if (cid < 0):
+  p.connect(p.GUI)
+q = p.loadURDF("quadruped/quadruped.urdf", useFixedBase=True)
+rollId = p.addUserDebugParameter("roll", -1.5, 1.5, 0)
+pitchId = p.addUserDebugParameter("pitch", -1.5, 1.5, 0)
+yawId = p.addUserDebugParameter("yaw", -1.5, 1.5, 0)
+fwdxId = p.addUserDebugParameter("fwd_x", -1, 1, 0)
+fwdyId = p.addUserDebugParameter("fwd_y", -1, 1, 0)
+fwdzId = p.addUserDebugParameter("fwd_z", -1, 1, 0)
 
 while True:
-	roll = p.readUserDebugParameter(rollId)
-	pitch = p.readUserDebugParameter(pitchId)
-	yaw = p.readUserDebugParameter(yawId)
-	x = p.readUserDebugParameter(fwdxId)
-	y = p.readUserDebugParameter(fwdyId)
-	z = p.readUserDebugParameter(fwdzId)
-
-	orn = p.getQuaternionFromEuler([roll,pitch,yaw])
-	p.resetBasePositionAndOrientation(q,[x,y,z],orn)
-	#p.stepSimulation()#not really necessary for this demo, no physics used
+  roll = p.readUserDebugParameter(rollId)
+  pitch = p.readUserDebugParameter(pitchId)
+  yaw = p.readUserDebugParameter(yawId)
+  x = p.readUserDebugParameter(fwdxId)
+  y = p.readUserDebugParameter(fwdyId)
+  z = p.readUserDebugParameter(fwdzId)
 
+  orn = p.getQuaternionFromEuler([roll, pitch, yaw])
+  p.resetBasePositionAndOrientation(q, [x, y, z], orn)
+  #p.stepSimulation()#not really necessary for this demo, no physics used
diff --git a/examples/pybullet/examples/satCollision.py b/examples/pybullet/examples/satCollision.py
index 847d2b8e6..c1da8d1b4 100644
--- a/examples/pybullet/examples/satCollision.py
+++ b/examples/pybullet/examples/satCollision.py
@@ -1,16 +1,18 @@
 import pybullet as p
-import time 
+import time
 
 p.connect(p.GUI)
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 p.setPhysicsEngineParameter(enableSAT=1)
-p.loadURDF("cube_concave.urdf",[0,0,-25], globalScaling=50, useFixedBase=True, flags=p.URDF_INITIALIZE_SAT_FEATURES)
-p.loadURDF("cube.urdf",[0,0,1], globalScaling=1,flags=p.URDF_INITIALIZE_SAT_FEATURES)
-p.loadURDF("duck_vhacd.urdf",[1,0,1], globalScaling=1,flags=p.URDF_INITIALIZE_SAT_FEATURES)
+p.loadURDF("cube_concave.urdf", [0, 0, -25],
+           globalScaling=50,
+           useFixedBase=True,
+           flags=p.URDF_INITIALIZE_SAT_FEATURES)
+p.loadURDF("cube.urdf", [0, 0, 1], globalScaling=1, flags=p.URDF_INITIALIZE_SAT_FEATURES)
+p.loadURDF("duck_vhacd.urdf", [1, 0, 1], globalScaling=1, flags=p.URDF_INITIALIZE_SAT_FEATURES)
 
 while (p.isConnected()):
-	p.stepSimulation()
-	pts = p.getContactPoints()
-	#print("num contacts = ", len(pts))
-	time.sleep(1./240.)
-	
\ No newline at end of file
+  p.stepSimulation()
+  pts = p.getContactPoints()
+  #print("num contacts = ", len(pts))
+  time.sleep(1. / 240.)
diff --git a/examples/pybullet/examples/saveRestoreState.py b/examples/pybullet/examples/saveRestoreState.py
index 7bac634be..7bce8313f 100644
--- a/examples/pybullet/examples/saveRestoreState.py
+++ b/examples/pybullet/examples/saveRestoreState.py
@@ -1,6 +1,6 @@
 import pybullet as p
 import math, time
-import difflib,sys
+import difflib, sys
 
 numSteps = 500
 numSteps2 = 30
@@ -10,69 +10,71 @@ verbose = 0
 
 logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "saveRestoreTimings.log")
 
+
 def setupWorld():
-	p.resetSimulation()
-	p.setPhysicsEngineParameter(deterministicOverlappingPairs=1)
-	p.loadURDF("planeMesh.urdf")
-	kukaId = p.loadURDF("kuka_iiwa/model_free_base.urdf",[0,0,10])
-	for i in range (p.getNumJoints(kukaId)):
-		p.setJointMotorControl2(kukaId,i,p.POSITION_CONTROL,force=0)
-	for i in range (numObjects):
-		cube = p.loadURDF("cube_small.urdf",[0,i*0.02,(i+1)*0.2])
-		#p.changeDynamics(cube,-1,mass=100)
-	p.stepSimulation()
-	p.setGravity(0,0,-10)
+  p.resetSimulation()
+  p.setPhysicsEngineParameter(deterministicOverlappingPairs=1)
+  p.loadURDF("planeMesh.urdf")
+  kukaId = p.loadURDF("kuka_iiwa/model_free_base.urdf", [0, 0, 10])
+  for i in range(p.getNumJoints(kukaId)):
+    p.setJointMotorControl2(kukaId, i, p.POSITION_CONTROL, force=0)
+  for i in range(numObjects):
+    cube = p.loadURDF("cube_small.urdf", [0, i * 0.02, (i + 1) * 0.2])
+    #p.changeDynamics(cube,-1,mass=100)
+  p.stepSimulation()
+  p.setGravity(0, 0, -10)
 
 
 def dumpStateToFile(file):
-	for i in  range (p.getNumBodies()):
-		pos,orn = p.getBasePositionAndOrientation(i)
-		linVel,angVel = p.getBaseVelocity(i)
-		txtPos = "pos="+str(pos)+"\n"
-		txtOrn = "orn="+str(orn)+"\n"
-		txtLinVel = "linVel"+str(linVel)+"\n"
-		txtAngVel = "angVel"+str(angVel)+"\n"
-		file.write(txtPos)
-		file.write(txtOrn)
-		file.write(txtLinVel)
-		file.write(txtAngVel)
-		
-def compareFiles(file1,file2):
-	diff = difflib.unified_diff(
-            file1.readlines(),
-            file2.readlines(),
-            fromfile='saveFile.txt',
-            tofile='restoreFile.txt',
-        )
-	numDifferences = 0
-	for line in diff:
-		numDifferences = numDifferences+1
-		sys.stdout.write(line)
-	if (numDifferences>0):
-		print("Error:", numDifferences, " lines are different between files.")
-	else:
-		print("OK, files are identical")
-	
+  for i in range(p.getNumBodies()):
+    pos, orn = p.getBasePositionAndOrientation(i)
+    linVel, angVel = p.getBaseVelocity(i)
+    txtPos = "pos=" + str(pos) + "\n"
+    txtOrn = "orn=" + str(orn) + "\n"
+    txtLinVel = "linVel" + str(linVel) + "\n"
+    txtAngVel = "angVel" + str(angVel) + "\n"
+    file.write(txtPos)
+    file.write(txtOrn)
+    file.write(txtLinVel)
+    file.write(txtAngVel)
+
+
+def compareFiles(file1, file2):
+  diff = difflib.unified_diff(
+      file1.readlines(),
+      file2.readlines(),
+      fromfile='saveFile.txt',
+      tofile='restoreFile.txt',
+  )
+  numDifferences = 0
+  for line in diff:
+    numDifferences = numDifferences + 1
+    sys.stdout.write(line)
+  if (numDifferences > 0):
+    print("Error:", numDifferences, " lines are different between files.")
+  else:
+    print("OK, files are identical")
+
+
 setupWorld()
-for i in range (numSteps):
-	p.stepSimulation()	
+for i in range(numSteps):
+  p.stepSimulation()
 p.saveBullet("state.bullet")
 if verbose:
-	p.setInternalSimFlags(1)
+  p.setInternalSimFlags(1)
 p.stepSimulation()
 if verbose:
-	p.setInternalSimFlags(0)
-	print("contact points=")
-	for q in p.getContactPoints():
-		print(q)
+  p.setInternalSimFlags(0)
+  print("contact points=")
+  for q in p.getContactPoints():
+    print(q)
 
-for i in range (numSteps2):
-	p.stepSimulation()
+for i in range(numSteps2):
+  p.stepSimulation()
 
-
-file = open("saveFile.txt","w") 
+file = open("saveFile.txt", "w")
 dumpStateToFile(file)
-file.close() 
+file.close()
 
 #################################
 setupWorld()
@@ -80,58 +82,55 @@ setupWorld()
 #both restore from file or from in-memory state should work
 p.restoreState(fileName="state.bullet")
 stateId = p.saveState()
-print("stateId=",stateId)
+print("stateId=", stateId)
 p.removeState(stateId)
 stateId = p.saveState()
-print("stateId=",stateId)
+print("stateId=", stateId)
 
 if verbose:
-	p.setInternalSimFlags(1)
+  p.setInternalSimFlags(1)
 p.stepSimulation()
 if verbose:
-	p.setInternalSimFlags(0)
-	print("contact points restored=")
-	for q in p.getContactPoints():
-		print(q)
-for i in range (numSteps2):
-	p.stepSimulation()
-
-	
-file = open("restoreFile.txt","w")
+  p.setInternalSimFlags(0)
+  print("contact points restored=")
+  for q in p.getContactPoints():
+    print(q)
+for i in range(numSteps2):
+  p.stepSimulation()
+
+file = open("restoreFile.txt", "w")
 dumpStateToFile(file)
-file.close() 
+file.close()
 
 p.restoreState(stateId)
 if verbose:
-	p.setInternalSimFlags(1)
+  p.setInternalSimFlags(1)
 p.stepSimulation()
 if verbose:
-	p.setInternalSimFlags(0)
-	print("contact points restored=")
-	for q in p.getContactPoints():
-		print(q)
-for i in range (numSteps2):
-	p.stepSimulation()
-
-	
-file = open("restoreFile2.txt","w")
+  p.setInternalSimFlags(0)
+  print("contact points restored=")
+  for q in p.getContactPoints():
+    print(q)
+for i in range(numSteps2):
+  p.stepSimulation()
+
+file = open("restoreFile2.txt", "w")
 dumpStateToFile(file)
-file.close() 
+file.close()
 
-file1 = open("saveFile.txt","r") 
-file2 = open("restoreFile.txt","r") 
-compareFiles(file1,file2)
+file1 = open("saveFile.txt", "r")
+file2 = open("restoreFile.txt", "r")
+compareFiles(file1, file2)
 file1.close()
 file2.close()
 
-file1 = open("saveFile.txt","r") 
-file2 = open("restoreFile2.txt","r") 
-compareFiles(file1,file2)
+file1 = open("saveFile.txt", "r")
+file2 = open("restoreFile2.txt", "r")
+compareFiles(file1, file2)
 file1.close()
-file2.close()        
+file2.close()
 
 p.stopStateLogging(logId)
 
 #while (p.getConnectionInfo()["isConnected"]):
 #	time.sleep(1)
-
diff --git a/examples/pybullet/examples/segmask_linkindex.py b/examples/pybullet/examples/segmask_linkindex.py
index 0c6ad2ff7..a32fb037f 100644
--- a/examples/pybullet/examples/segmask_linkindex.py
+++ b/examples/pybullet/examples/segmask_linkindex.py
@@ -1,42 +1,41 @@
 import pybullet as p
 p.connect(p.GUI)
-r2d2 = p.loadURDF("r2d2.urdf",[0,0,1])
-for l in range (p.getNumJoints(r2d2)):
-	print(p.getJointInfo(r2d2,l))
+r2d2 = p.loadURDF("r2d2.urdf", [0, 0, 1])
+for l in range(p.getNumJoints(r2d2)):
+  print(p.getJointInfo(r2d2, l))
 
-p.loadURDF("r2d2.urdf",[2,0,1])
-p.loadURDF("r2d2.urdf",[4,0,1])
+p.loadURDF("r2d2.urdf", [2, 0, 1])
+p.loadURDF("r2d2.urdf", [4, 0, 1])
 
-p.getCameraImage(320,200,flags=p.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX)
-segLinkIndex=1
+p.getCameraImage(320, 200, flags=p.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX)
+segLinkIndex = 1
 verbose = 0
 
 while (1):
-	keys = p.getKeyboardEvents()
-	#for k in keys:
-	#	print("key=",k,"state=", keys[k])
-	if ord('1') in keys:
-		state = keys[ord('1')]
-		if (state & p.KEY_WAS_RELEASED):
-			verbose = 1-verbose
-	if ord('s') in keys:
-		state = keys[ord('s')]
-		if (state & p.KEY_WAS_RELEASED):
-			segLinkIndex = 1-segLinkIndex
-			#print("segLinkIndex=",segLinkIndex)
-	flags = 0
-	if (segLinkIndex):
-		flags=p.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX
-		
-	img = p.getCameraImage(320,200,flags=flags)
-	#print(img[0],img[1])
-	seg=img[4]
-	if (verbose):
-		for pixel in seg:
-			if (pixel>=0):
-				obUid = pixel & ((1<<24)-1)
-				linkIndex = (pixel >> 24)-1
-				print("obUid=",obUid,"linkIndex=",linkIndex)
-	
-	
-	p.stepSimulation()
\ No newline at end of file
+  keys = p.getKeyboardEvents()
+  #for k in keys:
+  #	print("key=",k,"state=", keys[k])
+  if ord('1') in keys:
+    state = keys[ord('1')]
+    if (state & p.KEY_WAS_RELEASED):
+      verbose = 1 - verbose
+  if ord('s') in keys:
+    state = keys[ord('s')]
+    if (state & p.KEY_WAS_RELEASED):
+      segLinkIndex = 1 - segLinkIndex
+      #print("segLinkIndex=",segLinkIndex)
+  flags = 0
+  if (segLinkIndex):
+    flags = p.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX
+
+  img = p.getCameraImage(320, 200, flags=flags)
+  #print(img[0],img[1])
+  seg = img[4]
+  if (verbose):
+    for pixel in seg:
+      if (pixel >= 0):
+        obUid = pixel & ((1 << 24) - 1)
+        linkIndex = (pixel >> 24) - 1
+        print("obUid=", obUid, "linkIndex=", linkIndex)
+
+  p.stepSimulation()
diff --git a/examples/pybullet/examples/shiftCenterOfMass.py b/examples/pybullet/examples/shiftCenterOfMass.py
index c77629b32..c0a3f1911 100644
--- a/examples/pybullet/examples/shiftCenterOfMass.py
+++ b/examples/pybullet/examples/shiftCenterOfMass.py
@@ -6,17 +6,29 @@ p.connect(p.GUI)
 p.setAdditionalSearchPath(pybullet_data.getDataPath())
 
 p.loadURDF("plane.urdf")
-p.setGravity(0,0,-10)
-visualShift = [0,0,0]
-collisionShift = [0,0,0]
-inertiaShift = [0,0,-0.5]
+p.setGravity(0, 0, -10)
+visualShift = [0, 0, 0]
+collisionShift = [0, 0, 0]
+inertiaShift = [0, 0, -0.5]
 
-meshScale=[1,1,1]
-visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,fileName="cube.obj", rgbaColor=[1,1,1,1], specularColor=[0.4,.4,0], visualFramePosition=visualShift, meshScale=meshScale)
-collisionShapeId = p.createCollisionShape(shapeType=p.GEOM_MESH, fileName="cube.obj", collisionFramePosition=collisionShift,meshScale=meshScale)
+meshScale = [1, 1, 1]
+visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,
+                                    fileName="cube.obj",
+                                    rgbaColor=[1, 1, 1, 1],
+                                    specularColor=[0.4, .4, 0],
+                                    visualFramePosition=visualShift,
+                                    meshScale=meshScale)
+collisionShapeId = p.createCollisionShape(shapeType=p.GEOM_MESH,
+                                          fileName="cube.obj",
+                                          collisionFramePosition=collisionShift,
+                                          meshScale=meshScale)
 
-p.createMultiBody(baseMass=1,baseInertialFramePosition=inertiaShift,baseCollisionShapeIndex=collisionShapeId, baseVisualShapeIndex = visualShapeId, basePosition = [0,0,1], useMaximalCoordinates=False)
+p.createMultiBody(baseMass=1,
+                  baseInertialFramePosition=inertiaShift,
+                  baseCollisionShapeIndex=collisionShapeId,
+                  baseVisualShapeIndex=visualShapeId,
+                  basePosition=[0, 0, 1],
+                  useMaximalCoordinates=False)
 while (1):
-	p.stepSimulation()
-	time.sleep(1./240.)
-		
\ No newline at end of file
+  p.stepSimulation()
+  time.sleep(1. / 240.)
diff --git a/examples/pybullet/examples/signedDistanceField.py b/examples/pybullet/examples/signedDistanceField.py
index 6e4153ef8..c3aac70f9 100644
--- a/examples/pybullet/examples/signedDistanceField.py
+++ b/examples/pybullet/examples/signedDistanceField.py
@@ -1,15 +1,14 @@
 import pybullet as p
-import pybullet 
+import pybullet
 import time
 p.connect(p.GUI)
 p.loadURDF("toys/concave_box.urdf")
-p.setGravity(0,0,-10)
-for i in range (10):
-	p.loadURDF("sphere_1cm.urdf",[i*0.02,0,0.5])
+p.setGravity(0, 0, -10)
+for i in range(10):
+  p.loadURDF("sphere_1cm.urdf", [i * 0.02, 0, 0.5])
 p.loadURDF("duck_vhacd.urdf")
-timeStep = 1./240.
+timeStep = 1. / 240.
 p.setTimeStep(timeStep)
 while (1):
-	p.stepSimulation()
-	time.sleep(timeStep)
-	
\ No newline at end of file
+  p.stepSimulation()
+  time.sleep(timeStep)
diff --git a/examples/pybullet/examples/sleeping.py b/examples/pybullet/examples/sleeping.py
index ab5c2ba57..e9ca7f404 100644
--- a/examples/pybullet/examples/sleeping.py
+++ b/examples/pybullet/examples/sleeping.py
@@ -1,34 +1,34 @@
 import pybullet as p
 import time
-useMaximalCoordinates=False
+useMaximalCoordinates = False
 
 flags = p.URDF_ENABLE_SLEEPING
 
 p.connect(p.GUI)
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
 
-p.loadURDF("plane100.urdf",flags=flags, useMaximalCoordinates=useMaximalCoordinates)
+p.loadURDF("plane100.urdf", flags=flags, useMaximalCoordinates=useMaximalCoordinates)
 #p.loadURDF("cube_small.urdf", [0,0,0.5], flags=flags)
 r2d2 = -1
-for k in range (5):
-	for i in range (5):
-		r2d2=p.loadURDF("r2d2.urdf",[k*2,i*2,1], useMaximalCoordinates=useMaximalCoordinates, flags=p.URDF_ENABLE_CACHED_GRAPHICS_SHAPES+flags)
+for k in range(5):
+  for i in range(5):
+    r2d2 = p.loadURDF("r2d2.urdf", [k * 2, i * 2, 1],
+                      useMaximalCoordinates=useMaximalCoordinates,
+                      flags=p.URDF_ENABLE_CACHED_GRAPHICS_SHAPES + flags)
 
-		#enable sleeping: you can pass the flag during URDF loading, or do it afterwards
-		#p.changeDynamics(r2d2,-1,activationState=p.ACTIVATION_STATE_ENABLE_SLEEPING)
-		
-		
-		for j in range (p.getNumJoints(r2d2)):
-			p.setJointMotorControl2(r2d2,j,p.VELOCITY_CONTROL,targetVelocity=0)
-print("r2d2=",r2d2)
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
-timestep = 1./240.
+    #enable sleeping: you can pass the flag during URDF loading, or do it afterwards
+    #p.changeDynamics(r2d2,-1,activationState=p.ACTIVATION_STATE_ENABLE_SLEEPING)
+
+    for j in range(p.getNumJoints(r2d2)):
+      p.setJointMotorControl2(r2d2, j, p.VELOCITY_CONTROL, targetVelocity=0)
+print("r2d2=", r2d2)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
+timestep = 1. / 240.
 p.setTimeStep(timestep)
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 
 while p.isConnected():
-	p.stepSimulation()
-	time.sleep(timestep)
-	#force the object to wake up
-	p.changeDynamics(r2d2,-1,activationState=p.ACTIVATION_STATE_WAKE_UP)
-	
\ No newline at end of file
+  p.stepSimulation()
+  time.sleep(timestep)
+  #force the object to wake up
+  p.changeDynamics(r2d2, -1, activationState=p.ACTIVATION_STATE_WAKE_UP)
diff --git a/examples/pybullet/examples/snake.py b/examples/pybullet/examples/snake.py
index af2c6d512..1384a3f53 100644
--- a/examples/pybullet/examples/snake.py
+++ b/examples/pybullet/examples/snake.py
@@ -2,125 +2,141 @@ import pybullet as p
 import time
 import math
 
-
 # This simple snake logic is from some 15 year old Havok C++ demo
 # Thanks to Michael Ewert!
-  
-  
+
 p.connect(p.GUI)
 plane = p.createCollisionShape(p.GEOM_PLANE)
 
-p.createMultiBody(0,plane)
+p.createMultiBody(0, plane)
 
 useMaximalCoordinates = True
 sphereRadius = 0.25
 #colBoxId = p.createCollisionShapeArray([p.GEOM_BOX, p.GEOM_SPHERE],radii=[sphereRadius+0.03,sphereRadius+0.03], halfExtents=[[sphereRadius,sphereRadius,sphereRadius],[sphereRadius,sphereRadius,sphereRadius]])
-colBoxId = p.createCollisionShape(p.GEOM_BOX,halfExtents=[sphereRadius,sphereRadius,sphereRadius])
+colBoxId = p.createCollisionShape(p.GEOM_BOX,
+                                  halfExtents=[sphereRadius, sphereRadius, sphereRadius])
 
 mass = 1
 visualShapeId = -1
-  
-link_Masses=[]
-linkCollisionShapeIndices=[]
-linkVisualShapeIndices=[]
-linkPositions=[]
-linkOrientations=[]
-linkInertialFramePositions=[]
-linkInertialFrameOrientations=[]
-indices=[]
-jointTypes=[]
-axis=[]
-
-for i in range (36):
+
+link_Masses = []
+linkCollisionShapeIndices = []
+linkVisualShapeIndices = []
+linkPositions = []
+linkOrientations = []
+linkInertialFramePositions = []
+linkInertialFrameOrientations = []
+indices = []
+jointTypes = []
+axis = []
+
+for i in range(36):
   link_Masses.append(1)
   linkCollisionShapeIndices.append(colBoxId)
   linkVisualShapeIndices.append(-1)
-  linkPositions.append([0,sphereRadius*2.0+0.01,0])
-  linkOrientations.append([0,0,0,1])
-  linkInertialFramePositions.append([0,0,0])
-  linkInertialFrameOrientations.append([0,0,0,1])
+  linkPositions.append([0, sphereRadius * 2.0 + 0.01, 0])
+  linkOrientations.append([0, 0, 0, 1])
+  linkInertialFramePositions.append([0, 0, 0])
+  linkInertialFrameOrientations.append([0, 0, 0, 1])
   indices.append(i)
   jointTypes.append(p.JOINT_REVOLUTE)
-  axis.append([0,0,1])
-
-basePosition = [0,0,1]
-baseOrientation = [0,0,0,1]
-sphereUid = p.createMultiBody(mass,colBoxId,visualShapeId,basePosition,baseOrientation,linkMasses=link_Masses,linkCollisionShapeIndices=linkCollisionShapeIndices,linkVisualShapeIndices=linkVisualShapeIndices,linkPositions=linkPositions,linkOrientations=linkOrientations,linkInertialFramePositions=linkInertialFramePositions, linkInertialFrameOrientations=linkInertialFrameOrientations,linkParentIndices=indices,linkJointTypes=jointTypes,linkJointAxis=axis, useMaximalCoordinates=useMaximalCoordinates)
-
-p.setGravity(0,0,-10)
+  axis.append([0, 0, 1])
+
+basePosition = [0, 0, 1]
+baseOrientation = [0, 0, 0, 1]
+sphereUid = p.createMultiBody(mass,
+                              colBoxId,
+                              visualShapeId,
+                              basePosition,
+                              baseOrientation,
+                              linkMasses=link_Masses,
+                              linkCollisionShapeIndices=linkCollisionShapeIndices,
+                              linkVisualShapeIndices=linkVisualShapeIndices,
+                              linkPositions=linkPositions,
+                              linkOrientations=linkOrientations,
+                              linkInertialFramePositions=linkInertialFramePositions,
+                              linkInertialFrameOrientations=linkInertialFrameOrientations,
+                              linkParentIndices=indices,
+                              linkJointTypes=jointTypes,
+                              linkJointAxis=axis,
+                              useMaximalCoordinates=useMaximalCoordinates)
+
+p.setGravity(0, 0, -10)
 p.setRealTimeSimulation(0)
 
-anistropicFriction = [1,0.01,0.01]
-p.changeDynamics(sphereUid,-1,lateralFriction=2,anisotropicFriction=anistropicFriction)
+anistropicFriction = [1, 0.01, 0.01]
+p.changeDynamics(sphereUid, -1, lateralFriction=2, anisotropicFriction=anistropicFriction)
 p.getNumJoints(sphereUid)
-for i in range (p.getNumJoints(sphereUid)):
-  p.getJointInfo(sphereUid,i)
-  p.changeDynamics(sphereUid,i,lateralFriction=2,anisotropicFriction=anistropicFriction)
-  
-dt = 1./240.
-SNAKE_NORMAL_PERIOD=0.1#1.5
+for i in range(p.getNumJoints(sphereUid)):
+  p.getJointInfo(sphereUid, i)
+  p.changeDynamics(sphereUid, i, lateralFriction=2, anisotropicFriction=anistropicFriction)
+
+dt = 1. / 240.
+SNAKE_NORMAL_PERIOD = 0.1  #1.5
 m_wavePeriod = SNAKE_NORMAL_PERIOD
 
-m_waveLength=4
-m_wavePeriod=1.5
-m_waveAmplitude=0.4
+m_waveLength = 4
+m_wavePeriod = 1.5
+m_waveAmplitude = 0.4
 m_waveFront = 0.0
 #our steering value
 m_steering = 0.0
-m_segmentLength = sphereRadius*2.0
+m_segmentLength = sphereRadius * 2.0
 forward = 0
 
 while (1):
   keys = p.getKeyboardEvents()
-  for k,v in keys.items():
-    
-    if (k == p.B3G_RIGHT_ARROW and (v&p.KEY_WAS_TRIGGERED)):
+  for k, v in keys.items():
+
+    if (k == p.B3G_RIGHT_ARROW and (v & p.KEY_WAS_TRIGGERED)):
       m_steering = -.2
-    if (k == p.B3G_RIGHT_ARROW and (v&p.KEY_WAS_RELEASED)):
+    if (k == p.B3G_RIGHT_ARROW and (v & p.KEY_WAS_RELEASED)):
       m_steering = 0
-    if (k == p.B3G_LEFT_ARROW and (v&p.KEY_WAS_TRIGGERED)):
+    if (k == p.B3G_LEFT_ARROW and (v & p.KEY_WAS_TRIGGERED)):
       m_steering = .2
-    if (k == p.B3G_LEFT_ARROW and (v&p.KEY_WAS_RELEASED)):
+    if (k == p.B3G_LEFT_ARROW and (v & p.KEY_WAS_RELEASED)):
       m_steering = 0
-    
+
   amp = 0.2
   offset = 0.6
   numMuscles = p.getNumJoints(sphereUid)
   scaleStart = 1.0
 
-  #start of the snake with smaller waves.  
+  #start of the snake with smaller waves.
   #I think starting the wave at the tail would work better ( while it still goes from head to tail )
-  if( m_waveFront < m_segmentLength*4.0 ):
-    scaleStart = m_waveFront/(m_segmentLength*4.0)
+  if (m_waveFront < m_segmentLength * 4.0):
+    scaleStart = m_waveFront / (m_segmentLength * 4.0)
 
-  segment = numMuscles-1
+  segment = numMuscles - 1
 
   #we simply move a sin wave down the body of the snake.
   #this snake may be going backwards, but who can tell ;)
-  for joint in range (p.getNumJoints(sphereUid)):
-    segment = joint#numMuscles-1-joint
+  for joint in range(p.getNumJoints(sphereUid)):
+    segment = joint  #numMuscles-1-joint
     #map segment to phase
-    phase = (m_waveFront - (segment+1)*m_segmentLength)/ m_waveLength
+    phase = (m_waveFront - (segment + 1) * m_segmentLength) / m_waveLength
     phase -= math.floor(phase)
     phase *= math.pi * 2.0
 
     #map phase to curvature
-    targetPos = math.sin( phase ) * scaleStart * m_waveAmplitude
+    targetPos = math.sin(phase) * scaleStart * m_waveAmplitude
 
     #// steer snake by squashing +ve or -ve side of sin curve
-    if( m_steering > 0 and targetPos < 0 ):
-      targetPos *= 1.0/( 1.0 + m_steering )
-      
-    if( m_steering < 0 and targetPos > 0 ):
-     targetPos *= 1.0/( 1.0 - m_steering )
-    
+    if (m_steering > 0 and targetPos < 0):
+      targetPos *= 1.0 / (1.0 + m_steering)
+
+    if (m_steering < 0 and targetPos > 0):
+      targetPos *= 1.0 / (1.0 - m_steering)
+
     #set our motor
-    p.setJointMotorControl2(sphereUid,joint,p.POSITION_CONTROL,targetPosition=targetPos+m_steering,force=30)
+    p.setJointMotorControl2(sphereUid,
+                            joint,
+                            p.POSITION_CONTROL,
+                            targetPosition=targetPos + m_steering,
+                            force=30)
 
   #wave keeps track of where the wave is in time
-  m_waveFront += dt/m_wavePeriod * m_waveLength;
+  m_waveFront += dt / m_wavePeriod * m_waveLength
   p.stepSimulation()
 
-  
   time.sleep(dt)
-  
\ No newline at end of file
diff --git a/examples/pybullet/examples/switchConstraintSolver.py b/examples/pybullet/examples/switchConstraintSolver.py
index 3ed0cfb85..1c44391f7 100644
--- a/examples/pybullet/examples/switchConstraintSolver.py
+++ b/examples/pybullet/examples/switchConstraintSolver.py
@@ -3,29 +3,28 @@ import time
 
 p.connect(p.GUI)
 #p.setPhysicsEngineParameter(constraintSolverType=p.CONSTRAINT_SOLVER_LCP_PGS, globalCFM = 0.0001)
-p.setPhysicsEngineParameter(constraintSolverType=p.CONSTRAINT_SOLVER_LCP_DANTZIG, globalCFM = 0.000001)
+p.setPhysicsEngineParameter(constraintSolverType=p.CONSTRAINT_SOLVER_LCP_DANTZIG,
+                            globalCFM=0.000001)
 #p.setPhysicsEngineParameter(constraintSolverType=p.CONSTRAINT_SOLVER_LCP_PGS, globalCFM = 0.0001)
 
-
 p.loadURDF("plane.urdf")
 radius = 0.025
 distance = 1.86
-yaw=135
-pitch=-11
-targetPos=[0,0,0]
+yaw = 135
+pitch = -11
+targetPos = [0, 0, 0]
 
 p.setPhysicsEngineParameter(solverResidualThreshold=0.001, numSolverIterations=200)
-p.resetDebugVisualizerCamera(distance, yaw,pitch, targetPos)
+p.resetDebugVisualizerCamera(distance, yaw, pitch, targetPos)
 objectId = -1
 
-for i in range (10):
-	objectId = p.loadURDF("cube_small.urdf",[1,1,radius+i*2*radius])
+for i in range(10):
+  objectId = p.loadURDF("cube_small.urdf", [1, 1, radius + i * 2 * radius])
 
-p.changeDynamics(objectId,-1,100)
+p.changeDynamics(objectId, -1, 100)
 
-timeStep = 1./240.
-p.setGravity(0,0,-10)
+timeStep = 1. / 240.
+p.setGravity(0, 0, -10)
 while (p.isConnected()):
-	p.stepSimulation()
-	time.sleep(timeStep)
-
+  p.stepSimulation()
+  time.sleep(timeStep)
diff --git a/examples/pybullet/examples/test.py b/examples/pybullet/examples/test.py
index 02de2984f..3a49bf1ab 100644
--- a/examples/pybullet/examples/test.py
+++ b/examples/pybullet/examples/test.py
@@ -2,10 +2,9 @@
 #but running from github repo instead
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
-print ("current_dir=" + currentdir)
-parentdir = os.path.join(currentdir,"../gym")
-os.sys.path.insert(0,parentdir)
-
+print("current_dir=" + currentdir)
+parentdir = os.path.join(currentdir, "../gym")
+os.sys.path.insert(0, parentdir)
 
 import pybullet
 import pybullet_data
@@ -14,34 +13,34 @@ import time
 
 #choose connection method: GUI, DIRECT, SHARED_MEMORY
 pybullet.connect(pybullet.GUI)
-pybullet.loadURDF(os.path.join(pybullet_data.getDataPath(),"plane.urdf"),0,0,-1)
+pybullet.loadURDF(os.path.join(pybullet_data.getDataPath(), "plane.urdf"), 0, 0, -1)
 #load URDF, given a relative or absolute file+path
-obj = pybullet.loadURDF(os.path.join(pybullet_data.getDataPath(),"r2d2.urdf"))
+obj = pybullet.loadURDF(os.path.join(pybullet_data.getDataPath(), "r2d2.urdf"))
 
-posX=0
-posY=3
-posZ=2
-obj2 = pybullet.loadURDF(os.path.join(pybullet_data.getDataPath(),"kuka_iiwa/model.urdf"),posX,posY,posZ)
+posX = 0
+posY = 3
+posZ = 2
+obj2 = pybullet.loadURDF(os.path.join(pybullet_data.getDataPath(), "kuka_iiwa/model.urdf"), posX,
+                         posY, posZ)
 
 #query the number of joints of the object
 numJoints = pybullet.getNumJoints(obj)
 
-print (numJoints)
+print(numJoints)
 
 #set the gravity acceleration
-pybullet.setGravity(0,0,-9.8)
+pybullet.setGravity(0, 0, -9.8)
 
 #step the simulation for 5 seconds
 t_end = time.time() + 5
 while time.time() < t_end:
-	pybullet.stepSimulation()
-	posAndOrn = pybullet.getBasePositionAndOrientation(obj)
-	print (posAndOrn)
+  pybullet.stepSimulation()
+  posAndOrn = pybullet.getBasePositionAndOrientation(obj)
+  print(posAndOrn)
 
-print ("finished")
+print("finished")
 #remove all objects
 pybullet.resetSimulation()
 
 #disconnect from the physics server
 pybullet.disconnect()
-
diff --git a/examples/pybullet/examples/testPlugin.py b/examples/pybullet/examples/testPlugin.py
index 5bc2e22ed..c3abe25ff 100644
--- a/examples/pybullet/examples/testPlugin.py
+++ b/examples/pybullet/examples/testPlugin.py
@@ -1,10 +1,10 @@
-
 import pybullet as p
 p.connect(p.GUI)
-plugin = p.loadPlugin("D:/develop/bullet3/bin/pybullet_testplugin_vs2010_x64_debug.dll","_testPlugin")
-print("plugin=",plugin)
-p.executePluginCommand(plugin ,"r2d2.urdf", [1,2,3],[50.0,3.3])
+plugin = p.loadPlugin("D:/develop/bullet3/bin/pybullet_testplugin_vs2010_x64_debug.dll",
+                      "_testPlugin")
+print("plugin=", plugin)
+p.executePluginCommand(plugin, "r2d2.urdf", [1, 2, 3], [50.0, 3.3])
 
 while (1):
-	p.getCameraImage(320,200)
-	p.stepSimulation()
\ No newline at end of file
+  p.getCameraImage(320, 200)
+  p.stepSimulation()
diff --git a/examples/pybullet/examples/testrender.py b/examples/pybullet/examples/testrender.py
index 8be4be7ff..0332f2a6c 100644
--- a/examples/pybullet/examples/testrender.py
+++ b/examples/pybullet/examples/testrender.py
@@ -1,34 +1,31 @@
-
 #testrender.py is a bit slower than testrender_np.py: pixels are copied from C to Python one by one
 
-
-import matplotlib.pyplot as plt 
+import matplotlib.pyplot as plt
 import pybullet
 import time
-import numpy as np #to reshape for matplotlib
+import numpy as np  #to reshape for matplotlib
 
 plt.ion()
 
-img = [[1,2,3]*50]*100#np.random.rand(200, 320)
+img = [[1, 2, 3] * 50] * 100  #np.random.rand(200, 320)
 #img = [tandard_normal((50,100))
-image = plt.imshow(img,interpolation='none',animated=True,label="blah")
+image = plt.imshow(img, interpolation='none', animated=True, label="blah")
 ax = plt.gca()
 
-
 pybullet.connect(pybullet.DIRECT)
 
 #pybullet.loadPlugin("eglRendererPlugin")
-pybullet.loadURDF("plane.urdf",[0,0,-1])
+pybullet.loadURDF("plane.urdf", [0, 0, -1])
 pybullet.loadURDF("r2d2.urdf")
 
-pybullet.setGravity(0,0,-10)
-camTargetPos = [0,0,0]
-cameraUp = [0,0,1]
-cameraPos = [1,1,1]
+pybullet.setGravity(0, 0, -10)
+camTargetPos = [0, 0, 0]
+cameraUp = [0, 0, 1]
+cameraPos = [1, 1, 1]
 
 pitch = -10.0
 
-roll=0
+roll = 0
 upAxisIndex = 2
 camDistance = 4
 pixelWidth = 320
@@ -39,41 +36,47 @@ farPlane = 100
 fov = 60
 
 main_start = time.time()
-while(1): 
-  for yaw in range (0,360,10) :
+while (1):
+  for yaw in range(0, 360, 10):
     pybullet.stepSimulation()
     start = time.time()
-    viewMatrix = pybullet.computeViewMatrixFromYawPitchRoll(camTargetPos, camDistance, yaw, pitch, roll, upAxisIndex)
-    aspect = pixelWidth / pixelHeight;
-    projectionMatrix = pybullet.computeProjectionMatrixFOV(fov, aspect, nearPlane, farPlane);
-    img_arr = pybullet.getCameraImage(pixelWidth, pixelHeight, viewMatrix,projectionMatrix, shadow=1,lightDirection=[1,1,1],renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
+    viewMatrix = pybullet.computeViewMatrixFromYawPitchRoll(camTargetPos, camDistance, yaw, pitch,
+                                                            roll, upAxisIndex)
+    aspect = pixelWidth / pixelHeight
+    projectionMatrix = pybullet.computeProjectionMatrixFOV(fov, aspect, nearPlane, farPlane)
+    img_arr = pybullet.getCameraImage(pixelWidth,
+                                      pixelHeight,
+                                      viewMatrix,
+                                      projectionMatrix,
+                                      shadow=1,
+                                      lightDirection=[1, 1, 1],
+                                      renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
     stop = time.time()
-    print ("renderImage %f" % (stop - start))
+    print("renderImage %f" % (stop - start))
 
-    w=img_arr[0] #width of the image, in pixels
-    h=img_arr[1] #height of the image, in pixels
-    rgb=img_arr[2] #color data RGB
-    dep=img_arr[3] #depth data
+    w = img_arr[0]  #width of the image, in pixels
+    h = img_arr[1]  #height of the image, in pixels
+    rgb = img_arr[2]  #color data RGB
+    dep = img_arr[3]  #depth data
     #print(rgb)
-    print ('width = %d height = %d' % (w,h))
+    print('width = %d height = %d' % (w, h))
 
     #note that sending the data using imshow to matplotlib is really slow, so we use set_data
 
     #plt.imshow(rgb,interpolation='none')
 
-
     #reshape is needed
     np_img_arr = np.reshape(rgb, (h, w, 4))
-    np_img_arr = np_img_arr*(1./255.)
+    np_img_arr = np_img_arr * (1. / 255.)
 
     image.set_data(np_img_arr)
     ax.plot([0])
     #plt.draw()
     #plt.show()
     plt.pause(0.01)
-    
+
 main_stop = time.time()
 
-print ("Total time %f" % (main_stop - main_start))
+print("Total time %f" % (main_stop - main_start))
 
 pybullet.resetSimulation()
diff --git a/examples/pybullet/examples/testrender_egl.py b/examples/pybullet/examples/testrender_egl.py
index 0625a363a..3c9727b1a 100644
--- a/examples/pybullet/examples/testrender_egl.py
+++ b/examples/pybullet/examples/testrender_egl.py
@@ -15,27 +15,27 @@ plt.ion()
 
 img = np.random.rand(200, 320)
 #img = [tandard_normal((50,100))
-image = plt.imshow(img,interpolation='none',animated=True,label="blah")
+image = plt.imshow(img, interpolation='none', animated=True, label="blah")
 ax = plt.gca()
-    
+
 pybullet.connect(pybullet.DIRECT)
 
 egl = pkgutil.get_loader('eglRenderer')
 if (egl):
-	pybullet.loadPlugin(egl.get_filename(), "_eglRendererPlugin")
+  pybullet.loadPlugin(egl.get_filename(), "_eglRendererPlugin")
 else:
-	pybullet.loadPlugin("eglRendererPlugin")
-pybullet.loadURDF("plane.urdf",[0,0,-1])
+  pybullet.loadPlugin("eglRendererPlugin")
+pybullet.loadURDF("plane.urdf", [0, 0, -1])
 pybullet.loadURDF("r2d2.urdf")
 
-camTargetPos = [0,0,0]
-cameraUp = [0,0,1]
-cameraPos = [1,1,1]
-pybullet.setGravity(0,0,-10)
+camTargetPos = [0, 0, 0]
+cameraUp = [0, 0, 1]
+cameraPos = [1, 1, 1]
+pybullet.setGravity(0, 0, -10)
 
 pitch = -10.0
 
-roll=0
+roll = 0
 upAxisIndex = 2
 camDistance = 4
 pixelWidth = 320
@@ -47,30 +47,37 @@ fov = 60
 
 main_start = time.time()
 while (1):
-  for yaw in range (0,360,10):
+  for yaw in range(0, 360, 10):
     pybullet.stepSimulation()
     start = time.time()
-    
-    viewMatrix = pybullet.computeViewMatrixFromYawPitchRoll(camTargetPos, camDistance, yaw, pitch, roll, upAxisIndex)
-    aspect = pixelWidth / pixelHeight;
-    projectionMatrix = pybullet.computeProjectionMatrixFOV(fov, aspect, nearPlane, farPlane);
-    img_arr = pybullet.getCameraImage(pixelWidth, pixelHeight, viewMatrix,projectionMatrix, shadow=1,lightDirection=[1,1,1],renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
+
+    viewMatrix = pybullet.computeViewMatrixFromYawPitchRoll(camTargetPos, camDistance, yaw, pitch,
+                                                            roll, upAxisIndex)
+    aspect = pixelWidth / pixelHeight
+    projectionMatrix = pybullet.computeProjectionMatrixFOV(fov, aspect, nearPlane, farPlane)
+    img_arr = pybullet.getCameraImage(pixelWidth,
+                                      pixelHeight,
+                                      viewMatrix,
+                                      projectionMatrix,
+                                      shadow=1,
+                                      lightDirection=[1, 1, 1],
+                                      renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
     stop = time.time()
-    print ("renderImage %f" % (stop - start))
+    print("renderImage %f" % (stop - start))
 
-    w=img_arr[0] #width of the image, in pixels
-    h=img_arr[1] #height of the image, in pixels
-    rgb=img_arr[2] #color data RGB
-    dep=img_arr[3] #depth data
+    w = img_arr[0]  #width of the image, in pixels
+    h = img_arr[1]  #height of the image, in pixels
+    rgb = img_arr[2]  #color data RGB
+    dep = img_arr[3]  #depth data
 
-    print ('width = %d height = %d' % (w,h))
+    print('width = %d height = %d' % (w, h))
 
     #note that sending the data to matplotlib is really slow
 
     #reshape is not needed
     np_img_arr = np.reshape(rgb, (h, w, 4))
-    np_img_arr = np_img_arr*(1./255.)
-    
+    np_img_arr = np_img_arr * (1. / 255.)
+
     #show
     #plt.imshow(np_img_arr,interpolation='none',extent=(0,1600,0,1200))
     #image = plt.imshow(np_img_arr,interpolation='none',animated=True,label="blah")
@@ -81,10 +88,9 @@ while (1):
     #plt.show()
     plt.pause(0.01)
     #image.draw()
-    
 
 main_stop = time.time()
 
-print ("Total time %f" % (main_stop - main_start))
+print("Total time %f" % (main_stop - main_start))
 
 pybullet.resetSimulation()
diff --git a/examples/pybullet/examples/testrender_np.py b/examples/pybullet/examples/testrender_np.py
index 814e7e0d0..d32edd09e 100644
--- a/examples/pybullet/examples/testrender_np.py
+++ b/examples/pybullet/examples/testrender_np.py
@@ -1,4 +1,3 @@
-
 #make sure to compile pybullet with PYBULLET_USE_NUMPY enabled
 #otherwise use testrender.py (slower but compatible without numpy)
 #you can also use GUI mode, for faster OpenGL rendering (instead of TinyRender CPU)
@@ -8,27 +7,26 @@ import matplotlib.pyplot as plt
 import pybullet
 import time
 
-
 plt.ion()
 
 img = np.random.rand(200, 320)
 #img = [tandard_normal((50,100))
-image = plt.imshow(img,interpolation='none',animated=True,label="blah")
+image = plt.imshow(img, interpolation='none', animated=True, label="blah")
 ax = plt.gca()
-    
+
 #pybullet.connect(pybullet.GUI)
 pybullet.connect(pybullet.DIRECT)
-pybullet.loadURDF("plane.urdf",[0,0,-1])
+pybullet.loadURDF("plane.urdf", [0, 0, -1])
 pybullet.loadURDF("r2d2.urdf")
 
-camTargetPos = [0,0,0]
-cameraUp = [0,0,1]
-cameraPos = [1,1,1]
-pybullet.setGravity(0,0,-10)
+camTargetPos = [0, 0, 0]
+cameraUp = [0, 0, 1]
+cameraPos = [1, 1, 1]
+pybullet.setGravity(0, 0, -10)
 
 pitch = -10.0
 
-roll=0
+roll = 0
 upAxisIndex = 2
 camDistance = 4
 pixelWidth = 320
@@ -40,30 +38,37 @@ fov = 60
 
 main_start = time.time()
 while (1):
-  for yaw in range (0,360,10):
+  for yaw in range(0, 360, 10):
     pybullet.stepSimulation()
     start = time.time()
-    
-    viewMatrix = pybullet.computeViewMatrixFromYawPitchRoll(camTargetPos, camDistance, yaw, pitch, roll, upAxisIndex)
-    aspect = pixelWidth / pixelHeight;
-    projectionMatrix = pybullet.computeProjectionMatrixFOV(fov, aspect, nearPlane, farPlane);
-    img_arr = pybullet.getCameraImage(pixelWidth, pixelHeight, viewMatrix,projectionMatrix, shadow=1,lightDirection=[1,1,1],renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
+
+    viewMatrix = pybullet.computeViewMatrixFromYawPitchRoll(camTargetPos, camDistance, yaw, pitch,
+                                                            roll, upAxisIndex)
+    aspect = pixelWidth / pixelHeight
+    projectionMatrix = pybullet.computeProjectionMatrixFOV(fov, aspect, nearPlane, farPlane)
+    img_arr = pybullet.getCameraImage(pixelWidth,
+                                      pixelHeight,
+                                      viewMatrix,
+                                      projectionMatrix,
+                                      shadow=1,
+                                      lightDirection=[1, 1, 1],
+                                      renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
     stop = time.time()
-    print ("renderImage %f" % (stop - start))
+    print("renderImage %f" % (stop - start))
 
-    w=img_arr[0] #width of the image, in pixels
-    h=img_arr[1] #height of the image, in pixels
-    rgb=img_arr[2] #color data RGB
-    dep=img_arr[3] #depth data
+    w = img_arr[0]  #width of the image, in pixels
+    h = img_arr[1]  #height of the image, in pixels
+    rgb = img_arr[2]  #color data RGB
+    dep = img_arr[3]  #depth data
 
-    print ('width = %d height = %d' % (w,h))
+    print('width = %d height = %d' % (w, h))
 
     #note that sending the data to matplotlib is really slow
 
     #reshape is needed
     np_img_arr = np.reshape(rgb, (h, w, 4))
-    np_img_arr = np_img_arr*(1./255.)
-    
+    np_img_arr = np_img_arr * (1. / 255.)
+
     #show
     #plt.imshow(np_img_arr,interpolation='none',extent=(0,1600,0,1200))
     #image = plt.imshow(np_img_arr,interpolation='none',animated=True,label="blah")
@@ -74,10 +79,9 @@ while (1):
     #plt.show()
     plt.pause(0.01)
     #image.draw()
-    
 
 main_stop = time.time()
 
-print ("Total time %f" % (main_stop - main_start))
+print("Total time %f" % (main_stop - main_start))
 
 pybullet.resetSimulation()
diff --git a/examples/pybullet/examples/transparent.py b/examples/pybullet/examples/transparent.py
index 6e66ad982..92a95d44a 100644
--- a/examples/pybullet/examples/transparent.py
+++ b/examples/pybullet/examples/transparent.py
@@ -2,18 +2,21 @@ import pybullet as p
 import time
 p.connect(p.GUI)
 p.loadURDF("plane.urdf")
-sphereUid = p.loadURDF("sphere_transparent.urdf",[0,0,2])
+sphereUid = p.loadURDF("sphere_transparent.urdf", [0, 0, 2])
 
-redSlider = p.addUserDebugParameter("red",0,1,1)
-greenSlider = p.addUserDebugParameter("green",0,1,0)
-blueSlider = p.addUserDebugParameter("blue",0,1,0)
-alphaSlider = p.addUserDebugParameter("alpha",0,1,0.5)
+redSlider = p.addUserDebugParameter("red", 0, 1, 1)
+greenSlider = p.addUserDebugParameter("green", 0, 1, 0)
+blueSlider = p.addUserDebugParameter("blue", 0, 1, 0)
+alphaSlider = p.addUserDebugParameter("alpha", 0, 1, 0.5)
 
 while (1):
-	red = p.readUserDebugParameter(redSlider)
-	green = p.readUserDebugParameter(greenSlider)
-	blue = p.readUserDebugParameter(blueSlider)
-	alpha = p.readUserDebugParameter(alphaSlider)
-	p.changeVisualShape(sphereUid,-1,rgbaColor=[red,green,blue,alpha])
-	p.getCameraImage(320,200,flags=p.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX, renderer=p.ER_BULLET_HARDWARE_OPENGL)
-	time.sleep(0.01)
+  red = p.readUserDebugParameter(redSlider)
+  green = p.readUserDebugParameter(greenSlider)
+  blue = p.readUserDebugParameter(blueSlider)
+  alpha = p.readUserDebugParameter(alphaSlider)
+  p.changeVisualShape(sphereUid, -1, rgbaColor=[red, green, blue, alpha])
+  p.getCameraImage(320,
+                   200,
+                   flags=p.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX,
+                   renderer=p.ER_BULLET_HARDWARE_OPENGL)
+  time.sleep(0.01)
diff --git a/examples/pybullet/examples/urdfEditor.py b/examples/pybullet/examples/urdfEditor.py
index 7d728d054..0b5404318 100644
--- a/examples/pybullet/examples/urdfEditor.py
+++ b/examples/pybullet/examples/urdfEditor.py
@@ -2,28 +2,22 @@ import pybullet as p
 import time
 from pybullet_utils import urdfEditor
 
-
-
-      
-      
 ##########################################
 org2 = p.connect(p.DIRECT)
 org = p.connect(p.SHARED_MEMORY)
-if (org<0):
-	org = p.connect(p.DIRECT)
-	
+if (org < 0):
+  org = p.connect(p.DIRECT)
+
 gui = p.connect(p.GUI)
 
 p.resetSimulation(physicsClientId=org)
 
 #door.urdf, hinge.urdf, duck_vhacd.urdf, r2d2.urdf, quadruped/quadruped.urdf
 
-
-
 mb = p.loadURDF("r2d2.urdf", flags=p.URDF_USE_IMPLICIT_CYLINDER, physicsClientId=org)
-for i in range(p.getNumJoints(mb,physicsClientId=org)):
-	p.setJointMotorControl2(mb,i,p.VELOCITY_CONTROL,force=0,physicsClientId=org)
-	
+for i in range(p.getNumJoints(mb, physicsClientId=org)):
+  p.setJointMotorControl2(mb, i, p.VELOCITY_CONTROL, force=0, physicsClientId=org)
+
 #print("numJoints:",p.getNumJoints(mb,physicsClientId=org))
 
 #print("base name:",p.getBodyInfo(mb,physicsClientId=org))
@@ -33,32 +27,33 @@ for i in range(p.getNumJoints(mb,physicsClientId=org)):
 #	print("linkState(",i,"):",p.getLinkState(mb,i,physicsClientId=org))
 
 parser = urdfEditor.UrdfEditor()
-parser.initializeFromBulletBody(mb,physicsClientId=org)
+parser.initializeFromBulletBody(mb, physicsClientId=org)
 parser.saveUrdf("test.urdf")
 
 if (1):
-	print("\ncreatingMultiBody...................n")
-
-	obUid = parser.createMultiBody(physicsClientId=gui)
-
-	parser2 = urdfEditor.UrdfEditor()
-	print("\n###########################################\n")
-	
-	parser2.initializeFromBulletBody(obUid,physicsClientId=gui)
-	parser2.saveUrdf("test2.urdf")
+  print("\ncreatingMultiBody...................n")
 
+  obUid = parser.createMultiBody(physicsClientId=gui)
 
-	for i in range (p.getNumJoints(obUid, physicsClientId=gui)):
-		p.setJointMotorControl2(obUid,i,p.VELOCITY_CONTROL,targetVelocity=0,force=1,physicsClientId=gui)
-		print(p.getJointInfo(obUid,i,physicsClientId=gui))
+  parser2 = urdfEditor.UrdfEditor()
+  print("\n###########################################\n")
 
+  parser2.initializeFromBulletBody(obUid, physicsClientId=gui)
+  parser2.saveUrdf("test2.urdf")
 
-	parser=0
+  for i in range(p.getNumJoints(obUid, physicsClientId=gui)):
+    p.setJointMotorControl2(obUid,
+                            i,
+                            p.VELOCITY_CONTROL,
+                            targetVelocity=0,
+                            force=1,
+                            physicsClientId=gui)
+    print(p.getJointInfo(obUid, i, physicsClientId=gui))
 
-p.setRealTimeSimulation(1,physicsClientId=gui)
+  parser = 0
 
+p.setRealTimeSimulation(1, physicsClientId=gui)
 
 while (p.getConnectionInfo(physicsClientId=gui)["isConnected"]):
-	p.stepSimulation(physicsClientId=gui)
-	time.sleep(0.01)
-		
+  p.stepSimulation(physicsClientId=gui)
+  time.sleep(0.01)
diff --git a/examples/pybullet/examples/userData.py b/examples/pybullet/examples/userData.py
index 139f3186c..acfde4dba 100644
--- a/examples/pybullet/examples/userData.py
+++ b/examples/pybullet/examples/userData.py
@@ -2,109 +2,104 @@ import pybullet as pb
 import time
 from pybullet_utils import bullet_client
 
-
 server = bullet_client.BulletClient(connection_mode=pb.SHARED_MEMORY_SERVER)
 
-
-print ("Connecting to bullet server")
+print("Connecting to bullet server")
 CONNECTION_METHOD = pb.SHARED_MEMORY
 client = bullet_client.BulletClient(connection_mode=CONNECTION_METHOD)
 
-
 PLANE_PATH = "plane.urdf"
 client.loadURDF(PLANE_PATH)
 
 client.setGravity(0, 0, -10)
 
-print ("Adding plane object")
+print("Adding plane object")
 plane_id = client.loadURDF(PLANE_PATH)
-print ("Plane ID: %s" % plane_id)
+print("Plane ID: %s" % plane_id)
 
-print ("Adding user data to plane")
+print("Adding user data to plane")
 MyKey1 = client.addUserData(plane_id, "MyKey1", "MyValue1")
 MyKey2 = client.addUserData(plane_id, "MyKey2", "MyValue2")
 MyKey3 = client.addUserData(plane_id, "MyKey3", "MyValue3")
 MyKey4 = client.addUserData(plane_id, "MyKey4", "MyValue4")
 
-print ("Retrieving cached user data")
-print (client.getUserData(MyKey1))
-print (client.getUserData(MyKey2))
-print (client.getUserData(MyKey3))
-print (client.getUserData(MyKey4))
+print("Retrieving cached user data")
+print(client.getUserData(MyKey1))
+print(client.getUserData(MyKey2))
+print(client.getUserData(MyKey3))
+print(client.getUserData(MyKey4))
 
-print ("Disconnecting")
+print("Disconnecting")
 del client
 
-print ("Reconnecting")
+print("Reconnecting")
 client = bullet_client.BulletClient(connection_mode=CONNECTION_METHOD)
 
-print ("Retrieving synced user data")
-print (client.getUserData(MyKey1))
-print (client.getUserData(MyKey2))
-print (client.getUserData(MyKey3))
-print (client.getUserData(MyKey4))
+print("Retrieving synced user data")
+print(client.getUserData(MyKey1))
+print(client.getUserData(MyKey2))
+print(client.getUserData(MyKey3))
+print(client.getUserData(MyKey4))
 
-print ("Number of user data entries: %s" % client.getNumUserData(plane_id))
+print("Number of user data entries: %s" % client.getNumUserData(plane_id))
 
-print ("Overriding user data")
+print("Overriding user data")
 client.addUserData(plane_id, "MyKey1", "MyNewValue")
 
-print ("Cached overridden data")
-print (client.getUserData(MyKey1))
+print("Cached overridden data")
+print(client.getUserData(MyKey1))
 
-
-print ("Disconnecting")
+print("Disconnecting")
 del client
 
-print ("Reconnecting")
+print("Reconnecting")
 client = bullet_client.BulletClient(connection_mode=CONNECTION_METHOD)
 
+print("Synced overridden data")
+print(client.getUserData(MyKey1))
 
-print ("Synced overridden data")
-print (client.getUserData(MyKey1))
-
-print ("Getting user data ID")
-print ("Retrieved ID: %s, ID retrieved from addUserData: %s"  % (client.getUserDataId(plane_id, "MyKey2"), MyKey2))
+print("Getting user data ID")
+print("Retrieved ID: %s, ID retrieved from addUserData: %s" %
+      (client.getUserDataId(plane_id, "MyKey2"), MyKey2))
 
-print ("Removing user data")
+print("Removing user data")
 client.removeUserData(MyKey2)
 
-print ("Retrieving cached removed data")
-print (client.getUserData(MyKey2))
+print("Retrieving cached removed data")
+print(client.getUserData(MyKey2))
 
-print ("Syncing")
+print("Syncing")
 client.syncUserData()
 
-print ("Retrieving removed removed data")
-print (client.getUserData(MyKey2))
+print("Retrieving removed removed data")
+print(client.getUserData(MyKey2))
 
-print ("Iterating over all user data entries and printing results")
+print("Iterating over all user data entries and printing results")
 for i in range(client.getNumUserData(plane_id)):
   userDataId, key, bodyId, linkIndex, visualShapeIndex = client.getUserDataInfo(plane_id, i)
-  print ("Info: (%s, %s, %s, %s, %s)" % (userDataId, key, bodyId, linkIndex, visualShapeIndex))
-  print ("Value: %s" % client.getUserData(userDataId))
+  print("Info: (%s, %s, %s, %s, %s)" % (userDataId, key, bodyId, linkIndex, visualShapeIndex))
+  print("Value: %s" % client.getUserData(userDataId))
 
-print ("Removing body")
+print("Removing body")
 client.removeBody(plane_id)
 
-print ("Retrieving user data")
-print (client.getUserData(MyKey1))
-print (client.getUserData(MyKey3))
-print (client.getUserData(MyKey4))
+print("Retrieving user data")
+print(client.getUserData(MyKey1))
+print(client.getUserData(MyKey3))
+print(client.getUserData(MyKey4))
 
-print ("Syncing")
+print("Syncing")
 client.syncUserData()
 
-print ("Retrieving user data")
-print (client.getUserData(MyKey1))
-print (client.getUserData(MyKey3))
-print (client.getUserData(MyKey4))
+print("Retrieving user data")
+print(client.getUserData(MyKey1))
+print(client.getUserData(MyKey3))
+print(client.getUserData(MyKey4))
 
 plane_id2 = client.loadURDF(PLANE_PATH)
-print ("Plane1: %s, plane2: %s" % (plane_id, plane_id2))
-
-print ("Retrieving user data")
-print (client.getUserData(MyKey1))
-print (client.getUserData(MyKey3))
-print (client.getUserData(MyKey4))
+print("Plane1: %s, plane2: %s" % (plane_id, plane_id2))
 
+print("Retrieving user data")
+print(client.getUserData(MyKey1))
+print(client.getUserData(MyKey3))
+print(client.getUserData(MyKey4))
diff --git a/examples/pybullet/examples/vrEvent.py b/examples/pybullet/examples/vrEvent.py
index 790d6881b..562b37cc0 100644
--- a/examples/pybullet/examples/vrEvent.py
+++ b/examples/pybullet/examples/vrEvent.py
@@ -6,79 +6,83 @@
 import pybullet as p
 
 CONTROLLER_ID = 0
-POSITION=1
-ORIENTATION=2
-NUM_MOVE_EVENTS=5
-BUTTONS=6
-ANALOG_AXIS=8
+POSITION = 1
+ORIENTATION = 2
+NUM_MOVE_EVENTS = 5
+BUTTONS = 6
+ANALOG_AXIS = 8
 
 #assume that the VR physics server is already started before
 c = p.connect(p.SHARED_MEMORY)
 print(c)
-if (c<0):
-		p.connect(p.GUI)
+if (c < 0):
+  p.connect(p.GUI)
 
-p.setInternalSimFlags(0)#don't load default robot assets etc
+p.setInternalSimFlags(0)  #don't load default robot assets etc
 p.resetSimulation()
 p.loadURDF("plane.urdf")
 
-prevPosition=[[0,0,0]]*p.VR_MAX_CONTROLLERS
-colors=[0.,0.5,0.5]*p.VR_MAX_CONTROLLERS
-widths = [3]*p.VR_MAX_CONTROLLERS
+prevPosition = [[0, 0, 0]] * p.VR_MAX_CONTROLLERS
+colors = [0., 0.5, 0.5] * p.VR_MAX_CONTROLLERS
+widths = [3] * p.VR_MAX_CONTROLLERS
 
 #use a few default colors
-colors[0] = [0,0,0]
-colors[1] = [0.5,0,0]
-colors[2] = [0,0.5,0]
-colors[3] = [0,0,0.5]
-colors[4] = [0.5,0.5,0.]
-colors[5] = [.5,.5,.5]
+colors[0] = [0, 0, 0]
+colors[1] = [0.5, 0, 0]
+colors[2] = [0, 0.5, 0]
+colors[3] = [0, 0, 0.5]
+colors[4] = [0.5, 0.5, 0.]
+colors[5] = [.5, .5, .5]
 
 controllerId = -1
-pt=[0,0,0]
+pt = [0, 0, 0]
 
 print("waiting for VR controller trigger")
-while (controllerId<0):
-	events = p.getVREvents()
-	for e in (events):
-		if (e[BUTTONS][33]==p.VR_BUTTON_IS_DOWN):
-			controllerId = e[CONTROLLER_ID]
-		if (e[BUTTONS][32]==p.VR_BUTTON_IS_DOWN):
-			controllerId = e[CONTROLLER_ID]
+while (controllerId < 0):
+  events = p.getVREvents()
+  for e in (events):
+    if (e[BUTTONS][33] == p.VR_BUTTON_IS_DOWN):
+      controllerId = e[CONTROLLER_ID]
+    if (e[BUTTONS][32] == p.VR_BUTTON_IS_DOWN):
+      controllerId = e[CONTROLLER_ID]
 
-print("Using controllerId="+str(controllerId))
+print("Using controllerId=" + str(controllerId))
 
 while True:
-	events = p.getVREvents(allAnalogAxes=1)
+  events = p.getVREvents(allAnalogAxes=1)
 
-	for e in (events):
-		if (e[CONTROLLER_ID]==controllerId ):
-			for a in range(10):
-				print("analog axis"+str(a)+"="+str(e[8][a]))
-		if (e[BUTTONS][33]&p.VR_BUTTON_WAS_TRIGGERED):
-			prevPosition[e[CONTROLLER_ID]] = e[POSITION]
-		if (e[BUTTONS][32]&p.VR_BUTTON_WAS_TRIGGERED):
-			widths[e[CONTROLLER_ID]]=widths[e[0]]+1
-			if (widths[e[CONTROLLER_ID]]>20):
-				widths[e[CONTROLLER_ID]] = 1
-		if (e[BUTTONS][1]&p.VR_BUTTON_WAS_TRIGGERED):
-			p.resetSimulation()
-			#p.setGravity(0,0,-10)
-			p.removeAllUserDebugItems()
-			p.loadURDF("plane.urdf")
-		if (e[BUTTONS][33]==p.VR_BUTTON_IS_DOWN):
-			pt = prevPosition[e[CONTROLLER_ID]]
-			
-			#print(prevPosition[e[0]])
-			print("e[POSITION]")
-			print(e[POSITION])
-			print("pt")
-			print(pt)
-			diff = [pt[0]-e[POSITION][0],pt[1]-e[POSITION][1],pt[2]-e[POSITION][2]]
-			lenSqr =	diff[0]*diff[0]+diff[1]*diff[1]+diff[2]*diff[2]
-			ptDistThreshold = 0.01
-			if (lenSqr>(ptDistThreshold*ptDistThreshold)):
-				p.addUserDebugLine(e[POSITION],prevPosition[e[CONTROLLER_ID]],colors[e[CONTROLLER_ID]],widths[e[CONTROLLER_ID]])
-				#p.loadURDF("cube_small.urdf",e[1])
-				colors[e[CONTROLLER_ID]] = [1-colors[e[CONTROLLER_ID]][0],1-colors[e[CONTROLLER_ID]][1],1-colors[e[CONTROLLER_ID]][2]]
-				prevPosition[e[CONTROLLER_ID]] = e[POSITION]			
\ No newline at end of file
+  for e in (events):
+    if (e[CONTROLLER_ID] == controllerId):
+      for a in range(10):
+        print("analog axis" + str(a) + "=" + str(e[8][a]))
+    if (e[BUTTONS][33] & p.VR_BUTTON_WAS_TRIGGERED):
+      prevPosition[e[CONTROLLER_ID]] = e[POSITION]
+    if (e[BUTTONS][32] & p.VR_BUTTON_WAS_TRIGGERED):
+      widths[e[CONTROLLER_ID]] = widths[e[0]] + 1
+      if (widths[e[CONTROLLER_ID]] > 20):
+        widths[e[CONTROLLER_ID]] = 1
+    if (e[BUTTONS][1] & p.VR_BUTTON_WAS_TRIGGERED):
+      p.resetSimulation()
+      #p.setGravity(0,0,-10)
+      p.removeAllUserDebugItems()
+      p.loadURDF("plane.urdf")
+    if (e[BUTTONS][33] == p.VR_BUTTON_IS_DOWN):
+      pt = prevPosition[e[CONTROLLER_ID]]
+
+      #print(prevPosition[e[0]])
+      print("e[POSITION]")
+      print(e[POSITION])
+      print("pt")
+      print(pt)
+      diff = [pt[0] - e[POSITION][0], pt[1] - e[POSITION][1], pt[2] - e[POSITION][2]]
+      lenSqr = diff[0] * diff[0] + diff[1] * diff[1] + diff[2] * diff[2]
+      ptDistThreshold = 0.01
+      if (lenSqr > (ptDistThreshold * ptDistThreshold)):
+        p.addUserDebugLine(e[POSITION], prevPosition[e[CONTROLLER_ID]], colors[e[CONTROLLER_ID]],
+                           widths[e[CONTROLLER_ID]])
+        #p.loadURDF("cube_small.urdf",e[1])
+        colors[e[CONTROLLER_ID]] = [
+            1 - colors[e[CONTROLLER_ID]][0], 1 - colors[e[CONTROLLER_ID]][1],
+            1 - colors[e[CONTROLLER_ID]][2]
+        ]
+        prevPosition[e[CONTROLLER_ID]] = e[POSITION]
diff --git a/examples/pybullet/examples/vr_kitchen_setup_vrSyncPython.py b/examples/pybullet/examples/vr_kitchen_setup_vrSyncPython.py
index 343115448..a977a6df3 100644
--- a/examples/pybullet/examples/vr_kitchen_setup_vrSyncPython.py
+++ b/examples/pybullet/examples/vr_kitchen_setup_vrSyncPython.py
@@ -2,26 +2,32 @@ import pybullet as p
 import time
 #p.connect(p.UDP,"192.168.86.100")
 
-
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	p.connect(p.GUI)
+if (cid < 0):
+  p.connect(p.GUI)
 p.resetSimulation()
 
+meshScale = [.1, .1, .01]
+shift = [0, 0, 0]
 
-
-
-meshScale = [.1,.1,.01]
-shift = [0,0,0]
-
-visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,fileName="marble_cube.obj", rgbaColor=[1,1,1,1], specularColor=[1,1,1], visualFramePosition=shift, meshScale=meshScale)
+visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,
+                                    fileName="marble_cube.obj",
+                                    rgbaColor=[1, 1, 1, 1],
+                                    specularColor=[1, 1, 1],
+                                    visualFramePosition=shift,
+                                    meshScale=meshScale)
 #collisionShapeId = p.createCollisionShape(shapeType=p.GEOM_MESH, fileName="textures/marble_cube.obj", collisionFramePosition=shift,meshScale=meshScale)
 collisionShapeId = -1
-uiCube = p.createMultiBody(baseMass=0,baseInertialFramePosition=[0,0,0],baseCollisionShapeIndex=collisionShapeId, baseVisualShapeIndex = visualShapeId, basePosition = [0,1,0], useMaximalCoordinates=True)
-
-textOrn = p.getQuaternionFromEuler([0,0,-1.5707963])
+uiCube = p.createMultiBody(baseMass=0,
+                           baseInertialFramePosition=[0, 0, 0],
+                           baseCollisionShapeIndex=collisionShapeId,
+                           baseVisualShapeIndex=visualShapeId,
+                           basePosition=[0, 1, 0],
+                           useMaximalCoordinates=True)
+
+textOrn = p.getQuaternionFromEuler([0, 0, -1.5707963])
 numLines = 1
-lines = [-1]*numLines
+lines = [-1] * numLines
 
 p.stepSimulation()
 #disable rendering during loading makes it much faster
@@ -29,65 +35,118 @@ p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
 #objects = [p.loadURDF("plane.urdf", 0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)]
 kitchenObj = p.loadSDF("kitchens/1.sdf")
 #objects = [p.loadURDF("samurai.urdf", 0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("pr2_gripper.urdf", 0.500000,0.300006,0.700000,-0.000000,-0.000000,-0.000031,1.000000)]
+objects = [
+    p.loadURDF("pr2_gripper.urdf", 0.500000, 0.300006, 0.700000, -0.000000, -0.000000, -0.000031,
+               1.000000)
+]
 pr2_gripper = objects[0]
-print ("pr2_gripper=")
-print (pr2_gripper)
-
-jointPositions=[ 0.550569, 0.000000, 0.549657, 0.000000 ]
-for jointIndex in range (p.getNumJoints(pr2_gripper)):
-	p.resetJointState(pr2_gripper,jointIndex,jointPositions[jointIndex])
-	p.setJointMotorControl2(pr2_gripper,jointIndex,p.POSITION_CONTROL,targetPosition=0,force=0)
-
-pr2_cid = p.createConstraint(pr2_gripper,-1,-1,-1,p.JOINT_FIXED,[0,0,0],[0.2,0,0],[0.500000,0.300006,0.700000])
-print ("pr2_cid")
-print (pr2_cid)
-
-pr2_cid2 = p.createConstraint(pr2_gripper,0,pr2_gripper,2,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(pr2_cid2,gearRatio=1, erp=0.5, relativePositionTarget=0.5, maxForce=3)
-
-
-
-objects = [p.loadURDF("kuka_iiwa/model_vr_limits.urdf", 1.400000,-0.200000,0.600000,0.000000,0.000000,0.000000,1.000000)]
+print("pr2_gripper=")
+print(pr2_gripper)
+
+jointPositions = [0.550569, 0.000000, 0.549657, 0.000000]
+for jointIndex in range(p.getNumJoints(pr2_gripper)):
+  p.resetJointState(pr2_gripper, jointIndex, jointPositions[jointIndex])
+  p.setJointMotorControl2(pr2_gripper, jointIndex, p.POSITION_CONTROL, targetPosition=0, force=0)
+
+pr2_cid = p.createConstraint(pr2_gripper, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0.2, 0, 0],
+                             [0.500000, 0.300006, 0.700000])
+print("pr2_cid")
+print(pr2_cid)
+
+pr2_cid2 = p.createConstraint(pr2_gripper,
+                              0,
+                              pr2_gripper,
+                              2,
+                              jointType=p.JOINT_GEAR,
+                              jointAxis=[0, 1, 0],
+                              parentFramePosition=[0, 0, 0],
+                              childFramePosition=[0, 0, 0])
+p.changeConstraint(pr2_cid2, gearRatio=1, erp=0.5, relativePositionTarget=0.5, maxForce=3)
+
+objects = [
+    p.loadURDF("kuka_iiwa/model_vr_limits.urdf", 1.400000, -0.200000, 0.600000, 0.000000, 0.000000,
+               0.000000, 1.000000)
+]
 kuka = objects[0]
-jointPositions=[ -0.000000, -0.000000, 0.000000, 1.570793, 0.000000, -1.036725, 0.000001 ]
-for jointIndex in range (p.getNumJoints(kuka)):
-	p.resetJointState(kuka,jointIndex,jointPositions[jointIndex])
-	p.setJointMotorControl2(kuka,jointIndex,p.POSITION_CONTROL,jointPositions[jointIndex],0)
-
-objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.700000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.800000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.900000,0.000000,0.000000,0.000000,1.000000)]
+jointPositions = [-0.000000, -0.000000, 0.000000, 1.570793, 0.000000, -1.036725, 0.000001]
+for jointIndex in range(p.getNumJoints(kuka)):
+  p.resetJointState(kuka, jointIndex, jointPositions[jointIndex])
+  p.setJointMotorControl2(kuka, jointIndex, p.POSITION_CONTROL, jointPositions[jointIndex], 0)
+
+objects = [
+    p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.700000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.800000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.900000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
 objects = p.loadSDF("gripper/wsg50_one_motor_gripper_new_free_base.sdf")
 kuka_gripper = objects[0]
-print ("kuka gripper=")
+print("kuka gripper=")
 print(kuka_gripper)
 
-p.resetBasePositionAndOrientation(kuka_gripper,[0.923103,-0.200000,1.250036],[-0.000000,0.964531,-0.000002,-0.263970])
-jointPositions=[ 0.000000, -0.011130, -0.206421, 0.205143, -0.009999, 0.000000, -0.010055, 0.000000 ]
-for jointIndex in range (p.getNumJoints(kuka_gripper)):
-	p.resetJointState(kuka_gripper,jointIndex,jointPositions[jointIndex])
-	p.setJointMotorControl2(kuka_gripper,jointIndex,p.POSITION_CONTROL,jointPositions[jointIndex],0)
-
-
-kuka_cid = p.createConstraint(kuka,   6,  kuka_gripper,0,p.JOINT_FIXED, [0,0,0], [0,0,0.05],[0,0,0])
-
-objects = [p.loadURDF("table/table.urdf", 1.000000,-0.200000,0.000000,0.000000,0.000000,0.707107,0.707107)]
+p.resetBasePositionAndOrientation(kuka_gripper, [0.923103, -0.200000, 1.250036],
+                                  [-0.000000, 0.964531, -0.000002, -0.263970])
+jointPositions = [
+    0.000000, -0.011130, -0.206421, 0.205143, -0.009999, 0.000000, -0.010055, 0.000000
+]
+for jointIndex in range(p.getNumJoints(kuka_gripper)):
+  p.resetJointState(kuka_gripper, jointIndex, jointPositions[jointIndex])
+  p.setJointMotorControl2(kuka_gripper, jointIndex, p.POSITION_CONTROL, jointPositions[jointIndex],
+                          0)
+
+kuka_cid = p.createConstraint(kuka, 6, kuka_gripper, 0, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0.05],
+                              [0, 0, 0])
+
+objects = [
+    p.loadURDF("table/table.urdf", 1.000000, -0.200000, 0.000000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
 #objects = [p.loadURDF("textures/table2.urdf", 1.000000,-0.200000,0.000000,0.000000,0.000000,0.707107,0.707107)]
 
-
-
-objects = [p.loadURDF("jenga/jenga.urdf", 1.300000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.200000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.100000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.000000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 0.900000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 0.800000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.300000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.200000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.100000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.000000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 0.900000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 0.800000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
 
 #objects = [p.loadURDF("teddy_vhacd.urdf", 1.050000,-0.500000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("cube_small.urdf", 0.950000,-0.100000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("sphere_small.urdf", 0.850000,-0.400000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("duck_vhacd.urdf", 0.850000,-0.400000,0.900000,0.000000,0.000000,0.707107,0.707107)]
+objects = [
+    p.loadURDF("cube_small.urdf", 0.950000, -0.100000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("sphere_small.urdf", 0.850000, -0.400000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("duck_vhacd.urdf", 0.850000, -0.400000, 0.900000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
 #bjects = p.loadSDF("kiva_shelf/model.sdf")
 #ob = objects[0]
 #p.resetBasePositionAndOrientation(ob,[0.000000,1.000000,1.204500],[0.000000,0.000000,0.000000,1.000000])
@@ -108,8 +167,7 @@ objects = [p.loadURDF("duck_vhacd.urdf", 0.850000,-0.400000,0.900000,0.000000,0.
 
 p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
 
-
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 
 ##show this for 10 seconds
 #now = time.time()
@@ -118,113 +176,126 @@ p.setGravity(0,0,-10)
 p.setRealTimeSimulation(1)
 
 CONTROLLER_ID = 0
-POSITION=1
-ORIENTATION=2
-ANALOG=3
-BUTTONS=6
+POSITION = 1
+ORIENTATION = 2
+ANALOG = 3
+BUTTONS = 6
 
 uiControllerId = -1
 
 print("waiting for VR UI controller trigger")
-while (uiControllerId<0):
-	events = p.getVREvents()
-	for e in (events):
-		if (e[BUTTONS][33]==p.VR_BUTTON_IS_DOWN):
-			uiControllerId = e[CONTROLLER_ID]
-		if (e[BUTTONS][32]==p.VR_BUTTON_IS_DOWN):
-			uiControllerId = e[CONTROLLER_ID]
-
-print("Using uiControllerId="+str(uiControllerId))
-
+while (uiControllerId < 0):
+  events = p.getVREvents()
+  for e in (events):
+    if (e[BUTTONS][33] == p.VR_BUTTON_IS_DOWN):
+      uiControllerId = e[CONTROLLER_ID]
+    if (e[BUTTONS][32] == p.VR_BUTTON_IS_DOWN):
+      uiControllerId = e[CONTROLLER_ID]
 
+print("Using uiControllerId=" + str(uiControllerId))
 
 controllerId = -1
 
 print("waiting for VR picking controller trigger")
-while (controllerId<0):
-	events = p.getVREvents()
-	for e in (events):
-		if (e[BUTTONS][33]==p.VR_BUTTON_IS_DOWN):
-			controllerId = e[CONTROLLER_ID]
-		if (e[BUTTONS][32]==p.VR_BUTTON_IS_DOWN):
-			controllerId = e[CONTROLLER_ID]
-		if (controllerId == uiControllerId):
-			controllerId = -1
-
-print("Using controllerId="+str(controllerId))
+while (controllerId < 0):
+  events = p.getVREvents()
+  for e in (events):
+    if (e[BUTTONS][33] == p.VR_BUTTON_IS_DOWN):
+      controllerId = e[CONTROLLER_ID]
+    if (e[BUTTONS][32] == p.VR_BUTTON_IS_DOWN):
+      controllerId = e[CONTROLLER_ID]
+    if (controllerId == uiControllerId):
+      controllerId = -1
+
+print("Using controllerId=" + str(controllerId))
 
 once = 1
 
 if (once):
-	logId = -1#p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "userDebugItems1.json")
-	print("logId userdebug")
-	print(logId)
-	
-	for i in range (numLines):
-		spacing = 0.01
-		textPos = [.1-(i+1)*spacing,.1,0.011]
-		text = "ABCDEFGH\nIJKLMNOPQRSTUVWXYZ\n01234567890abcdefg\n"*10
-		lines[i] = p.addUserDebugText(text,textColorRGB=[0,0,0], textSize = 0.01, textPosition = textPos,textOrientation = textOrn,parentObjectUniqueId=uiCube, parentLinkIndex = -1)
-		
-	if (once):
-		once = 0
-		if (logId and logId>0):
-			p.stopStateLogging(logId)
+  logId = -1  #p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "userDebugItems1.json")
+  print("logId userdebug")
+  print(logId)
+
+  for i in range(numLines):
+    spacing = 0.01
+    textPos = [.1 - (i + 1) * spacing, .1, 0.011]
+    text = "ABCDEFGH\nIJKLMNOPQRSTUVWXYZ\n01234567890abcdefg\n" * 10
+    lines[i] = p.addUserDebugText(text,
+                                  textColorRGB=[0, 0, 0],
+                                  textSize=0.01,
+                                  textPosition=textPos,
+                                  textOrientation=textOrn,
+                                  parentObjectUniqueId=uiCube,
+                                  parentLinkIndex=-1)
+
+  if (once):
+    once = 0
+    if (logId and logId > 0):
+      p.stopStateLogging(logId)
 
 frameNr = 0
 objectInfo = ""
 pointRay = -1
 rayLen = 100
 while (1):
-	
-	frameNr=frameNr+1
-
-	
-	for i in range (numLines):
-		spacing = 0.01
-		textPos = [.1-(i+1)*spacing,.1,0.011]
-		text = "Frame:"+str(frameNr)+"\nObject UID:"+objectInfo
-		textUid = p.addUserDebugText(text,textColorRGB=[0,0,0], textSize = 0.02, textPosition = textPos,textOrientation = textOrn,parentObjectUniqueId=uiCube, parentLinkIndex = -1, replaceItemUniqueId = lines[i])
-		lines[i] = textUid
-	
-	
-	#keep the gripper centered/symmetric
-	b = p.getJointState(pr2_gripper,2)[0]
-	p.setJointMotorControl2(pr2_gripper, 0, p.POSITION_CONTROL, targetPosition=b, force=3)
-	
-	events = p.getVREvents()
-	for e in (events):
-		if e[CONTROLLER_ID] == uiControllerId:
-			p.resetBasePositionAndOrientation(uiCube,e[POSITION], e[ORIENTATION])
-			pos = e[POSITION]
-			orn = e[ORIENTATION]
-			lineFrom = pos
-			mat = p.getMatrixFromQuaternion(orn)
-			dir = [mat[0],mat[3],mat[6]]
-			to = [pos[0]+dir[0]*rayLen,pos[1]+dir[1]*rayLen,pos[2]+dir[2]*rayLen]
-			hit = p.rayTest(lineFrom,to)
-			oldRay = pointRay
-			color = [1,1,0]
-			width = 3
-			#pointRay = p.addUserDebugLine(lineFrom,to,color,width,lifeTime=1)
-			#if (oldRay>=0):
-			#	p.removeUserDebugItem(oldRay)
-				
-			if (hit):
-				#if (hit[0][0]>=0):
-				hitObjectUid = hit[0][0]
-				linkIndex =  hit[0][1]
-				if (hitObjectUid>=0):
-					objectInfo = str(hitObjectUid)+" Link Index="+str(linkIndex)+"\nBase Name:"+p.getBodyInfo(hitObjectUid)[0].decode()+"\nBody Info:"+p.getBodyInfo(hitObjectUid)[1].decode()
-				else:
-					objectInfo="None"
-			
-		if e[CONTROLLER_ID] == controllerId:  # To make sure we only get the value for one of the remotes
-			#sync the vr pr2 gripper with the vr controller position
-			p.changeConstraint(pr2_cid, e[POSITION], e[ORIENTATION], maxForce=500)
-			relPosTarget = 1 - e[ANALOG]
-			#open/close the gripper, based on analogue
-			p.changeConstraint(pr2_cid2,gearRatio=1, erp=1, relativePositionTarget=relPosTarget, maxForce=3)
-			
-			
-			
\ No newline at end of file
+
+  frameNr = frameNr + 1
+
+  for i in range(numLines):
+    spacing = 0.01
+    textPos = [.1 - (i + 1) * spacing, .1, 0.011]
+    text = "Frame:" + str(frameNr) + "\nObject UID:" + objectInfo
+    textUid = p.addUserDebugText(text,
+                                 textColorRGB=[0, 0, 0],
+                                 textSize=0.02,
+                                 textPosition=textPos,
+                                 textOrientation=textOrn,
+                                 parentObjectUniqueId=uiCube,
+                                 parentLinkIndex=-1,
+                                 replaceItemUniqueId=lines[i])
+    lines[i] = textUid
+
+  #keep the gripper centered/symmetric
+  b = p.getJointState(pr2_gripper, 2)[0]
+  p.setJointMotorControl2(pr2_gripper, 0, p.POSITION_CONTROL, targetPosition=b, force=3)
+
+  events = p.getVREvents()
+  for e in (events):
+    if e[CONTROLLER_ID] == uiControllerId:
+      p.resetBasePositionAndOrientation(uiCube, e[POSITION], e[ORIENTATION])
+      pos = e[POSITION]
+      orn = e[ORIENTATION]
+      lineFrom = pos
+      mat = p.getMatrixFromQuaternion(orn)
+      dir = [mat[0], mat[3], mat[6]]
+      to = [pos[0] + dir[0] * rayLen, pos[1] + dir[1] * rayLen, pos[2] + dir[2] * rayLen]
+      hit = p.rayTest(lineFrom, to)
+      oldRay = pointRay
+      color = [1, 1, 0]
+      width = 3
+      #pointRay = p.addUserDebugLine(lineFrom,to,color,width,lifeTime=1)
+      #if (oldRay>=0):
+      #	p.removeUserDebugItem(oldRay)
+
+      if (hit):
+        #if (hit[0][0]>=0):
+        hitObjectUid = hit[0][0]
+        linkIndex = hit[0][1]
+        if (hitObjectUid >= 0):
+          objectInfo = str(hitObjectUid) + " Link Index=" + str(
+              linkIndex) + "\nBase Name:" + p.getBodyInfo(hitObjectUid)[0].decode(
+              ) + "\nBody Info:" + p.getBodyInfo(hitObjectUid)[1].decode()
+        else:
+          objectInfo = "None"
+
+    if e[
+        CONTROLLER_ID] == controllerId:  # To make sure we only get the value for one of the remotes
+      #sync the vr pr2 gripper with the vr controller position
+      p.changeConstraint(pr2_cid, e[POSITION], e[ORIENTATION], maxForce=500)
+      relPosTarget = 1 - e[ANALOG]
+      #open/close the gripper, based on analogue
+      p.changeConstraint(pr2_cid2,
+                         gearRatio=1,
+                         erp=1,
+                         relativePositionTarget=relPosTarget,
+                         maxForce=3)
diff --git a/examples/pybullet/examples/vr_kuka_control.py b/examples/pybullet/examples/vr_kuka_control.py
index 04c3573dd..14e67e276 100644
--- a/examples/pybullet/examples/vr_kuka_control.py
+++ b/examples/pybullet/examples/vr_kuka_control.py
@@ -10,77 +10,92 @@ kuka = 3
 kuka_gripper = 7
 POSITION = 1
 ORIENTATION = 2
-ANALOG=3
+ANALOG = 3
 BUTTONS = 6
 
-
 THRESHOLD = .5
 LOWER_LIMITS = [-.967, -2.0, -2.96, 0.19, -2.96, -2.09, -3.05]
 UPPER_LIMITS = [.96, 2.0, 2.96, 2.29, 2.96, 2.09, 3.05]
 JOINT_RANGE = [5.8, 4, 5.8, 4, 5.8, 4, 6]
 REST_POSE = [0, 0, 0, math.pi / 2, 0, -math.pi * 0.66, 0]
-JOINT_DAMP = [0.1]*10
+JOINT_DAMP = [0.1] * 10
 REST_JOINT_POS = [-0., -0., 0., 1.570793, 0., -1.036725, 0.000001]
 MAX_FORCE = 500
 KUKA_GRIPPER_REST_POS = [0., -0.011130, -0.206421, 0.205143, -0.009999, 0., -0.010055, 0.]
-KUKA_GRIPPER_CLOZ_POS = [0.0, -0.047564246423083795, 0.6855956234759611, -0.7479294372303137, 0.05054599996976922, 0.0, 0.049838105678835724, 0.0]
+KUKA_GRIPPER_CLOZ_POS = [
+    0.0, -0.047564246423083795, 0.6855956234759611, -0.7479294372303137, 0.05054599996976922, 0.0,
+    0.049838105678835724, 0.0
+]
+
 
 def euc_dist(posA, posB):
-	dist = 0.
-	for i in range(len(posA)):
-		dist += (posA[i] - posB[i]) ** 2
-	return dist
+  dist = 0.
+  for i in range(len(posA)):
+    dist += (posA[i] - posB[i])**2
+  return dist
+
 
 p.setRealTimeSimulation(1)
 
 controllers = [e[0] for e in p.getVREvents()]
 
 for j in range(p.getNumJoints(kuka_gripper)):
-	print(p.getJointInfo(kuka_gripper,j))
+  print(p.getJointInfo(kuka_gripper, j))
 while True:
 
-	events = p.getVREvents()
-	for e in (events):
-
-		# Only use one controller
-		###########################################
-		# This is important: make sure there's only one VR Controller!
-		if e[0] == controllers[0]:
-			break
-
-		sq_len = euc_dist(p.getLinkState(kuka, 6)[0], e[POSITION])
-
-		# A simplistic version of gripper control
-		#@TO-DO: Add slider for the gripper
-		
-		
-		
-		#for i in range(p.getNumJoints(kuka_gripper)):
-		i=4
-		p.setJointMotorControl2(kuka_gripper, i, p.POSITION_CONTROL, targetPosition=e[ANALOG]*0.05, force=10)
-		i=6
-		p.setJointMotorControl2(kuka_gripper, i, p.POSITION_CONTROL, targetPosition=e[ANALOG]*0.05, force=10)
-
-		
-		if sq_len < THRESHOLD * THRESHOLD:
-			eef_pos = e[POSITION]
-			eef_orn = p.getQuaternionFromEuler([0,-math.pi,0])
-			joint_pos = p.calculateInverseKinematics(kuka, 6, eef_pos, eef_orn,
-				lowerLimits=LOWER_LIMITS, upperLimits=UPPER_LIMITS, 
-				jointRanges=JOINT_RANGE, restPoses=REST_POSE, jointDamping=JOINT_DAMP)
-
-			
-			for i in range(len(joint_pos)):
-				p.setJointMotorControl2(kuka, i, p.POSITION_CONTROL, 
-					targetPosition=joint_pos[i], targetVelocity=0, positionGain=0.15, 
-					velocityGain=1.0, force=MAX_FORCE)
-	
-
-		else:
-			# Set back to original rest pose
-			for jointIndex in range(p.getNumJoints(kuka)):
-				p.setJointMotorControl2(kuka, jointIndex, p.POSITION_CONTROL, 
-					REST_JOINT_POS[jointIndex], 0)
-
-
-
+  events = p.getVREvents()
+  for e in (events):
+
+    # Only use one controller
+    ###########################################
+    # This is important: make sure there's only one VR Controller!
+    if e[0] == controllers[0]:
+      break
+
+    sq_len = euc_dist(p.getLinkState(kuka, 6)[0], e[POSITION])
+
+    # A simplistic version of gripper control
+    #@TO-DO: Add slider for the gripper
+
+    #for i in range(p.getNumJoints(kuka_gripper)):
+    i = 4
+    p.setJointMotorControl2(kuka_gripper,
+                            i,
+                            p.POSITION_CONTROL,
+                            targetPosition=e[ANALOG] * 0.05,
+                            force=10)
+    i = 6
+    p.setJointMotorControl2(kuka_gripper,
+                            i,
+                            p.POSITION_CONTROL,
+                            targetPosition=e[ANALOG] * 0.05,
+                            force=10)
+
+    if sq_len < THRESHOLD * THRESHOLD:
+      eef_pos = e[POSITION]
+      eef_orn = p.getQuaternionFromEuler([0, -math.pi, 0])
+      joint_pos = p.calculateInverseKinematics(kuka,
+                                               6,
+                                               eef_pos,
+                                               eef_orn,
+                                               lowerLimits=LOWER_LIMITS,
+                                               upperLimits=UPPER_LIMITS,
+                                               jointRanges=JOINT_RANGE,
+                                               restPoses=REST_POSE,
+                                               jointDamping=JOINT_DAMP)
+
+      for i in range(len(joint_pos)):
+        p.setJointMotorControl2(kuka,
+                                i,
+                                p.POSITION_CONTROL,
+                                targetPosition=joint_pos[i],
+                                targetVelocity=0,
+                                positionGain=0.15,
+                                velocityGain=1.0,
+                                force=MAX_FORCE)
+
+    else:
+      # Set back to original rest pose
+      for jointIndex in range(p.getNumJoints(kuka)):
+        p.setJointMotorControl2(kuka, jointIndex, p.POSITION_CONTROL, REST_JOINT_POS[jointIndex],
+                                0)
diff --git a/examples/pybullet/examples/vr_kuka_pr2_move.py b/examples/pybullet/examples/vr_kuka_pr2_move.py
index 53dbe844d..1af2bd265 100644
--- a/examples/pybullet/examples/vr_kuka_pr2_move.py
+++ b/examples/pybullet/examples/vr_kuka_pr2_move.py
@@ -7,16 +7,24 @@ pr2_gripper = 2
 pr2_cid = 1
 
 CONTROLLER_ID = 0
-POSITION=1
-ORIENTATION=2
-ANALOG=3
-BUTTONS=6
+POSITION = 1
+ORIENTATION = 2
+ANALOG = 3
+BUTTONS = 6
 
 gripper_max_joint = 0.550569
 while True:
-	events = p.getVREvents()
-	for e in (events):
-		if e[CONTROLLER_ID] == 3:  # To make sure we only get the value for one of the remotes
-			p.changeConstraint(pr2_cid, e[POSITION], e[ORIENTATION], maxForce=500)
-			p.setJointMotorControl2(pr2_gripper, 0, controlMode=p.POSITION_CONTROL,targetPosition=gripper_max_joint - e[ANALOG] * gripper_max_joint,force=1.0)
-			p.setJointMotorControl2(pr2_gripper, 2, controlMode=p.POSITION_CONTROL,targetPosition=gripper_max_joint - e[ANALOG] * gripper_max_joint,force=1.1)
\ No newline at end of file
+  events = p.getVREvents()
+  for e in (events):
+    if e[CONTROLLER_ID] == 3:  # To make sure we only get the value for one of the remotes
+      p.changeConstraint(pr2_cid, e[POSITION], e[ORIENTATION], maxForce=500)
+      p.setJointMotorControl2(pr2_gripper,
+                              0,
+                              controlMode=p.POSITION_CONTROL,
+                              targetPosition=gripper_max_joint - e[ANALOG] * gripper_max_joint,
+                              force=1.0)
+      p.setJointMotorControl2(pr2_gripper,
+                              2,
+                              controlMode=p.POSITION_CONTROL,
+                              targetPosition=gripper_max_joint - e[ANALOG] * gripper_max_joint,
+                              force=1.1)
diff --git a/examples/pybullet/examples/vr_kuka_setup.py b/examples/pybullet/examples/vr_kuka_setup.py
index cc278f8b4..f6a90bbf7 100644
--- a/examples/pybullet/examples/vr_kuka_setup.py
+++ b/examples/pybullet/examples/vr_kuka_setup.py
@@ -2,89 +2,171 @@ import pybullet as p
 import time
 #p.connect(p.UDP,"192.168.86.100")
 
-
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	p.connect(p.GUI)
+if (cid < 0):
+  p.connect(p.GUI)
 p.resetSimulation()
 #disable rendering during loading makes it much faster
 p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
-objects = [p.loadURDF("plane.urdf", 0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("samurai.urdf", 0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("pr2_gripper.urdf", 0.500000,0.300006,0.700000,-0.000000,-0.000000,-0.000031,1.000000)]
+objects = [
+    p.loadURDF("plane.urdf", 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000)
+]
+objects = [
+    p.loadURDF("samurai.urdf", 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("pr2_gripper.urdf", 0.500000, 0.300006, 0.700000, -0.000000, -0.000000, -0.000031,
+               1.000000)
+]
 pr2_gripper = objects[0]
-print ("pr2_gripper=")
-print (pr2_gripper)
-
-jointPositions=[ 0.550569, 0.000000, 0.549657, 0.000000 ]
-for jointIndex in range (p.getNumJoints(pr2_gripper)):
-	p.resetJointState(pr2_gripper,jointIndex,jointPositions[jointIndex])
-
-pr2_cid = p.createConstraint(pr2_gripper,-1,-1,-1,p.JOINT_FIXED,[0,0,0],[0.2,0,0],[0.500000,0.300006,0.700000])
-print ("pr2_cid")
-print (pr2_cid)
-
-objects = [p.loadURDF("kuka_iiwa/model_vr_limits.urdf", 1.400000,-0.200000,0.600000,0.000000,0.000000,0.000000,1.000000)]
+print("pr2_gripper=")
+print(pr2_gripper)
+
+jointPositions = [0.550569, 0.000000, 0.549657, 0.000000]
+for jointIndex in range(p.getNumJoints(pr2_gripper)):
+  p.resetJointState(pr2_gripper, jointIndex, jointPositions[jointIndex])
+
+pr2_cid = p.createConstraint(pr2_gripper, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0.2, 0, 0],
+                             [0.500000, 0.300006, 0.700000])
+print("pr2_cid")
+print(pr2_cid)
+
+objects = [
+    p.loadURDF("kuka_iiwa/model_vr_limits.urdf", 1.400000, -0.200000, 0.600000, 0.000000, 0.000000,
+               0.000000, 1.000000)
+]
 kuka = objects[0]
-jointPositions=[ -0.000000, -0.000000, 0.000000, 1.570793, 0.000000, -1.036725, 0.000001 ]
-for jointIndex in range (p.getNumJoints(kuka)):
-	p.resetJointState(kuka,jointIndex,jointPositions[jointIndex])
-	p.setJointMotorControl2(kuka,jointIndex,p.POSITION_CONTROL,jointPositions[jointIndex],0)
-
-objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.700000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.800000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.900000,0.000000,0.000000,0.000000,1.000000)]
+jointPositions = [-0.000000, -0.000000, 0.000000, 1.570793, 0.000000, -1.036725, 0.000001]
+for jointIndex in range(p.getNumJoints(kuka)):
+  p.resetJointState(kuka, jointIndex, jointPositions[jointIndex])
+  p.setJointMotorControl2(kuka, jointIndex, p.POSITION_CONTROL, jointPositions[jointIndex], 0)
+
+objects = [
+    p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.700000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.800000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.900000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
 objects = p.loadSDF("gripper/wsg50_one_motor_gripper_new_free_base.sdf")
 kuka_gripper = objects[0]
-print ("kuka gripper=")
+print("kuka gripper=")
 print(kuka_gripper)
 
-p.resetBasePositionAndOrientation(kuka_gripper,[0.923103,-0.200000,1.250036],[-0.000000,0.964531,-0.000002,-0.263970])
-jointPositions=[ 0.000000, -0.011130, -0.206421, 0.205143, -0.009999, 0.000000, -0.010055, 0.000000 ]
-for jointIndex in range (p.getNumJoints(kuka_gripper)):
-	p.resetJointState(kuka_gripper,jointIndex,jointPositions[jointIndex])
-	p.setJointMotorControl2(kuka_gripper,jointIndex,p.POSITION_CONTROL,jointPositions[jointIndex],0)
-
-
-kuka_cid = p.createConstraint(kuka,   6,  kuka_gripper,0,p.JOINT_FIXED, [0,0,0], [0,0,0.05],[0,0,0])
-
-pr2_cid2 = p.createConstraint(kuka_gripper,4,kuka_gripper,6,jointType=p.JOINT_GEAR,jointAxis =[1,1,1],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(pr2_cid2,gearRatio=-1, erp=0.5, relativePositionTarget=0, maxForce=100)
-
-
-objects = [p.loadURDF("jenga/jenga.urdf", 1.300000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.200000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.100000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.000000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 0.900000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 0.800000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("table/table.urdf", 1.000000,-0.200000,0.000000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("teddy_vhacd.urdf", 1.050000,-0.500000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("cube_small.urdf", 0.950000,-0.100000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("sphere_small.urdf", 0.850000,-0.400000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("duck_vhacd.urdf", 0.850000,-0.400000,0.900000,0.000000,0.000000,0.707107,0.707107)]
+p.resetBasePositionAndOrientation(kuka_gripper, [0.923103, -0.200000, 1.250036],
+                                  [-0.000000, 0.964531, -0.000002, -0.263970])
+jointPositions = [
+    0.000000, -0.011130, -0.206421, 0.205143, -0.009999, 0.000000, -0.010055, 0.000000
+]
+for jointIndex in range(p.getNumJoints(kuka_gripper)):
+  p.resetJointState(kuka_gripper, jointIndex, jointPositions[jointIndex])
+  p.setJointMotorControl2(kuka_gripper, jointIndex, p.POSITION_CONTROL, jointPositions[jointIndex],
+                          0)
+
+kuka_cid = p.createConstraint(kuka, 6, kuka_gripper, 0, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0.05],
+                              [0, 0, 0])
+
+pr2_cid2 = p.createConstraint(kuka_gripper,
+                              4,
+                              kuka_gripper,
+                              6,
+                              jointType=p.JOINT_GEAR,
+                              jointAxis=[1, 1, 1],
+                              parentFramePosition=[0, 0, 0],
+                              childFramePosition=[0, 0, 0])
+p.changeConstraint(pr2_cid2, gearRatio=-1, erp=0.5, relativePositionTarget=0, maxForce=100)
+
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.300000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.200000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.100000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.000000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 0.900000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 0.800000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("table/table.urdf", 1.000000, -0.200000, 0.000000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("teddy_vhacd.urdf", 1.050000, -0.500000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("cube_small.urdf", 0.950000, -0.100000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("sphere_small.urdf", 0.850000, -0.400000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("duck_vhacd.urdf", 0.850000, -0.400000, 0.900000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
 objects = p.loadSDF("kiva_shelf/model.sdf")
 ob = objects[0]
-p.resetBasePositionAndOrientation(ob,[0.000000,1.000000,1.204500],[0.000000,0.000000,0.000000,1.000000])
-objects = [p.loadURDF("teddy_vhacd.urdf", -0.100000,0.600000,0.850000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("sphere_small.urdf", -0.100000,0.955006,1.169706,0.633232,-0.000000,-0.000000,0.773962)]
-objects = [p.loadURDF("cube_small.urdf", 0.300000,0.600000,0.850000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("table_square/table_square.urdf", -1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)]
+p.resetBasePositionAndOrientation(ob, [0.000000, 1.000000, 1.204500],
+                                  [0.000000, 0.000000, 0.000000, 1.000000])
+objects = [
+    p.loadURDF("teddy_vhacd.urdf", -0.100000, 0.600000, 0.850000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("sphere_small.urdf", -0.100000, 0.955006, 1.169706, 0.633232, -0.000000, -0.000000,
+               0.773962)
+]
+objects = [
+    p.loadURDF("cube_small.urdf", 0.300000, 0.600000, 0.850000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("table_square/table_square.urdf", -1.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+               0.000000, 1.000000)
+]
 ob = objects[0]
-jointPositions=[ 0.000000 ]
-for jointIndex in range (p.getNumJoints(ob)):
-	p.resetJointState(ob,jointIndex,jointPositions[jointIndex])
-
-objects = [p.loadURDF("husky/husky.urdf", 2.000000,-5.000000,1.000000,0.000000,0.000000,0.000000,1.000000)]
+jointPositions = [0.000000]
+for jointIndex in range(p.getNumJoints(ob)):
+  p.resetJointState(ob, jointIndex, jointPositions[jointIndex])
+
+objects = [
+    p.loadURDF("husky/husky.urdf", 2.000000, -5.000000, 1.000000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
 ob = objects[0]
-jointPositions=[ 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000 ]
-for jointIndex in range (p.getNumJoints(ob)):
-	p.resetJointState(ob,jointIndex,jointPositions[jointIndex])
+jointPositions = [
+    0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+    0.000000
+]
+for jointIndex in range(p.getNumJoints(ob)):
+  p.resetJointState(ob, jointIndex, jointPositions[jointIndex])
 
 p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
 
-p.setGravity(0.000000,0.000000,0.000000)
-p.setGravity(0,0,-10)
+p.setGravity(0.000000, 0.000000, 0.000000)
+p.setGravity(0, 0, -10)
 
 ##show this for 10 seconds
 #now = time.time()
@@ -93,5 +175,5 @@ p.setGravity(0,0,-10)
 p.setRealTimeSimulation(1)
 
 while (1):
-	p.setGravity(0,0,-10)
+  p.setGravity(0, 0, -10)
 p.disconnect()
diff --git a/examples/pybullet/examples/vr_kuka_setup_vrSyncPlugin.py b/examples/pybullet/examples/vr_kuka_setup_vrSyncPlugin.py
index 4465a5324..17b6637d8 100644
--- a/examples/pybullet/examples/vr_kuka_setup_vrSyncPlugin.py
+++ b/examples/pybullet/examples/vr_kuka_setup_vrSyncPlugin.py
@@ -2,91 +2,169 @@ import pybullet as p
 import time
 #p.connect(p.UDP,"192.168.86.100")
 
-
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	p.connect(p.GUI)
+if (cid < 0):
+  p.connect(p.GUI)
 p.resetSimulation()
 #disable rendering during loading makes it much faster
 p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
-objects = [p.loadURDF("plane.urdf", 0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)]
+objects = [
+    p.loadURDF("plane.urdf", 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000)
+]
 #objects = [p.loadURDF("samurai.urdf", 0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("pr2_gripper.urdf", 0.500000,0.300006,0.700000,-0.000000,-0.000000,-0.000031,1.000000)]
+objects = [
+    p.loadURDF("pr2_gripper.urdf", 0.500000, 0.300006, 0.700000, -0.000000, -0.000000, -0.000031,
+               1.000000)
+]
 pr2_gripper = objects[0]
-print ("pr2_gripper=")
-print (pr2_gripper)
-
-jointPositions=[ 0.550569, 0.000000, 0.549657, 0.000000 ]
-for jointIndex in range (p.getNumJoints(pr2_gripper)):
-	p.resetJointState(pr2_gripper,jointIndex,jointPositions[jointIndex])
-	p.setJointMotorControl2(pr2_gripper,jointIndex,p.POSITION_CONTROL,targetPosition=0,force=0)
-
-pr2_cid = p.createConstraint(pr2_gripper,-1,-1,-1,p.JOINT_FIXED,[0,0,0],[0.2,0,0],[0.500000,0.300006,0.700000])
-print ("pr2_cid")
-print (pr2_cid)
-
-pr2_cid2 = p.createConstraint(pr2_gripper,0,pr2_gripper,2,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(pr2_cid2,gearRatio=1, erp=0.5, relativePositionTarget=0.5, maxForce=3)
-
-
-
-objects = [p.loadURDF("kuka_iiwa/model_vr_limits.urdf", 1.400000,-0.200000,0.600000,0.000000,0.000000,0.000000,1.000000)]
+print("pr2_gripper=")
+print(pr2_gripper)
+
+jointPositions = [0.550569, 0.000000, 0.549657, 0.000000]
+for jointIndex in range(p.getNumJoints(pr2_gripper)):
+  p.resetJointState(pr2_gripper, jointIndex, jointPositions[jointIndex])
+  p.setJointMotorControl2(pr2_gripper, jointIndex, p.POSITION_CONTROL, targetPosition=0, force=0)
+
+pr2_cid = p.createConstraint(pr2_gripper, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0.2, 0, 0],
+                             [0.500000, 0.300006, 0.700000])
+print("pr2_cid")
+print(pr2_cid)
+
+pr2_cid2 = p.createConstraint(pr2_gripper,
+                              0,
+                              pr2_gripper,
+                              2,
+                              jointType=p.JOINT_GEAR,
+                              jointAxis=[0, 1, 0],
+                              parentFramePosition=[0, 0, 0],
+                              childFramePosition=[0, 0, 0])
+p.changeConstraint(pr2_cid2, gearRatio=1, erp=0.5, relativePositionTarget=0.5, maxForce=3)
+
+objects = [
+    p.loadURDF("kuka_iiwa/model_vr_limits.urdf", 1.400000, -0.200000, 0.600000, 0.000000, 0.000000,
+               0.000000, 1.000000)
+]
 kuka = objects[0]
-jointPositions=[ -0.000000, -0.000000, 0.000000, 1.570793, 0.000000, -1.036725, 0.000001 ]
-for jointIndex in range (p.getNumJoints(kuka)):
-	p.resetJointState(kuka,jointIndex,jointPositions[jointIndex])
-	p.setJointMotorControl2(kuka,jointIndex,p.POSITION_CONTROL,jointPositions[jointIndex],0)
-
-objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.700000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.800000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.900000,0.000000,0.000000,0.000000,1.000000)]
+jointPositions = [-0.000000, -0.000000, 0.000000, 1.570793, 0.000000, -1.036725, 0.000001]
+for jointIndex in range(p.getNumJoints(kuka)):
+  p.resetJointState(kuka, jointIndex, jointPositions[jointIndex])
+  p.setJointMotorControl2(kuka, jointIndex, p.POSITION_CONTROL, jointPositions[jointIndex], 0)
+
+objects = [
+    p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.700000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.800000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.900000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
 objects = p.loadSDF("gripper/wsg50_one_motor_gripper_new_free_base.sdf")
 kuka_gripper = objects[0]
-print ("kuka gripper=")
+print("kuka gripper=")
 print(kuka_gripper)
 
-p.resetBasePositionAndOrientation(kuka_gripper,[0.923103,-0.200000,1.250036],[-0.000000,0.964531,-0.000002,-0.263970])
-jointPositions=[ 0.000000, -0.011130, -0.206421, 0.205143, -0.009999, 0.000000, -0.010055, 0.000000 ]
-for jointIndex in range (p.getNumJoints(kuka_gripper)):
-	p.resetJointState(kuka_gripper,jointIndex,jointPositions[jointIndex])
-	p.setJointMotorControl2(kuka_gripper,jointIndex,p.POSITION_CONTROL,jointPositions[jointIndex],0)
-
-
-kuka_cid = p.createConstraint(kuka,   6,  kuka_gripper,0,p.JOINT_FIXED, [0,0,0], [0,0,0.05],[0,0,0])
-
-objects = [p.loadURDF("jenga/jenga.urdf", 1.300000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.200000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.100000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.000000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 0.900000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 0.800000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("table/table.urdf", 1.000000,-0.200000,0.000000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("teddy_vhacd.urdf", 1.050000,-0.500000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("cube_small.urdf", 0.950000,-0.100000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("sphere_small.urdf", 0.850000,-0.400000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("duck_vhacd.urdf", 0.850000,-0.400000,0.900000,0.000000,0.000000,0.707107,0.707107)]
+p.resetBasePositionAndOrientation(kuka_gripper, [0.923103, -0.200000, 1.250036],
+                                  [-0.000000, 0.964531, -0.000002, -0.263970])
+jointPositions = [
+    0.000000, -0.011130, -0.206421, 0.205143, -0.009999, 0.000000, -0.010055, 0.000000
+]
+for jointIndex in range(p.getNumJoints(kuka_gripper)):
+  p.resetJointState(kuka_gripper, jointIndex, jointPositions[jointIndex])
+  p.setJointMotorControl2(kuka_gripper, jointIndex, p.POSITION_CONTROL, jointPositions[jointIndex],
+                          0)
+
+kuka_cid = p.createConstraint(kuka, 6, kuka_gripper, 0, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0.05],
+                              [0, 0, 0])
+
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.300000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.200000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.100000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.000000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 0.900000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 0.800000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("table/table.urdf", 1.000000, -0.200000, 0.000000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("teddy_vhacd.urdf", 1.050000, -0.500000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("cube_small.urdf", 0.950000, -0.100000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("sphere_small.urdf", 0.850000, -0.400000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("duck_vhacd.urdf", 0.850000, -0.400000, 0.900000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
 objects = p.loadSDF("kiva_shelf/model.sdf")
 ob = objects[0]
-p.resetBasePositionAndOrientation(ob,[0.000000,1.000000,1.204500],[0.000000,0.000000,0.000000,1.000000])
-objects = [p.loadURDF("teddy_vhacd.urdf", -0.100000,0.600000,0.850000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("sphere_small.urdf", -0.100000,0.955006,1.169706,0.633232,-0.000000,-0.000000,0.773962)]
-objects = [p.loadURDF("cube_small.urdf", 0.300000,0.600000,0.850000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("table_square/table_square.urdf", -1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)]
+p.resetBasePositionAndOrientation(ob, [0.000000, 1.000000, 1.204500],
+                                  [0.000000, 0.000000, 0.000000, 1.000000])
+objects = [
+    p.loadURDF("teddy_vhacd.urdf", -0.100000, 0.600000, 0.850000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("sphere_small.urdf", -0.100000, 0.955006, 1.169706, 0.633232, -0.000000, -0.000000,
+               0.773962)
+]
+objects = [
+    p.loadURDF("cube_small.urdf", 0.300000, 0.600000, 0.850000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("table_square/table_square.urdf", -1.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+               0.000000, 1.000000)
+]
 ob = objects[0]
-jointPositions=[ 0.000000 ]
-for jointIndex in range (p.getNumJoints(ob)):
-	p.resetJointState(ob,jointIndex,jointPositions[jointIndex])
-
-objects = [p.loadURDF("husky/husky.urdf", 2.000000,-5.000000,1.000000,0.000000,0.000000,0.000000,1.000000)]
+jointPositions = [0.000000]
+for jointIndex in range(p.getNumJoints(ob)):
+  p.resetJointState(ob, jointIndex, jointPositions[jointIndex])
+
+objects = [
+    p.loadURDF("husky/husky.urdf", 2.000000, -5.000000, 1.000000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
 ob = objects[0]
-jointPositions=[ 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000 ]
-for jointIndex in range (p.getNumJoints(ob)):
-	p.resetJointState(ob,jointIndex,jointPositions[jointIndex])
+jointPositions = [
+    0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+    0.000000
+]
+for jointIndex in range(p.getNumJoints(ob)):
+  p.resetJointState(ob, jointIndex, jointPositions[jointIndex])
 
 p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
 
-p.setGravity(0.000000,0.000000,0.000000)
-p.setGravity(0,0,-10)
+p.setGravity(0.000000, 0.000000, 0.000000)
+p.setGravity(0, 0, -10)
 
 ##show this for 10 seconds
 #now = time.time()
@@ -94,10 +172,6 @@ p.setGravity(0,0,-10)
 #	p.stepSimulation()
 p.setRealTimeSimulation(1)
 
-
-
-
-
 CONTROLLER_ID = 0
 POSITION = 1
 ORIENTATION = 2
@@ -106,26 +180,25 @@ BUTTONS = 6
 controllerId = -1
 
 print("waiting for VR controller trigger")
-while (controllerId<0):
-	events = p.getVREvents()
-	for e in (events):
-		if (e[BUTTONS][33]==p.VR_BUTTON_IS_DOWN):
-			controllerId = e[CONTROLLER_ID]
-		if (e[BUTTONS][32]==p.VR_BUTTON_IS_DOWN):
-			controllerId = e[CONTROLLER_ID]
+while (controllerId < 0):
+  events = p.getVREvents()
+  for e in (events):
+    if (e[BUTTONS][33] == p.VR_BUTTON_IS_DOWN):
+      controllerId = e[CONTROLLER_ID]
+    if (e[BUTTONS][32] == p.VR_BUTTON_IS_DOWN):
+      controllerId = e[CONTROLLER_ID]
 
-print("Using controllerId="+str(controllerId))
+print("Using controllerId=" + str(controllerId))
 
-	
 #plugin = p.loadPlugin("d:/develop/bullet3/bin/pybullet_vrSyncPlugin_vs2010_x64_release.dll","_vrSyncPlugin")
 plugin = p.loadPlugin("vrSyncPlugin")
-print("PluginId="+str(plugin))
+print("PluginId=" + str(plugin))
 
-p.executePluginCommand(plugin ,"bla", [controllerId,pr2_cid, pr2_cid2,pr2_gripper],[50,3])
+p.executePluginCommand(plugin, "bla", [controllerId, pr2_cid, pr2_cid2, pr2_gripper], [50, 3])
 
 while (1):
-	#b = p.getJointState(pr2_gripper,2)[0]
-	#print("b = " + str(b))
-	#p.setJointMotorControl2(pr2_gripper, 0, p.POSITION_CONTROL, targetPosition=b, force=3)
-	p.setGravity(0,0,-10)
+  #b = p.getJointState(pr2_gripper,2)[0]
+  #print("b = " + str(b))
+  #p.setJointMotorControl2(pr2_gripper, 0, p.POSITION_CONTROL, targetPosition=b, force=3)
+  p.setGravity(0, 0, -10)
 p.disconnect()
diff --git a/examples/pybullet/examples/vr_kuka_setup_vrSyncPython.py b/examples/pybullet/examples/vr_kuka_setup_vrSyncPython.py
index 458406251..989df74d2 100644
--- a/examples/pybullet/examples/vr_kuka_setup_vrSyncPython.py
+++ b/examples/pybullet/examples/vr_kuka_setup_vrSyncPython.py
@@ -2,91 +2,169 @@ import pybullet as p
 import time
 #p.connect(p.UDP,"192.168.86.100")
 
-
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	p.connect(p.GUI)
+if (cid < 0):
+  p.connect(p.GUI)
 p.resetSimulation()
 #disable rendering during loading makes it much faster
 p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
-objects = [p.loadURDF("plane.urdf", 0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)]
+objects = [
+    p.loadURDF("plane.urdf", 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000)
+]
 #objects = [p.loadURDF("samurai.urdf", 0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("pr2_gripper.urdf", 0.500000,0.300006,0.700000,-0.000000,-0.000000,-0.000031,1.000000)]
+objects = [
+    p.loadURDF("pr2_gripper.urdf", 0.500000, 0.300006, 0.700000, -0.000000, -0.000000, -0.000031,
+               1.000000)
+]
 pr2_gripper = objects[0]
-print ("pr2_gripper=")
-print (pr2_gripper)
-
-jointPositions=[ 0.550569, 0.000000, 0.549657, 0.000000 ]
-for jointIndex in range (p.getNumJoints(pr2_gripper)):
-	p.resetJointState(pr2_gripper,jointIndex,jointPositions[jointIndex])
-	p.setJointMotorControl2(pr2_gripper,jointIndex,p.POSITION_CONTROL,targetPosition=0,force=0)
-
-pr2_cid = p.createConstraint(pr2_gripper,-1,-1,-1,p.JOINT_FIXED,[0,0,0],[0.2,0,0],[0.500000,0.300006,0.700000])
-print ("pr2_cid")
-print (pr2_cid)
-
-pr2_cid2 = p.createConstraint(pr2_gripper,0,pr2_gripper,2,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(pr2_cid2,gearRatio=1, erp=0.5, relativePositionTarget=0.5, maxForce=3)
-
-
-
-objects = [p.loadURDF("kuka_iiwa/model_vr_limits.urdf", 1.400000,-0.200000,0.600000,0.000000,0.000000,0.000000,1.000000)]
+print("pr2_gripper=")
+print(pr2_gripper)
+
+jointPositions = [0.550569, 0.000000, 0.549657, 0.000000]
+for jointIndex in range(p.getNumJoints(pr2_gripper)):
+  p.resetJointState(pr2_gripper, jointIndex, jointPositions[jointIndex])
+  p.setJointMotorControl2(pr2_gripper, jointIndex, p.POSITION_CONTROL, targetPosition=0, force=0)
+
+pr2_cid = p.createConstraint(pr2_gripper, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0.2, 0, 0],
+                             [0.500000, 0.300006, 0.700000])
+print("pr2_cid")
+print(pr2_cid)
+
+pr2_cid2 = p.createConstraint(pr2_gripper,
+                              0,
+                              pr2_gripper,
+                              2,
+                              jointType=p.JOINT_GEAR,
+                              jointAxis=[0, 1, 0],
+                              parentFramePosition=[0, 0, 0],
+                              childFramePosition=[0, 0, 0])
+p.changeConstraint(pr2_cid2, gearRatio=1, erp=0.5, relativePositionTarget=0.5, maxForce=3)
+
+objects = [
+    p.loadURDF("kuka_iiwa/model_vr_limits.urdf", 1.400000, -0.200000, 0.600000, 0.000000, 0.000000,
+               0.000000, 1.000000)
+]
 kuka = objects[0]
-jointPositions=[ -0.000000, -0.000000, 0.000000, 1.570793, 0.000000, -1.036725, 0.000001 ]
-for jointIndex in range (p.getNumJoints(kuka)):
-	p.resetJointState(kuka,jointIndex,jointPositions[jointIndex])
-	p.setJointMotorControl2(kuka,jointIndex,p.POSITION_CONTROL,jointPositions[jointIndex],0)
-
-objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.700000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.800000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.900000,0.000000,0.000000,0.000000,1.000000)]
+jointPositions = [-0.000000, -0.000000, 0.000000, 1.570793, 0.000000, -1.036725, 0.000001]
+for jointIndex in range(p.getNumJoints(kuka)):
+  p.resetJointState(kuka, jointIndex, jointPositions[jointIndex])
+  p.setJointMotorControl2(kuka, jointIndex, p.POSITION_CONTROL, jointPositions[jointIndex], 0)
+
+objects = [
+    p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.700000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.800000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.900000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
 objects = p.loadSDF("gripper/wsg50_one_motor_gripper_new_free_base.sdf")
 kuka_gripper = objects[0]
-print ("kuka gripper=")
+print("kuka gripper=")
 print(kuka_gripper)
 
-p.resetBasePositionAndOrientation(kuka_gripper,[0.923103,-0.200000,1.250036],[-0.000000,0.964531,-0.000002,-0.263970])
-jointPositions=[ 0.000000, -0.011130, -0.206421, 0.205143, -0.009999, 0.000000, -0.010055, 0.000000 ]
-for jointIndex in range (p.getNumJoints(kuka_gripper)):
-	p.resetJointState(kuka_gripper,jointIndex,jointPositions[jointIndex])
-	p.setJointMotorControl2(kuka_gripper,jointIndex,p.POSITION_CONTROL,jointPositions[jointIndex],0)
-
-
-kuka_cid = p.createConstraint(kuka,   6,  kuka_gripper,0,p.JOINT_FIXED, [0,0,0], [0,0,0.05],[0,0,0])
-
-objects = [p.loadURDF("jenga/jenga.urdf", 1.300000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.200000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.100000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.000000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 0.900000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 0.800000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("table/table.urdf", 1.000000,-0.200000,0.000000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("teddy_vhacd.urdf", 1.050000,-0.500000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("cube_small.urdf", 0.950000,-0.100000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("sphere_small.urdf", 0.850000,-0.400000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("duck_vhacd.urdf", 0.850000,-0.400000,0.900000,0.000000,0.000000,0.707107,0.707107)]
+p.resetBasePositionAndOrientation(kuka_gripper, [0.923103, -0.200000, 1.250036],
+                                  [-0.000000, 0.964531, -0.000002, -0.263970])
+jointPositions = [
+    0.000000, -0.011130, -0.206421, 0.205143, -0.009999, 0.000000, -0.010055, 0.000000
+]
+for jointIndex in range(p.getNumJoints(kuka_gripper)):
+  p.resetJointState(kuka_gripper, jointIndex, jointPositions[jointIndex])
+  p.setJointMotorControl2(kuka_gripper, jointIndex, p.POSITION_CONTROL, jointPositions[jointIndex],
+                          0)
+
+kuka_cid = p.createConstraint(kuka, 6, kuka_gripper, 0, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0.05],
+                              [0, 0, 0])
+
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.300000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.200000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.100000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.000000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 0.900000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 0.800000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("table/table.urdf", 1.000000, -0.200000, 0.000000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("teddy_vhacd.urdf", 1.050000, -0.500000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("cube_small.urdf", 0.950000, -0.100000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("sphere_small.urdf", 0.850000, -0.400000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("duck_vhacd.urdf", 0.850000, -0.400000, 0.900000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
 objects = p.loadSDF("kiva_shelf/model.sdf")
 ob = objects[0]
-p.resetBasePositionAndOrientation(ob,[0.000000,1.000000,1.204500],[0.000000,0.000000,0.000000,1.000000])
-objects = [p.loadURDF("teddy_vhacd.urdf", -0.100000,0.600000,0.850000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("sphere_small.urdf", -0.100000,0.955006,1.169706,0.633232,-0.000000,-0.000000,0.773962)]
-objects = [p.loadURDF("cube_small.urdf", 0.300000,0.600000,0.850000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("table_square/table_square.urdf", -1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)]
+p.resetBasePositionAndOrientation(ob, [0.000000, 1.000000, 1.204500],
+                                  [0.000000, 0.000000, 0.000000, 1.000000])
+objects = [
+    p.loadURDF("teddy_vhacd.urdf", -0.100000, 0.600000, 0.850000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("sphere_small.urdf", -0.100000, 0.955006, 1.169706, 0.633232, -0.000000, -0.000000,
+               0.773962)
+]
+objects = [
+    p.loadURDF("cube_small.urdf", 0.300000, 0.600000, 0.850000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("table_square/table_square.urdf", -1.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+               0.000000, 1.000000)
+]
 ob = objects[0]
-jointPositions=[ 0.000000 ]
-for jointIndex in range (p.getNumJoints(ob)):
-	p.resetJointState(ob,jointIndex,jointPositions[jointIndex])
-
-objects = [p.loadURDF("husky/husky.urdf", 2.000000,-5.000000,1.000000,0.000000,0.000000,0.000000,1.000000)]
+jointPositions = [0.000000]
+for jointIndex in range(p.getNumJoints(ob)):
+  p.resetJointState(ob, jointIndex, jointPositions[jointIndex])
+
+objects = [
+    p.loadURDF("husky/husky.urdf", 2.000000, -5.000000, 1.000000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
 ob = objects[0]
-jointPositions=[ 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000 ]
-for jointIndex in range (p.getNumJoints(ob)):
-	p.resetJointState(ob,jointIndex,jointPositions[jointIndex])
+jointPositions = [
+    0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+    0.000000
+]
+for jointIndex in range(p.getNumJoints(ob)):
+  p.resetJointState(ob, jointIndex, jointPositions[jointIndex])
 
 p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
 
-p.setGravity(0.000000,0.000000,0.000000)
-p.setGravity(0,0,-10)
+p.setGravity(0.000000, 0.000000, 0.000000)
+p.setGravity(0, 0, -10)
 
 ##show this for 10 seconds
 #now = time.time()
@@ -95,39 +173,40 @@ p.setGravity(0,0,-10)
 p.setRealTimeSimulation(1)
 
 CONTROLLER_ID = 0
-POSITION=1
-ORIENTATION=2
-ANALOG=3
-BUTTONS=6
-
+POSITION = 1
+ORIENTATION = 2
+ANALOG = 3
+BUTTONS = 6
 
 controllerId = -1
 
 print("waiting for VR controller trigger")
-while (controllerId<0):
-	events = p.getVREvents()
-	for e in (events):
-		if (e[BUTTONS][33]==p.VR_BUTTON_IS_DOWN):
-			controllerId = e[CONTROLLER_ID]
-		if (e[BUTTONS][32]==p.VR_BUTTON_IS_DOWN):
-			controllerId = e[CONTROLLER_ID]
+while (controllerId < 0):
+  events = p.getVREvents()
+  for e in (events):
+    if (e[BUTTONS][33] == p.VR_BUTTON_IS_DOWN):
+      controllerId = e[CONTROLLER_ID]
+    if (e[BUTTONS][32] == p.VR_BUTTON_IS_DOWN):
+      controllerId = e[CONTROLLER_ID]
 
-print("Using controllerId="+str(controllerId))
+print("Using controllerId=" + str(controllerId))
 
 while (1):
-	
-
-	#keep the gripper centered/symmetric
-	b = p.getJointState(pr2_gripper,2)[0]
-	p.setJointMotorControl2(pr2_gripper, 0, p.POSITION_CONTROL, targetPosition=b, force=3)
-
-
-	events = p.getVREvents()
-	for e in (events):
-		if e[CONTROLLER_ID] == controllerId:  # To make sure we only get the value for one of the remotes
-			#sync the vr pr2 gripper with the vr controller position
-			p.changeConstraint(pr2_cid, e[POSITION], e[ORIENTATION], maxForce=500)
-			relPosTarget = 1 - e[ANALOG]
-			#open/close the gripper, based on analogue
-			p.changeConstraint(pr2_cid2,gearRatio=1, erp=1, relativePositionTarget=relPosTarget, maxForce=3)
-			
\ No newline at end of file
+
+  #keep the gripper centered/symmetric
+  b = p.getJointState(pr2_gripper, 2)[0]
+  p.setJointMotorControl2(pr2_gripper, 0, p.POSITION_CONTROL, targetPosition=b, force=3)
+
+  events = p.getVREvents()
+  for e in (events):
+    if e[
+        CONTROLLER_ID] == controllerId:  # To make sure we only get the value for one of the remotes
+      #sync the vr pr2 gripper with the vr controller position
+      p.changeConstraint(pr2_cid, e[POSITION], e[ORIENTATION], maxForce=500)
+      relPosTarget = 1 - e[ANALOG]
+      #open/close the gripper, based on analogue
+      p.changeConstraint(pr2_cid2,
+                         gearRatio=1,
+                         erp=1,
+                         relativePositionTarget=relPosTarget,
+                         maxForce=3)
diff --git a/examples/pybullet/examples/vr_racecar_differential.py b/examples/pybullet/examples/vr_racecar_differential.py
index 5cdb8dc95..c664967f0 100644
--- a/examples/pybullet/examples/vr_racecar_differential.py
+++ b/examples/pybullet/examples/vr_racecar_differential.py
@@ -1,93 +1,149 @@
 import pybullet as p
 import time
 CONTROLLER_ID = 0
-POSITION=1
-ORIENTATION=2
-BUTTONS=6
-
+POSITION = 1
+ORIENTATION = 2
+BUTTONS = 6
 
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	p.connect(p.GUI)
-	
+if (cid < 0):
+  p.connect(p.GUI)
+
 p.resetSimulation()
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 useRealTimeSim = 1
 
 #for video recording (works best on Mac and Linux, not well on Windows)
 #p.startStateLogging(p.STATE_LOGGING_VIDEO_MP4, "racecar.mp4")
-p.setRealTimeSimulation(useRealTimeSim) # either this
+p.setRealTimeSimulation(useRealTimeSim)  # either this
 p.loadURDF("plane.urdf")
 #p.loadSDF("stadium.sdf")
 
-car = p.loadURDF("racecar/racecar_differential.urdf") #, [0,0,2],useFixedBase=True)
-for i in range (p.getNumJoints(car)):
-	print (p.getJointInfo(car,i))
+car = p.loadURDF("racecar/racecar_differential.urdf")  #, [0,0,2],useFixedBase=True)
+for i in range(p.getNumJoints(car)):
+  print(p.getJointInfo(car, i))
 for wheel in range(p.getNumJoints(car)):
-		p.setJointMotorControl2(car,wheel,p.VELOCITY_CONTROL,targetVelocity=0,force=0)
-		p.getJointInfo(car,wheel)	
+  p.setJointMotorControl2(car, wheel, p.VELOCITY_CONTROL, targetVelocity=0, force=0)
+  p.getJointInfo(car, wheel)
 
-wheels = [8,15]
+wheels = [8, 15]
 print("----------------")
 
 #p.setJointMotorControl2(car,10,p.VELOCITY_CONTROL,targetVelocity=1,force=10)
-c = p.createConstraint(car,9,car,11,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=1, maxForce=10000)
-
-c = p.createConstraint(car,10,car,13,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=-1, maxForce=10000)
-
-c = p.createConstraint(car,9,car,13,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=-1, maxForce=10000)
-
-c = p.createConstraint(car,16,car,18,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=1, maxForce=10000)
-
-
-c = p.createConstraint(car,16,car,19,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=-1, maxForce=10000)
-
-c = p.createConstraint(car,17,car,19,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=-1, maxForce=10000)
-
-c = p.createConstraint(car,1,car,18,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=-1, gearAuxLink = 15, maxForce=10000)
-c = p.createConstraint(car,3,car,19,jointType=p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-p.changeConstraint(c,gearRatio=-1, gearAuxLink = 15,maxForce=10000)
-
-
-steering = [0,2]
-
-targetVelocitySlider = p.addUserDebugParameter("wheelVelocity",-50,50,0)
-maxForceSlider = p.addUserDebugParameter("maxForce",0,50,20)
-steeringSlider = p.addUserDebugParameter("steering",-1,1,0)
+c = p.createConstraint(car,
+                       9,
+                       car,
+                       11,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=1, maxForce=10000)
+
+c = p.createConstraint(car,
+                       10,
+                       car,
+                       13,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=-1, maxForce=10000)
+
+c = p.createConstraint(car,
+                       9,
+                       car,
+                       13,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=-1, maxForce=10000)
+
+c = p.createConstraint(car,
+                       16,
+                       car,
+                       18,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=1, maxForce=10000)
+
+c = p.createConstraint(car,
+                       16,
+                       car,
+                       19,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=-1, maxForce=10000)
+
+c = p.createConstraint(car,
+                       17,
+                       car,
+                       19,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=-1, maxForce=10000)
+
+c = p.createConstraint(car,
+                       1,
+                       car,
+                       18,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=-1, gearAuxLink=15, maxForce=10000)
+c = p.createConstraint(car,
+                       3,
+                       car,
+                       19,
+                       jointType=p.JOINT_GEAR,
+                       jointAxis=[0, 1, 0],
+                       parentFramePosition=[0, 0, 0],
+                       childFramePosition=[0, 0, 0])
+p.changeConstraint(c, gearRatio=-1, gearAuxLink=15, maxForce=10000)
+
+steering = [0, 2]
+
+targetVelocitySlider = p.addUserDebugParameter("wheelVelocity", -50, 50, 0)
+maxForceSlider = p.addUserDebugParameter("maxForce", 0, 50, 20)
+steeringSlider = p.addUserDebugParameter("steering", -1, 1, 0)
 activeController = -1
 
 while (True):
-			
-	
-	maxForce = p.readUserDebugParameter(maxForceSlider)
-	targetVelocity = p.readUserDebugParameter(targetVelocitySlider)
-	steeringAngle = p.readUserDebugParameter(steeringSlider)
-	#print(targetVelocity)
-	events = p.getVREvents()
-	for e in events:
-		if (e[BUTTONS][33]&p.VR_BUTTON_WAS_TRIGGERED):
-			activeController = e[CONTROLLER_ID]
-		if (activeController == e[CONTROLLER_ID]):
-			orn = e[2]
-			eul = p.getEulerFromQuaternion(orn)
-			steeringAngle=eul[0]
-			
-			targetVelocity = 20.0*e[3]
-		
-	for wheel in wheels:
-		p.setJointMotorControl2(car,wheel,p.VELOCITY_CONTROL,targetVelocity=targetVelocity,force=maxForce)
-		
-	for steer in steering:
-		p.setJointMotorControl2(car,steer,p.POSITION_CONTROL,targetPosition=-steeringAngle)
-		
-	steering
-	if (useRealTimeSim==0):
-		p.stepSimulation()
-	time.sleep(0.01)
+
+  maxForce = p.readUserDebugParameter(maxForceSlider)
+  targetVelocity = p.readUserDebugParameter(targetVelocitySlider)
+  steeringAngle = p.readUserDebugParameter(steeringSlider)
+  #print(targetVelocity)
+  events = p.getVREvents()
+  for e in events:
+    if (e[BUTTONS][33] & p.VR_BUTTON_WAS_TRIGGERED):
+      activeController = e[CONTROLLER_ID]
+    if (activeController == e[CONTROLLER_ID]):
+      orn = e[2]
+      eul = p.getEulerFromQuaternion(orn)
+      steeringAngle = eul[0]
+
+      targetVelocity = 20.0 * e[3]
+
+  for wheel in wheels:
+    p.setJointMotorControl2(car,
+                            wheel,
+                            p.VELOCITY_CONTROL,
+                            targetVelocity=targetVelocity,
+                            force=maxForce)
+
+  for steer in steering:
+    p.setJointMotorControl2(car, steer, p.POSITION_CONTROL, targetPosition=-steeringAngle)
+
+  steering
+  if (useRealTimeSim == 0):
+    p.stepSimulation()
+  time.sleep(0.01)
diff --git a/examples/pybullet/examples/vrhand.py b/examples/pybullet/examples/vrhand.py
index 2ab6ab732..298957e71 100644
--- a/examples/pybullet/examples/vrhand.py
+++ b/examples/pybullet/examples/vrhand.py
@@ -11,101 +11,107 @@ import pybullet as p
 
 #first try to connect to shared memory (VR), if it fails use local GUI
 c = p.connect(p.SHARED_MEMORY)
-if (c<0):
-	c = p.connect(p.GUI)
+if (c < 0):
+  c = p.connect(p.GUI)
 #p.resetSimulation()
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 print(c)
-if (c<0):
-		p.connect(p.GUI)
-		
+if (c < 0):
+  p.connect(p.GUI)
+
 #load the MuJoCo MJCF hand
 objects = p.loadMJCF("MPL/mpl2.xml")
 
-hand=objects[0]
-ho = p.getQuaternionFromEuler([0,3.14,0])
-hand_cid = p.createConstraint(hand,-1,-1,-1,p.JOINT_FIXED,[0,0,0],[0.1,0,0],[0.500000,0.300006,0.700000],ho)
-print ("hand_cid")
-print (hand_cid)
-for i in range (p.getNumJoints(hand)):
-	p.setJointMotorControl2(hand,i,p.POSITION_CONTROL,0,0)
+hand = objects[0]
+ho = p.getQuaternionFromEuler([0, 3.14, 0])
+hand_cid = p.createConstraint(hand, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0.1, 0, 0],
+                              [0.500000, 0.300006, 0.700000], ho)
+print("hand_cid")
+print(hand_cid)
+for i in range(p.getNumJoints(hand)):
+  p.setJointMotorControl2(hand, i, p.POSITION_CONTROL, 0, 0)
 
 #clamp in range 400-600
 minV = 400
 maxV = 600
 
-POSITION=1
-ORIENTATION=2
-BUTTONS=6
+POSITION = 1
+ORIENTATION = 2
+BUTTONS = 6
 
 p.setRealTimeSimulation(1)
 
+
 def convertSensor(x):
-	v = minV
-	try:
-		v = float(x)
-	except ValueError:
-		v = minV    
-	if (v<minV):
-		v=minV
-	if (v>maxV):
-		v=maxV
-	b = (v-minV)/float(maxV-minV)
-	return (1.0-b)
-	
+  v = minV
+  try:
+    v = float(x)
+  except ValueError:
+    v = minV
+  if (v < minV):
+    v = minV
+  if (v > maxV):
+    v = maxV
+  b = (v - minV) / float(maxV - minV)
+  return (1.0 - b)
+
+
 controllerId = -1
 
 serialSteps = 0
 serialStepsUntilCheckVREvents = 3
 
-
-ser = serial.Serial(port='COM9',baudrate=115200,parity=serial.PARITY_ODD,stopbits=serial.STOPBITS_TWO,bytesize=serial.SEVENBITS)
+ser = serial.Serial(port='COM9',
+                    baudrate=115200,
+                    parity=serial.PARITY_ODD,
+                    stopbits=serial.STOPBITS_TWO,
+                    bytesize=serial.SEVENBITS)
 if (ser.isOpen()):
-	while True:
-		events = p.getVREvents()
-		for e in (events):
-			if (e[BUTTONS][33]&p.VR_BUTTON_IS_DOWN):
-				controllerId = e[0]
-			if (e[0] == controllerId):
-				p.changeConstraint(hand_cid,e[POSITION],e[ORIENTATION], maxForce=50)
-			
-		serialSteps = 0
-		while ser.inWaiting() > 0:
-			serialSteps=serialSteps+1
-			if (serialSteps>serialStepsUntilCheckVREvents):
-				ser.flushInput()
-				break
-			line = str(ser.readline())
-			words = line.split(",")
-			if (len(words)==6):
-				middle = convertSensor(words[1])
-				pink = convertSensor(words[2])
-				index = convertSensor(words[3])
-				thumb = convertSensor(words[4])+0.2
-
-				p.setJointMotorControl2(hand,5,p.POSITION_CONTROL,1.3)
-				p.setJointMotorControl2(hand,7,p.POSITION_CONTROL,thumb)
-				p.setJointMotorControl2(hand,9,p.POSITION_CONTROL,thumb)
-				p.setJointMotorControl2(hand,11,p.POSITION_CONTROL,thumb)
-				
-				p.setJointMotorControl2(hand,15,p.POSITION_CONTROL,index)
-				p.setJointMotorControl2(hand,17,p.POSITION_CONTROL,index)
-				p.setJointMotorControl2(hand,19,p.POSITION_CONTROL,index)
-
-				p.setJointMotorControl2(hand,22,p.POSITION_CONTROL,middle)
-				p.setJointMotorControl2(hand,24,p.POSITION_CONTROL,middle)
-				p.setJointMotorControl2(hand,26,p.POSITION_CONTROL,middle)
-				
-				p.setJointMotorControl2(hand,38,p.POSITION_CONTROL,pink)
-				p.setJointMotorControl2(hand,40,p.POSITION_CONTROL,pink)
-				p.setJointMotorControl2(hand,42,p.POSITION_CONTROL,pink)
-
-				ringpos = 0.5*(pink+middle)
-				p.setJointMotorControl2(hand,30,p.POSITION_CONTROL,ringpos)
-				p.setJointMotorControl2(hand,32,p.POSITION_CONTROL,ringpos)
-				p.setJointMotorControl2(hand,34,p.POSITION_CONTROL,ringpos)
-								
-				#print(middle)
-				#print(pink)
-				#print(index)
-				#print(thumb)
\ No newline at end of file
+  while True:
+    events = p.getVREvents()
+    for e in (events):
+      if (e[BUTTONS][33] & p.VR_BUTTON_IS_DOWN):
+        controllerId = e[0]
+      if (e[0] == controllerId):
+        p.changeConstraint(hand_cid, e[POSITION], e[ORIENTATION], maxForce=50)
+
+    serialSteps = 0
+    while ser.inWaiting() > 0:
+      serialSteps = serialSteps + 1
+      if (serialSteps > serialStepsUntilCheckVREvents):
+        ser.flushInput()
+        break
+      line = str(ser.readline())
+      words = line.split(",")
+      if (len(words) == 6):
+        middle = convertSensor(words[1])
+        pink = convertSensor(words[2])
+        index = convertSensor(words[3])
+        thumb = convertSensor(words[4]) + 0.2
+
+        p.setJointMotorControl2(hand, 5, p.POSITION_CONTROL, 1.3)
+        p.setJointMotorControl2(hand, 7, p.POSITION_CONTROL, thumb)
+        p.setJointMotorControl2(hand, 9, p.POSITION_CONTROL, thumb)
+        p.setJointMotorControl2(hand, 11, p.POSITION_CONTROL, thumb)
+
+        p.setJointMotorControl2(hand, 15, p.POSITION_CONTROL, index)
+        p.setJointMotorControl2(hand, 17, p.POSITION_CONTROL, index)
+        p.setJointMotorControl2(hand, 19, p.POSITION_CONTROL, index)
+
+        p.setJointMotorControl2(hand, 22, p.POSITION_CONTROL, middle)
+        p.setJointMotorControl2(hand, 24, p.POSITION_CONTROL, middle)
+        p.setJointMotorControl2(hand, 26, p.POSITION_CONTROL, middle)
+
+        p.setJointMotorControl2(hand, 38, p.POSITION_CONTROL, pink)
+        p.setJointMotorControl2(hand, 40, p.POSITION_CONTROL, pink)
+        p.setJointMotorControl2(hand, 42, p.POSITION_CONTROL, pink)
+
+        ringpos = 0.5 * (pink + middle)
+        p.setJointMotorControl2(hand, 30, p.POSITION_CONTROL, ringpos)
+        p.setJointMotorControl2(hand, 32, p.POSITION_CONTROL, ringpos)
+        p.setJointMotorControl2(hand, 34, p.POSITION_CONTROL, ringpos)
+
+        #print(middle)
+        #print(pink)
+        #print(index)
+        #print(thumb)
diff --git a/examples/pybullet/examples/vrhand_vive_tracker.py b/examples/pybullet/examples/vrhand_vive_tracker.py
index 1242829dd..9fc3c3988 100644
--- a/examples/pybullet/examples/vrhand_vive_tracker.py
+++ b/examples/pybullet/examples/vrhand_vive_tracker.py
@@ -11,38 +11,66 @@ import pybullet as p
 
 #first try to connect to shared memory (VR), if it fails use local GUI
 c = p.connect(p.SHARED_MEMORY)
-if (c<0):
-	c = p.connect(p.GUI)
-	
-p.setInternalSimFlags(0)#don't load default robot assets etc
+if (c < 0):
+  c = p.connect(p.GUI)
+
+p.setInternalSimFlags(0)  #don't load default robot assets etc
 p.resetSimulation()
 
 #p.resetSimulation()
-p.setGravity(0,0,-10)
-objects = [p.loadURDF("plane.urdf", 0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)]
-
-objects = [p.loadURDF("jenga/jenga.urdf", 1.300000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.200000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.100000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.000000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 0.900000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 0.800000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("table/table.urdf", 1.000000,-0.200000,0.000000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("cube_small.urdf", 0.950000,-0.100000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("sphere_small.urdf", 0.850000,-0.400000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-
-		
+p.setGravity(0, 0, -10)
+objects = [
+    p.loadURDF("plane.urdf", 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000)
+]
+
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.300000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.200000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.100000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.000000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 0.900000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 0.800000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("table/table.urdf", 1.000000, -0.200000, 0.000000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("cube_small.urdf", 0.950000, -0.100000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("sphere_small.urdf", 0.850000, -0.400000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+
 #load the MuJoCo MJCF hand
 objects = p.loadMJCF("MPL/mpl2.xml")
 
-hand=objects[0]
-ho = p.getQuaternionFromEuler([3.14,-3.14/2,0])
-hand_cid = p.createConstraint(hand,-1,-1,-1,p.JOINT_FIXED,[0,0,0],[-0.05,0,0.02],[0.500000,0.300006,0.700000],ho)
-print ("hand_cid")
-print (hand_cid)
-for i in range (p.getNumJoints(hand)):
-	p.setJointMotorControl2(hand,i,p.POSITION_CONTROL,0,0)
-
+hand = objects[0]
+ho = p.getQuaternionFromEuler([3.14, -3.14 / 2, 0])
+hand_cid = p.createConstraint(hand, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [-0.05, 0, 0.02],
+                              [0.500000, 0.300006, 0.700000], ho)
+print("hand_cid")
+print(hand_cid)
+for i in range(p.getNumJoints(hand)):
+  p.setJointMotorControl2(hand, i, p.POSITION_CONTROL, 0, 0)
 
 #clamp in range 400-600
 #minV = 400
@@ -50,100 +78,105 @@ for i in range (p.getNumJoints(hand)):
 minV = 250
 maxV = 450
 
-
-POSITION=1
-ORIENTATION=2
-BUTTONS=6
+POSITION = 1
+ORIENTATION = 2
+BUTTONS = 6
 
 p.setRealTimeSimulation(1)
 
+
 def convertSensor(x):
-	v = minV
-	try:
-		v = float(x)
-	except ValueError:
-		v = minV    
-	if (v<minV):
-		v=minV
-	if (v>maxV):
-		v=maxV
-	b = (v-minV)/float(maxV-minV)
-	return (b)
-	
+  v = minV
+  try:
+    v = float(x)
+  except ValueError:
+    v = minV
+  if (v < minV):
+    v = minV
+  if (v > maxV):
+    v = maxV
+  b = (v - minV) / float(maxV - minV)
+  return (b)
+
+
 controllerId = -1
 
 serialSteps = 0
 serialStepsUntilCheckVREvents = 3
 
+
 def getSerialOrNone(portname):
-    try:
-       return serial.Serial(port=portname,baudrate=115200,parity=serial.PARITY_ODD,stopbits=serial.STOPBITS_TWO,bytesize=serial.SEVENBITS)
-    except:
-       return None
+  try:
+    return serial.Serial(port=portname,
+                         baudrate=115200,
+                         parity=serial.PARITY_ODD,
+                         stopbits=serial.STOPBITS_TWO,
+                         bytesize=serial.SEVENBITS)
+  except:
+    return None
 
 
 ser = None
 
 portindex = 10
 while (ser is None and portindex < 30):
-	portname = 'COM'+str(portindex)
-	print(portname)
-	ser = getSerialOrNone(portname)
-	if (ser is None):
-		portname = "/dev/cu.usbmodem14"+str(portindex)
-		print(portname)
-		ser = getSerialOrNone(portname)
-		if (ser is not None):
-			print("COnnected!")
-	portindex = portindex+1
+  portname = 'COM' + str(portindex)
+  print(portname)
+  ser = getSerialOrNone(portname)
+  if (ser is None):
+    portname = "/dev/cu.usbmodem14" + str(portindex)
+    print(portname)
+    ser = getSerialOrNone(portname)
+    if (ser is not None):
+      print("COnnected!")
+  portindex = portindex + 1
 
 p.saveWorld("setupTrackerWorld.py")
 
-
 if (ser.isOpen()):
-	while True:
-
-		events = p.getVREvents(deviceTypeFilter=p.VR_DEVICE_GENERIC_TRACKER)
-		for e in (events):
-				p.changeConstraint(hand_cid,e[POSITION],e[ORIENTATION], maxForce=50)
-			
-		serialSteps = 0
-		while ser.inWaiting() > 0:
-			serialSteps=serialSteps+1
-			if (serialSteps>serialStepsUntilCheckVREvents):
-				ser.flushInput()
-				break
-			line = str(ser.readline())
-			words = line.split(",")
-			if (len(words)==6):
-				pink = convertSensor(words[1])
-				middle = convertSensor(words[2])
-				index = convertSensor(words[3])
-				thumb = convertSensor(words[4])+0.2
-
-				p.setJointMotorControl2(hand,5,p.POSITION_CONTROL,1.3)
-				p.setJointMotorControl2(hand,7,p.POSITION_CONTROL,thumb)
-				p.setJointMotorControl2(hand,9,p.POSITION_CONTROL,thumb)
-				p.setJointMotorControl2(hand,11,p.POSITION_CONTROL,thumb)
-				
-				p.setJointMotorControl2(hand,15,p.POSITION_CONTROL,index)
-				p.setJointMotorControl2(hand,17,p.POSITION_CONTROL,index)
-				p.setJointMotorControl2(hand,19,p.POSITION_CONTROL,index)
-
-				p.setJointMotorControl2(hand,22,p.POSITION_CONTROL,middle)
-				p.setJointMotorControl2(hand,24,p.POSITION_CONTROL,middle)
-				p.setJointMotorControl2(hand,26,p.POSITION_CONTROL,middle)
-				
-				p.setJointMotorControl2(hand,38,p.POSITION_CONTROL,pink)
-				p.setJointMotorControl2(hand,40,p.POSITION_CONTROL,pink)
-				p.setJointMotorControl2(hand,42,p.POSITION_CONTROL,pink)
-
-				ringpos = 0.5*(pink+middle)
-				p.setJointMotorControl2(hand,30,p.POSITION_CONTROL,ringpos)
-				p.setJointMotorControl2(hand,32,p.POSITION_CONTROL,ringpos)
-				p.setJointMotorControl2(hand,34,p.POSITION_CONTROL,ringpos)
-								
-				#print(middle)
-				#print(pink)
-				#print(index)
-				#print(thumb)
\ No newline at end of file
+  while True:
+
+    events = p.getVREvents(deviceTypeFilter=p.VR_DEVICE_GENERIC_TRACKER)
+    for e in (events):
+      p.changeConstraint(hand_cid, e[POSITION], e[ORIENTATION], maxForce=50)
+
+    serialSteps = 0
+    while ser.inWaiting() > 0:
+      serialSteps = serialSteps + 1
+      if (serialSteps > serialStepsUntilCheckVREvents):
+        ser.flushInput()
+        break
+      line = str(ser.readline())
+      words = line.split(",")
+      if (len(words) == 6):
+        pink = convertSensor(words[1])
+        middle = convertSensor(words[2])
+        index = convertSensor(words[3])
+        thumb = convertSensor(words[4]) + 0.2
+
+        p.setJointMotorControl2(hand, 5, p.POSITION_CONTROL, 1.3)
+        p.setJointMotorControl2(hand, 7, p.POSITION_CONTROL, thumb)
+        p.setJointMotorControl2(hand, 9, p.POSITION_CONTROL, thumb)
+        p.setJointMotorControl2(hand, 11, p.POSITION_CONTROL, thumb)
+
+        p.setJointMotorControl2(hand, 15, p.POSITION_CONTROL, index)
+        p.setJointMotorControl2(hand, 17, p.POSITION_CONTROL, index)
+        p.setJointMotorControl2(hand, 19, p.POSITION_CONTROL, index)
+
+        p.setJointMotorControl2(hand, 22, p.POSITION_CONTROL, middle)
+        p.setJointMotorControl2(hand, 24, p.POSITION_CONTROL, middle)
+        p.setJointMotorControl2(hand, 26, p.POSITION_CONTROL, middle)
+
+        p.setJointMotorControl2(hand, 38, p.POSITION_CONTROL, pink)
+        p.setJointMotorControl2(hand, 40, p.POSITION_CONTROL, pink)
+        p.setJointMotorControl2(hand, 42, p.POSITION_CONTROL, pink)
+
+        ringpos = 0.5 * (pink + middle)
+        p.setJointMotorControl2(hand, 30, p.POSITION_CONTROL, ringpos)
+        p.setJointMotorControl2(hand, 32, p.POSITION_CONTROL, ringpos)
+        p.setJointMotorControl2(hand, 34, p.POSITION_CONTROL, ringpos)
+
+        #print(middle)
+        #print(pink)
+        #print(index)
+        #print(thumb)
diff --git a/examples/pybullet/examples/vrminitaur.py b/examples/pybullet/examples/vrminitaur.py
index c6e1daac8..4039c80d5 100644
--- a/examples/pybullet/examples/vrminitaur.py
+++ b/examples/pybullet/examples/vrminitaur.py
@@ -5,45 +5,44 @@ import pybullet as p
 
 #first try to connect to shared memory (VR), if it fails use local GUI
 c = p.connect(p.SHARED_MEMORY)
-if (c<0):
-	c = p.connect(p.GUI)
+if (c < 0):
+  c = p.connect(p.GUI)
 p.resetSimulation()
-p.setGravity(0,0,0)
+p.setGravity(0, 0, 0)
 print(c)
-if (c<0):
-		p.connect(p.GUI)
-		
+if (c < 0):
+  p.connect(p.GUI)
+
 #load the MuJoCo MJCF hand
 minitaur = p.loadURDF("quadruped/minitaur.urdf")
 robot_cid = -1
 
-POSITION=1
-ORIENTATION=2
-BUTTONS=6
+POSITION = 1
+ORIENTATION = 2
+BUTTONS = 6
 
 p.setRealTimeSimulation(1)
 
 controllerId = -1
 
 while True:
-	events = p.getVREvents()
-	for e in (events):
-		#print (e[BUTTONS])
-		if (e[BUTTONS][33]&p.VR_BUTTON_WAS_TRIGGERED ):
-			if (controllerId >= 0):
-				controllerId = -1
-			else:
-				controllerId = e[0]
-		if (e[0] == controllerId):
-			if (robot_cid>=0):
-				p.changeConstraint(robot_cid,e[POSITION],e[ORIENTATION], maxForce=5000)
-		if (e[BUTTONS][32]&p.VR_BUTTON_WAS_TRIGGERED ):
-			if (robot_cid>=0):
-				p.removeConstraint(robot_cid)
-
-			#q = [0,0,0,1]
-			euler = p.getEulerFromQuaternion(e[ORIENTATION])
-			q = p.getQuaternionFromEuler([euler[0],euler[1],0]) #-euler[0],-euler[1],-euler[2]])
-			robot_cid = p.createConstraint(minitaur,-1,-1,-1,p.JOINT_FIXED,[0,0,0],[0.1,0,0],e[POSITION],q,e[ORIENTATION])
-
-			
\ No newline at end of file
+  events = p.getVREvents()
+  for e in (events):
+    #print (e[BUTTONS])
+    if (e[BUTTONS][33] & p.VR_BUTTON_WAS_TRIGGERED):
+      if (controllerId >= 0):
+        controllerId = -1
+      else:
+        controllerId = e[0]
+    if (e[0] == controllerId):
+      if (robot_cid >= 0):
+        p.changeConstraint(robot_cid, e[POSITION], e[ORIENTATION], maxForce=5000)
+    if (e[BUTTONS][32] & p.VR_BUTTON_WAS_TRIGGERED):
+      if (robot_cid >= 0):
+        p.removeConstraint(robot_cid)
+
+      #q = [0,0,0,1]
+      euler = p.getEulerFromQuaternion(e[ORIENTATION])
+      q = p.getQuaternionFromEuler([euler[0], euler[1], 0])  #-euler[0],-euler[1],-euler[2]])
+      robot_cid = p.createConstraint(minitaur, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0.1, 0, 0],
+                                     e[POSITION], q, e[ORIENTATION])
diff --git a/examples/pybullet/examples/vrtracker.py b/examples/pybullet/examples/vrtracker.py
index af5a71035..500e36947 100644
--- a/examples/pybullet/examples/vrtracker.py
+++ b/examples/pybullet/examples/vrtracker.py
@@ -6,84 +6,88 @@
 import pybullet as p
 
 CONTROLLER_ID = 0
-POSITION=1
-ORIENTATION=2
-BUTTONS=6
+POSITION = 1
+ORIENTATION = 2
+BUTTONS = 6
 
 #assume that the VR physics server is already started before
 c = p.connect(p.SHARED_MEMORY)
 print(c)
-if (c<0):
-		p.connect(p.GUI)
+if (c < 0):
+  p.connect(p.GUI)
 
-p.setInternalSimFlags(0)#don't load default robot assets etc
+p.setInternalSimFlags(0)  #don't load default robot assets etc
 p.resetSimulation()
 p.loadURDF("plane.urdf")
-p.loadURDF("cube.urdf",0,0,1)
-p.setGravity(0,0,-10)
+p.loadURDF("cube.urdf", 0, 0, 1)
+p.setGravity(0, 0, -10)
 p.setRealTimeSimulation(1)
 
-prevPosition=[None]*p.VR_MAX_CONTROLLERS
-colors=[0.,0.5,0.5]*p.VR_MAX_CONTROLLERS
-widths = [3]*p.VR_MAX_CONTROLLERS
-a=[0,0,0]
+prevPosition = [None] * p.VR_MAX_CONTROLLERS
+colors = [0., 0.5, 0.5] * p.VR_MAX_CONTROLLERS
+widths = [3] * p.VR_MAX_CONTROLLERS
+a = [0, 0, 0]
 #use a few default colors
-colors[0] = [0,0,0]
-colors[1] = [0.5,0,0]
-colors[2] = [0,0.5,0]
-colors[3] = [0,0,0.5]
-colors[4] = [0.5,0.5,0.]
-colors[5] = [.5,.5,.5]
+colors[0] = [0, 0, 0]
+colors[1] = [0.5, 0, 0]
+colors[2] = [0, 0.5, 0]
+colors[3] = [0, 0, 0.5]
+colors[4] = [0.5, 0.5, 0.]
+colors[5] = [.5, .5, .5]
 
-p.startStateLogging(p.STATE_LOGGING_VR_CONTROLLERS, "vr_hmd.bin",deviceTypeFilter=p.VR_DEVICE_HMD)
+p.startStateLogging(p.STATE_LOGGING_VR_CONTROLLERS, "vr_hmd.bin", deviceTypeFilter=p.VR_DEVICE_HMD)
 p.startStateLogging(p.STATE_LOGGING_GENERIC_ROBOT, "generic_data.bin")
 p.startStateLogging(p.STATE_LOGGING_CONTACT_POINTS, "contact_points.bin")
 
 while True:
-	events = p.getVREvents(p.VR_DEVICE_HMD+p.VR_DEVICE_GENERIC_TRACKER)
-	for e in (events):
-		pos = e[POSITION]
-		mat = p.getMatrixFromQuaternion(e[ORIENTATION])
-		dir0 = [mat[0],mat[3],mat[6]]
-		dir1 = [mat[1],mat[4],mat[7]]
-		dir2 = [mat[2],mat[5],mat[8]]
-		lineLen = 0.1
-		dir = [-mat[2],-mat[5],-mat[8]]
+  events = p.getVREvents(p.VR_DEVICE_HMD + p.VR_DEVICE_GENERIC_TRACKER)
+  for e in (events):
+    pos = e[POSITION]
+    mat = p.getMatrixFromQuaternion(e[ORIENTATION])
+    dir0 = [mat[0], mat[3], mat[6]]
+    dir1 = [mat[1], mat[4], mat[7]]
+    dir2 = [mat[2], mat[5], mat[8]]
+    lineLen = 0.1
+    dir = [-mat[2], -mat[5], -mat[8]]
 
-		to = [pos[0]+lineLen*dir[0],pos[1]+lineLen*dir[1],pos[2]+lineLen*dir[2]]
-		toX = [pos[0]+lineLen*dir0[0],pos[1]+lineLen*dir0[1],pos[2]+lineLen*dir0[2]]
-		toY = [pos[0]+lineLen*dir1[0],pos[1]+lineLen*dir1[1],pos[2]+lineLen*dir1[2]]
-		toZ = [pos[0]+lineLen*dir2[0],pos[1]+lineLen*dir2[1],pos[2]+lineLen*dir2[2]]
-		p.addUserDebugLine(pos,toX,[1,0,0],1)
-		p.addUserDebugLine(pos,toY,[0,1,0],1)
-		p.addUserDebugLine(pos,toZ,[0,0,1],1)
-	
-		p.addUserDebugLine(pos,to,[0.5,0.5,0.],1,3)
-		
-	events = p.getVREvents()
+    to = [pos[0] + lineLen * dir[0], pos[1] + lineLen * dir[1], pos[2] + lineLen * dir[2]]
+    toX = [pos[0] + lineLen * dir0[0], pos[1] + lineLen * dir0[1], pos[2] + lineLen * dir0[2]]
+    toY = [pos[0] + lineLen * dir1[0], pos[1] + lineLen * dir1[1], pos[2] + lineLen * dir1[2]]
+    toZ = [pos[0] + lineLen * dir2[0], pos[1] + lineLen * dir2[1], pos[2] + lineLen * dir2[2]]
+    p.addUserDebugLine(pos, toX, [1, 0, 0], 1)
+    p.addUserDebugLine(pos, toY, [0, 1, 0], 1)
+    p.addUserDebugLine(pos, toZ, [0, 0, 1], 1)
 
-	for e in (events):
-		if (e[BUTTONS][33]&p.VR_BUTTON_WAS_TRIGGERED):
-			prevPosition[e[CONTROLLER_ID]] = e[POSITION]
-		if (e[BUTTONS][32]&p.VR_BUTTON_WAS_TRIGGERED):
-			widths[e[CONTROLLER_ID]]=widths[e[0]]+1
-			if (widths[e[CONTROLLER_ID]]>20):
-				widths[e[CONTROLLER_ID]] = 1
-		if (e[BUTTONS][1]&p.VR_BUTTON_WAS_TRIGGERED):
-			p.resetSimulation()
-			#p.setGravity(0,0,-10)
-			p.removeAllUserDebugItems()
-			p.loadURDF("plane.urdf")
-		if (e[BUTTONS][33]==p.VR_BUTTON_IS_DOWN):
-			pt = prevPosition[e[CONTROLLER_ID]]
-			
-			#print(prevPosition[e[0]])
-			#print(e[1])
-			diff = [pt[0]-e[POSITION][0],pt[1]-e[POSITION][1],pt[2]-e[POSITION][2]]
-			lenSqr =	diff[0]*diff[0]+diff[1]*diff[1]+diff[2]*diff[2]
-			ptDistThreshold = 0.01
-			if (lenSqr>(ptDistThreshold*ptDistThreshold)):
-				p.addUserDebugLine(e[POSITION],prevPosition[e[CONTROLLER_ID]],colors[e[CONTROLLER_ID]],widths[e[CONTROLLER_ID]])
-				#p.loadURDF("cube_small.urdf",e[1])
-				colors[e[CONTROLLER_ID]] = [1-colors[e[CONTROLLER_ID]][0],1-colors[e[CONTROLLER_ID]][1],1-colors[e[CONTROLLER_ID]][2]]
-				prevPosition[e[CONTROLLER_ID]] = e[POSITION]
\ No newline at end of file
+    p.addUserDebugLine(pos, to, [0.5, 0.5, 0.], 1, 3)
+
+  events = p.getVREvents()
+
+  for e in (events):
+    if (e[BUTTONS][33] & p.VR_BUTTON_WAS_TRIGGERED):
+      prevPosition[e[CONTROLLER_ID]] = e[POSITION]
+    if (e[BUTTONS][32] & p.VR_BUTTON_WAS_TRIGGERED):
+      widths[e[CONTROLLER_ID]] = widths[e[0]] + 1
+      if (widths[e[CONTROLLER_ID]] > 20):
+        widths[e[CONTROLLER_ID]] = 1
+    if (e[BUTTONS][1] & p.VR_BUTTON_WAS_TRIGGERED):
+      p.resetSimulation()
+      #p.setGravity(0,0,-10)
+      p.removeAllUserDebugItems()
+      p.loadURDF("plane.urdf")
+    if (e[BUTTONS][33] == p.VR_BUTTON_IS_DOWN):
+      pt = prevPosition[e[CONTROLLER_ID]]
+
+      #print(prevPosition[e[0]])
+      #print(e[1])
+      diff = [pt[0] - e[POSITION][0], pt[1] - e[POSITION][1], pt[2] - e[POSITION][2]]
+      lenSqr = diff[0] * diff[0] + diff[1] * diff[1] + diff[2] * diff[2]
+      ptDistThreshold = 0.01
+      if (lenSqr > (ptDistThreshold * ptDistThreshold)):
+        p.addUserDebugLine(e[POSITION], prevPosition[e[CONTROLLER_ID]], colors[e[CONTROLLER_ID]],
+                           widths[e[CONTROLLER_ID]])
+        #p.loadURDF("cube_small.urdf",e[1])
+        colors[e[CONTROLLER_ID]] = [
+            1 - colors[e[CONTROLLER_ID]][0], 1 - colors[e[CONTROLLER_ID]][1],
+            1 - colors[e[CONTROLLER_ID]][2]
+        ]
+        prevPosition[e[CONTROLLER_ID]] = e[POSITION]
diff --git a/examples/pybullet/examples/widows.py b/examples/pybullet/examples/widows.py
index aa1af8cb5..9ecc5d256 100644
--- a/examples/pybullet/examples/widows.py
+++ b/examples/pybullet/examples/widows.py
@@ -2,20 +2,23 @@ import pybullet as p
 import time
 
 p.connect(p.GUI)
-p.loadURDF("table/table.urdf", 0.5000000,0.00000,-.820000,0.000000,0.000000,0.0,1.0)
-p.setGravity(0,0,-10)
-arm = p.loadURDF("widowx/widowx.urdf",useFixedBase=True)
+p.loadURDF("table/table.urdf", 0.5000000, 0.00000, -.820000, 0.000000, 0.000000, 0.0, 1.0)
+p.setGravity(0, 0, -10)
+arm = p.loadURDF("widowx/widowx.urdf", useFixedBase=True)
+
+p.resetBasePositionAndOrientation(arm, [-0.098612, -0.000726, -0.194018],
+                                  [0.000000, 0.000000, 0.000000, 1.000000])
 
-p.resetBasePositionAndOrientation(arm,[-0.098612,-0.000726,-0.194018],[0.000000,0.000000,0.000000,1.000000])
-  
-	
 while (1):
-	p.stepSimulation()
-	time.sleep(0.01)
-	#p.saveWorld("test.py")
-	viewMat = p.getDebugVisualizerCamera()[2]
-	#projMatrix = [0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0]
-	projMatrix = p.getDebugVisualizerCamera()[3]
-	width=640
-	height=480
-	img_arr = p.getCameraImage(width=width,height=height,viewMatrix=viewMat,projectionMatrix=projMatrix)
+  p.stepSimulation()
+  time.sleep(0.01)
+  #p.saveWorld("test.py")
+  viewMat = p.getDebugVisualizerCamera()[2]
+  #projMatrix = [0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0]
+  projMatrix = p.getDebugVisualizerCamera()[3]
+  width = 640
+  height = 480
+  img_arr = p.getCameraImage(width=width,
+                             height=height,
+                             viewMatrix=viewMat,
+                             projectionMatrix=projMatrix)
diff --git a/examples/pybullet/gym/pybullet_data/__init__.py b/examples/pybullet/gym/pybullet_data/__init__.py
index 90f6f6b05..a4e54023e 100644
--- a/examples/pybullet/gym/pybullet_data/__init__.py
+++ b/examples/pybullet/gym/pybullet_data/__init__.py
@@ -1,5 +1,6 @@
 import os
 
+
 def getDataPath():
-	resdir = os.path.join(os.path.dirname(__file__))
-	return resdir
+  resdir = os.path.join(os.path.dirname(__file__))
+  return resdir
diff --git a/examples/pybullet/gym/pybullet_data/data/motions/laikago_walk.json b/examples/pybullet/gym/pybullet_data/data/motions/laikago_walk.txt
index cf2c9a697..cf2c9a697 100644
--- a/examples/pybullet/gym/pybullet_data/data/motions/laikago_walk.json
+++ b/examples/pybullet/gym/pybullet_data/data/motions/laikago_walk.txt
diff --git a/examples/pybullet/gym/pybullet_data/laikago/laikago.py b/examples/pybullet/gym/pybullet_data/laikago/laikago.py
index 5b4ab8f01..2a5d8b369 100644
--- a/examples/pybullet/gym/pybullet_data/laikago/laikago.py
+++ b/examples/pybullet/gym/pybullet_data/laikago/laikago.py
@@ -3,98 +3,108 @@ import time
 

 p.connect(p.GUI)

 plane = p.loadURDF("plane.urdf")

-p.setGravity(0,0,-9.8)

-p.setTimeStep(1./500)

+p.setGravity(0, 0, -9.8)

+p.setTimeStep(1. / 500)

 #p.setDefaultContactERP(0)

-#urdfFlags = p.URDF_USE_SELF_COLLISION+p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS 

+#urdfFlags = p.URDF_USE_SELF_COLLISION+p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS

 urdfFlags = p.URDF_USE_SELF_COLLISION

-quadruped = p.loadURDF("laikago.urdf",[0,0,.5],[0,0.5,0.5,0], flags = urdfFlags,useFixedBase=False)

+quadruped = p.loadURDF("laikago.urdf", [0, 0, .5], [0, 0.5, 0.5, 0],

+                       flags=urdfFlags,

+                       useFixedBase=False)

 

 #enable collision between lower legs

 

-for j in range (p.getNumJoints(quadruped)):

-		print(p.getJointInfo(quadruped,j))

+for j in range(p.getNumJoints(quadruped)):

+  print(p.getJointInfo(quadruped, j))

 

 #2,5,8 and 11 are the lower legs

-lower_legs = [2,5,8,11]

+lower_legs = [2, 5, 8, 11]

 for l0 in lower_legs:

-	for l1 in lower_legs:

-		if (l1>l0):

-			enableCollision = 1

-			print("collision for pair",l0,l1, p.getJointInfo(quadruped,l0)[12],p.getJointInfo(quadruped,l1)[12], "enabled=",enableCollision)

-			p.setCollisionFilterPair(quadruped, quadruped, 2,5,enableCollision)

-

-jointIds=[]

-paramIds=[]

-jointOffsets=[]

-jointDirections=[-1,1,1,1,1,1,-1,1,1,1,1,1]

-jointAngles=[0,0,0,0,0,0,0,0,0,0,0,0]

-

-for i in range (4):

-	jointOffsets.append(0)

-	jointOffsets.append(-0.7)

-	jointOffsets.append(0.7)

-

-maxForceId = p.addUserDebugParameter("maxForce",0,100,20)

-

-for j in range (p.getNumJoints(quadruped)):

-        p.changeDynamics(quadruped,j,linearDamping=0, angularDamping=0)

-        info = p.getJointInfo(quadruped,j)

-        #print(info)

-        jointName = info[1]

-        jointType = info[2]

-        if (jointType==p.JOINT_PRISMATIC or jointType==p.JOINT_REVOLUTE):

-                jointIds.append(j)

-

-		

-p.getCameraImage(480,320)

+  for l1 in lower_legs:

+    if (l1 > l0):

+      enableCollision = 1

+      print("collision for pair", l0, l1,

+            p.getJointInfo(quadruped, l0)[12],

+            p.getJointInfo(quadruped, l1)[12], "enabled=", enableCollision)

+      p.setCollisionFilterPair(quadruped, quadruped, 2, 5, enableCollision)

+

+jointIds = []

+paramIds = []

+jointOffsets = []

+jointDirections = [-1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1]

+jointAngles = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

+

+for i in range(4):

+  jointOffsets.append(0)

+  jointOffsets.append(-0.7)

+  jointOffsets.append(0.7)

+

+maxForceId = p.addUserDebugParameter("maxForce", 0, 100, 20)

+

+for j in range(p.getNumJoints(quadruped)):

+  p.changeDynamics(quadruped, j, linearDamping=0, angularDamping=0)

+  info = p.getJointInfo(quadruped, j)

+  #print(info)

+  jointName = info[1]

+  jointType = info[2]

+  if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE):

+    jointIds.append(j)

+

+p.getCameraImage(480, 320)

 p.setRealTimeSimulation(0)

 

-joints=[]

-

-with open("data1.txt","r") as filestream:

-        for line in filestream:

-		print("line=",line)

-		maxForce = p.readUserDebugParameter(maxForceId)

-                currentline = line.split(",")

-                #print (currentline)

-                #print("-----")

-                frame = currentline[0]

-                t = currentline[1]

-                #print("frame[",frame,"]")

-                joints=currentline[2:14]

-                #print("joints=",joints)

-		for j in range (12):

-			targetPos = float(joints[j])

-			p.setJointMotorControl2(quadruped,jointIds[j],p.POSITION_CONTROL,jointDirections[j]*targetPos+jointOffsets[j], force=maxForce)

- 		p.stepSimulation()

-		for lower_leg in lower_legs:

-			#print("points for ", quadruped, " link: ", lower_leg)

-			pts = p.getContactPoints(quadruped,-1, lower_leg)

-			#print("num points=",len(pts))

-			#for pt in pts:

-			#	print(pt[9])

-		time.sleep(1./500.)

-

-

-for j in range (p.getNumJoints(quadruped)):

-        p.changeDynamics(quadruped,j,linearDamping=0, angularDamping=0)

-        info = p.getJointInfo(quadruped,j)

-        js = p.getJointState(quadruped,j)

-        #print(info)

-        jointName = info[1]

-        jointType = info[2]

-        if (jointType==p.JOINT_PRISMATIC or jointType==p.JOINT_REVOLUTE):

-                paramIds.append(p.addUserDebugParameter(jointName.decode("utf-8"),-4,4,(js[0]-jointOffsets[j])/jointDirections[j]))

-

+joints = []

+

+with open("data1.txt", "r") as filestream:

+  for line in filestream:

+    print("line=", line)

+    maxForce = p.readUserDebugParameter(maxForceId)

+    currentline = line.split(",")

+    #print (currentline)

+    #print("-----")

+    frame = currentline[0]

+    t = currentline[1]

+    #print("frame[",frame,"]")

+    joints = currentline[2:14]

+    #print("joints=",joints)

+    for j in range(12):

+      targetPos = float(joints[j])

+      p.setJointMotorControl2(quadruped,

+                              jointIds[j],

+                              p.POSITION_CONTROL,

+                              jointDirections[j] * targetPos + jointOffsets[j],

+                              force=maxForce)

+    p.stepSimulation()

+    for lower_leg in lower_legs:

+      #print("points for ", quadruped, " link: ", lower_leg)

+      pts = p.getContactPoints(quadruped, -1, lower_leg)

+      #print("num points=",len(pts))

+      #for pt in pts:

+      #	print(pt[9])

+    time.sleep(1. / 500.)

+

+for j in range(p.getNumJoints(quadruped)):

+  p.changeDynamics(quadruped, j, linearDamping=0, angularDamping=0)

+  info = p.getJointInfo(quadruped, j)

+  js = p.getJointState(quadruped, j)

+  #print(info)

+  jointName = info[1]

+  jointType = info[2]

+  if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE):

+    paramIds.append(

+        p.addUserDebugParameter(jointName.decode("utf-8"), -4, 4,

+                                (js[0] - jointOffsets[j]) / jointDirections[j]))

 

 p.setRealTimeSimulation(1)

 

 while (1):

-	

-	for i in range(len(paramIds)):

-		c = paramIds[i]

-		targetPos = p.readUserDebugParameter(c)

-		maxForce = p.readUserDebugParameter(maxForceId)

-		p.setJointMotorControl2(quadruped,jointIds[i],p.POSITION_CONTROL,jointDirections[i]*targetPos+jointOffsets[i], force=maxForce)

-	

+

+  for i in range(len(paramIds)):

+    c = paramIds[i]

+    targetPos = p.readUserDebugParameter(c)

+    maxForce = p.readUserDebugParameter(maxForceId)

+    p.setJointMotorControl2(quadruped,

+                            jointIds[i],

+                            p.POSITION_CONTROL,

+                            jointDirections[i] * targetPos + jointOffsets[i],

+                            force=maxForce)

diff --git a/examples/pybullet/gym/pybullet_envs/ARS/ars.py b/examples/pybullet/gym/pybullet_envs/ARS/ars.py
index 760ebd732..51bc776db 100644
--- a/examples/pybullet/gym/pybullet_envs/ARS/ars.py
+++ b/examples/pybullet/gym/pybullet_envs/ARS/ars.py
@@ -4,7 +4,7 @@ import os
 import inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 # Importing the libraries
 import os
@@ -20,17 +20,17 @@ import argparse
 
 # Setting the Hyper Parameters
 class Hp():
-    
-    def __init__(self):
-        self.nb_steps = 10000
-        self.episode_length = 1000
-        self.learning_rate = 0.02
-        self.nb_directions = 16
-        self.nb_best_directions = 16
-        assert self.nb_best_directions <= self.nb_directions
-        self.noise = 0.03
-        self.seed = 1
-        self.env_name = 'HalfCheetahBulletEnv-v0'
+
+  def __init__(self):
+    self.nb_steps = 10000
+    self.episode_length = 1000
+    self.learning_rate = 0.02
+    self.nb_directions = 16
+    self.nb_best_directions = 8
+    assert self.nb_best_directions <= self.nb_directions
+    self.noise = 0.03
+    self.seed = 1
+    self.env_name = 'HalfCheetahBulletEnv-v0'
 
 
 # Multiprocess Exploring the policy on one specific direction and over one episode
@@ -39,239 +39,250 @@ _RESET = 1
 _CLOSE = 2
 _EXPLORE = 3
 
-def ExploreWorker(rank,childPipe, envname, args):
-    env = gym.make(envname)
-    nb_inputs = env.observation_space.shape[0]
-    normalizer = Normalizer(nb_inputs)
-    observation_n = env.reset()
-    n=0
-    while True:
-      n+=1
-      try:
-        # Only block for short times to have keyboard exceptions be raised.
-        if not childPipe.poll(0.001):
-          continue
-        message, payload = childPipe.recv()
-      except (EOFError, KeyboardInterrupt):
-        break
-      if message == _RESET:
-        observation_n = env.reset()
-        childPipe.send(["reset ok"])
-        continue
-      if message == _EXPLORE:
-        #normalizer = payload[0] #use our local normalizer
-        policy = payload[1]
-        hp = payload[2]
-        direction = payload[3]
-        delta = payload[4]
-        state = env.reset()
-        done = False
-        num_plays = 0.
-        sum_rewards = 0
-        while not done and num_plays < hp.episode_length:
-            normalizer.observe(state)
-            state = normalizer.normalize(state)
-            action = policy.evaluate(state, delta, direction,hp)
-            state, reward, done, _ = env.step(action)
-            reward = max(min(reward, 1), -1)
-            sum_rewards += reward
-            num_plays += 1
-        childPipe.send([sum_rewards])
+
+def ExploreWorker(rank, childPipe, envname, args):
+  env = gym.make(envname)
+  nb_inputs = env.observation_space.shape[0]
+  normalizer = Normalizer(nb_inputs)
+  observation_n = env.reset()
+  n = 0
+  while True:
+    n += 1
+    try:
+      # Only block for short times to have keyboard exceptions be raised.
+      if not childPipe.poll(0.001):
         continue
-      if message == _CLOSE:
-        childPipe.send(["close ok"])
-        break
-    childPipe.close()
-        
+      message, payload = childPipe.recv()
+    except (EOFError, KeyboardInterrupt):
+      break
+    if message == _RESET:
+      observation_n = env.reset()
+      childPipe.send(["reset ok"])
+      continue
+    if message == _EXPLORE:
+      #normalizer = payload[0] #use our local normalizer
+      policy = payload[1]
+      hp = payload[2]
+      direction = payload[3]
+      delta = payload[4]
+      state = env.reset()
+      done = False
+      num_plays = 0.
+      sum_rewards = 0
+      while not done and num_plays < hp.episode_length:
+        normalizer.observe(state)
+        state = normalizer.normalize(state)
+        action = policy.evaluate(state, delta, direction, hp)
+        state, reward, done, _ = env.step(action)
+        reward = max(min(reward, 1), -1)
+        sum_rewards += reward
+        num_plays += 1
+      childPipe.send([sum_rewards])
+      continue
+    if message == _CLOSE:
+      childPipe.send(["close ok"])
+      break
+  childPipe.close()
+
 
 # Normalizing the states
 
+
 class Normalizer():
-    
-    def __init__(self, nb_inputs):
-        self.n = np.zeros(nb_inputs)
-        self.mean = np.zeros(nb_inputs)
-        self.mean_diff = np.zeros(nb_inputs)
-        self.var = np.zeros(nb_inputs)
-    
-    def observe(self, x):
-        self.n += 1.
-        last_mean = self.mean.copy()
-        self.mean += (x - self.mean) / self.n
-        self.mean_diff += (x - last_mean) * (x - self.mean)
-        self.var = (self.mean_diff / self.n).clip(min = 1e-2)
-    
-    def normalize(self, inputs):
-        obs_mean = self.mean
-        obs_std = np.sqrt(self.var)
-        return (inputs - obs_mean) / obs_std
+
+  def __init__(self, nb_inputs):
+    self.n = np.zeros(nb_inputs)
+    self.mean = np.zeros(nb_inputs)
+    self.mean_diff = np.zeros(nb_inputs)
+    self.var = np.zeros(nb_inputs)
+
+  def observe(self, x):
+    self.n += 1.
+    last_mean = self.mean.copy()
+    self.mean += (x - self.mean) / self.n
+    self.mean_diff += (x - last_mean) * (x - self.mean)
+    self.var = (self.mean_diff / self.n).clip(min=1e-2)
+
+  def normalize(self, inputs):
+    obs_mean = self.mean
+    obs_std = np.sqrt(self.var)
+    return (inputs - obs_mean) / obs_std
+
 
 # Building the AI
 
+
 class Policy():
-    def __init__(self, input_size, output_size, env_name, args):
-        try:
-          self.theta = np.load(args.policy)
-        except:
-          self.theta = np.zeros((output_size, input_size))
-        self.env_name = env_name
-        print("Starting policy theta=",self.theta)
-    def evaluate(self, input, delta, direction, hp):
-        if direction is None:
-            return np.clip(self.theta.dot(input), -1.0, 1.0)
-        elif direction == "positive":
-            return np.clip((self.theta + hp.noise*delta).dot(input), -1.0, 1.0)
-        else:
-            return np.clip((self.theta - hp.noise*delta).dot(input), -1.0, 1.0)
-    
-    def sample_deltas(self):
-        return [np.random.randn(*self.theta.shape) for _ in range(hp.nb_directions)]
-    
-    def update(self, rollouts, sigma_r, args):
-        step = np.zeros(self.theta.shape)
-        for r_pos, r_neg, d in rollouts:
-            step += (r_pos - r_neg) * d
-        self.theta += hp.learning_rate / (hp.nb_best_directions * sigma_r) * step
-        timestr = time.strftime("%Y%m%d-%H%M%S")
-        np.save(args.logdir+"/policy_"+self.env_name+"_"+timestr+".npy", self.theta)
+
+  def __init__(self, input_size, output_size, env_name, args):
+    try:
+      self.theta = np.load(args.policy)
+    except:
+      self.theta = np.zeros((output_size, input_size))
+    self.env_name = env_name
+    print("Starting policy theta=", self.theta)
+
+  def evaluate(self, input, delta, direction, hp):
+    if direction is None:
+      return np.clip(self.theta.dot(input), -1.0, 1.0)
+    elif direction == "positive":
+      return np.clip((self.theta + hp.noise * delta).dot(input), -1.0, 1.0)
+    else:
+      return np.clip((self.theta - hp.noise * delta).dot(input), -1.0, 1.0)
+
+  def sample_deltas(self):
+    return [np.random.randn(*self.theta.shape) for _ in range(hp.nb_directions)]
+
+  def update(self, rollouts, sigma_r, args):
+    step = np.zeros(self.theta.shape)
+    for r_pos, r_neg, d in rollouts:
+      step += (r_pos - r_neg) * d
+    self.theta += hp.learning_rate / (hp.nb_best_directions * sigma_r) * step
+    timestr = time.strftime("%Y%m%d-%H%M%S")
+    np.save(args.logdir + "/policy_" + self.env_name + "_" + timestr + ".npy", self.theta)
 
 
 # Exploring the policy on one specific direction and over one episode
 
+
 def explore(env, normalizer, policy, direction, delta, hp):
-    state = env.reset()
-    done = False
-    num_plays = 0.
-    sum_rewards = 0
-    while not done and num_plays < hp.episode_length:
-        normalizer.observe(state)
-        state = normalizer.normalize(state)
-        action = policy.evaluate(state, delta, direction, hp)
-        state, reward, done, _ = env.step(action)
-        reward = max(min(reward, 1), -1)
-        sum_rewards += reward
-        num_plays += 1
-    return sum_rewards
+  state = env.reset()
+  done = False
+  num_plays = 0.
+  sum_rewards = 0
+  while not done and num_plays < hp.episode_length:
+    normalizer.observe(state)
+    state = normalizer.normalize(state)
+    action = policy.evaluate(state, delta, direction, hp)
+    state, reward, done, _ = env.step(action)
+    reward = max(min(reward, 1), -1)
+    sum_rewards += reward
+    num_plays += 1
+  return sum_rewards
+
 
 # Training the AI
 
+
 def train(env, policy, normalizer, hp, parentPipes, args):
-    
-    for step in range(hp.nb_steps):
-        
-        # Initializing the perturbations deltas and the positive/negative rewards
-        deltas = policy.sample_deltas()
-        positive_rewards = [0] * hp.nb_directions
-        negative_rewards = [0] * hp.nb_directions
-        
-        if parentPipes:
-          for k in range(hp.nb_directions):
-            parentPipe = parentPipes[k]
-            parentPipe.send([_EXPLORE,[normalizer, policy, hp, "positive", deltas[k]]])
-          for k in range(hp.nb_directions):
-            positive_rewards[k] = parentPipes[k].recv()[0]
-          
-          for k in range(hp.nb_directions):
-            parentPipe = parentPipes[k]
-            parentPipe.send([_EXPLORE,[normalizer, policy, hp, "negative", deltas[k]]])
-          for k in range(hp.nb_directions):
-            negative_rewards[k] = parentPipes[k].recv()[0]
-          
-        else:
-          # Getting the positive rewards in the positive directions
-          for k in range(hp.nb_directions):
-              positive_rewards[k] = explore(env, normalizer, policy, "positive", deltas[k], hp)
-        
-          
-          # Getting the negative rewards in the negative/opposite directions
-          for k in range(hp.nb_directions):
-              negative_rewards[k] = explore(env, normalizer, policy, "negative", deltas[k], hp)
-            
-        
-        # Gathering all the positive/negative rewards to compute the standard deviation of these rewards
-        all_rewards = np.array(positive_rewards + negative_rewards)
-        sigma_r = all_rewards.std()
-        
-        # Sorting the rollouts by the max(r_pos, r_neg) and selecting the best directions
-        scores = {k:max(r_pos, r_neg) for k,(r_pos,r_neg) in enumerate(zip(positive_rewards, negative_rewards))}
-        order = sorted(scores.keys(), key = lambda x:scores[x])[:hp.nb_best_directions]
-        rollouts = [(positive_rewards[k], negative_rewards[k], deltas[k]) for k in order]
-        
-        # Updating our policy
-        policy.update(rollouts, sigma_r, args)
-        
-        # Printing the final reward of the policy after the update
-        reward_evaluation = explore(env, normalizer, policy, None, None, hp)
-        print('Step:', step, 'Reward:', reward_evaluation)
+  for step in range(hp.nb_steps):
+
+    # Initializing the perturbations deltas and the positive/negative rewards
+    deltas = policy.sample_deltas()
+    positive_rewards = [0] * hp.nb_directions
+    negative_rewards = [0] * hp.nb_directions
+
+    if parentPipes:
+      for k in range(hp.nb_directions):
+        parentPipe = parentPipes[k]
+        parentPipe.send([_EXPLORE, [normalizer, policy, hp, "positive", deltas[k]]])
+      for k in range(hp.nb_directions):
+        positive_rewards[k] = parentPipes[k].recv()[0]
+
+      for k in range(hp.nb_directions):
+        parentPipe = parentPipes[k]
+        parentPipe.send([_EXPLORE, [normalizer, policy, hp, "negative", deltas[k]]])
+      for k in range(hp.nb_directions):
+        negative_rewards[k] = parentPipes[k].recv()[0]
+
+    else:
+      # Getting the positive rewards in the positive directions
+      for k in range(hp.nb_directions):
+        positive_rewards[k] = explore(env, normalizer, policy, "positive", deltas[k], hp)
+
+      # Getting the negative rewards in the negative/opposite directions
+      for k in range(hp.nb_directions):
+        negative_rewards[k] = explore(env, normalizer, policy, "negative", deltas[k], hp)
+
+    # Gathering all the positive/negative rewards to compute the standard deviation of these rewards
+    all_rewards = np.array(positive_rewards + negative_rewards)
+    sigma_r = all_rewards.std()
+
+    # Sorting the rollouts by the max(r_pos, r_neg) and selecting the best directions
+    scores = {
+        k: max(r_pos, r_neg)
+        for k, (r_pos, r_neg) in enumerate(zip(positive_rewards, negative_rewards))
+    }
+    order = sorted(scores.keys(), key=lambda x: -scores[x])[:hp.nb_best_directions]
+    rollouts = [(positive_rewards[k], negative_rewards[k], deltas[k]) for k in order]
+
+    # Updating our policy
+    policy.update(rollouts, sigma_r, args)
+
+    # Printing the final reward of the policy after the update
+    reward_evaluation = explore(env, normalizer, policy, None, None, hp)
+    print('Step:', step, 'Reward:', reward_evaluation)
+
 
 # Running the main code
 
+
 def mkdir(base, name):
-    path = os.path.join(base, name)
-    if not os.path.exists(path):
-        os.makedirs(path)
-    return path
+  path = os.path.join(base, name)
+  if not os.path.exists(path):
+    os.makedirs(path)
+  return path
 
 
+if __name__ == "__main__":
+  mp.freeze_support()
 
+  parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument(
+      '--env', help='Gym environment name', type=str, default='HalfCheetahBulletEnv-v0')
+  parser.add_argument('--seed', help='RNG seed', type=int, default=1)
+  parser.add_argument('--render', help='OpenGL Visualizer', type=int, default=0)
+  parser.add_argument('--movie', help='rgb_array gym movie', type=int, default=0)
+  parser.add_argument('--steps', help='Number of steps', type=int, default=10000)
+  parser.add_argument('--policy', help='Starting policy file (npy)', type=str, default='')
+  parser.add_argument(
+      '--logdir', help='Directory root to log policy files (npy)', type=str, default='.')
+  parser.add_argument('--mp', help='Enable multiprocessing', type=int, default=1)
 
-if __name__ == "__main__":
-    mp.freeze_support()
-
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--env', help='Gym environment name', type=str, default='HalfCheetahBulletEnv-v0')
-    parser.add_argument('--seed', help='RNG seed', type=int, default=1)
-    parser.add_argument('--render', help='OpenGL Visualizer', type=int, default=0)
-    parser.add_argument('--movie',help='rgb_array gym movie',type=int, default=0)
-    parser.add_argument('--steps', help='Number of steps', type=int, default=10000)
-    parser.add_argument('--policy', help='Starting policy file (npy)', type=str, default='')
-    parser.add_argument('--logdir', help='Directory root to log policy files (npy)', type=str, default='.')
-    parser.add_argument('--mp', help='Enable multiprocessing', type=int, default=1)
-        
-    args = parser.parse_args()
-        
-    hp = Hp()
-    hp.env_name = args.env
-    hp.seed = args.seed
-    hp.nb_steps = args.steps
-    print("seed = ", hp.seed)
-    np.random.seed(hp.seed)
-
-    parentPipes = None
-    if args.mp:
-      num_processes = hp.nb_directions
-      processes = []
-      childPipes = []
-      parentPipes = []
-      
-      for pr in range (num_processes):
-        parentPipe, childPipe = Pipe()
-        parentPipes.append(parentPipe)
-        childPipes.append(childPipe)
-      
-      for rank in range(num_processes):
-          p = mp.Process(target=ExploreWorker, args=(rank,childPipes[rank], hp.env_name, args))
-          p.start()
-          processes.append(p)
-        
-    work_dir = mkdir('exp', 'brs')
-    monitor_dir = mkdir(work_dir, 'monitor')
-    env = gym.make(hp.env_name)
-    if args.render:
-      env.render(mode = "human")
-    if args.movie:
-      env = wrappers.Monitor(env, monitor_dir, force = True)
-    nb_inputs = env.observation_space.shape[0]
-    nb_outputs = env.action_space.shape[0]
-    policy = Policy(nb_inputs, nb_outputs,hp.env_name, args)
-    normalizer = Normalizer(nb_inputs)
-    
-    print("start training")
-    train(env, policy, normalizer, hp, parentPipes, args)
-
-    if args.mp:
-      for parentPipe in parentPipes:
-        parentPipe.send([_CLOSE,"pay2"])
-      
-      for p in processes:
-        p.join()
+  args = parser.parse_args()
+
+  hp = Hp()
+  hp.env_name = args.env
+  hp.seed = args.seed
+  hp.nb_steps = args.steps
+  print("seed = ", hp.seed)
+  np.random.seed(hp.seed)
+
+  parentPipes = None
+  if args.mp:
+    num_processes = hp.nb_directions
+    processes = []
+    childPipes = []
+    parentPipes = []
+
+    for pr in range(num_processes):
+      parentPipe, childPipe = Pipe()
+      parentPipes.append(parentPipe)
+      childPipes.append(childPipe)
+
+    for rank in range(num_processes):
+      p = mp.Process(target=ExploreWorker, args=(rank, childPipes[rank], hp.env_name, args))
+      p.start()
+      processes.append(p)
+
+  work_dir = mkdir('exp', 'brs')
+  monitor_dir = mkdir(work_dir, 'monitor')
+  env = gym.make(hp.env_name)
+  if args.render:
+    env.render(mode="human")
+  if args.movie:
+    env = wrappers.Monitor(env, monitor_dir, force=True)
+  nb_inputs = env.observation_space.shape[0]
+  nb_outputs = env.action_space.shape[0]
+  policy = Policy(nb_inputs, nb_outputs, hp.env_name, args)
+  normalizer = Normalizer(nb_inputs)
+
+  print("start training")
+  train(env, policy, normalizer, hp, parentPipes, args)
+
+  if args.mp:
+    for parentPipe in parentPipes:
+      parentPipe.send([_CLOSE, "pay2"])
+
+    for p in processes:
+      p.join()
diff --git a/examples/pybullet/gym/pybullet_envs/__init__.py b/examples/pybullet/gym/pybullet_envs/__init__.py
index e34508ed2..42c5d7ccf 100644
--- a/examples/pybullet/gym/pybullet_envs/__init__.py
+++ b/examples/pybullet/gym/pybullet_envs/__init__.py
@@ -1,25 +1,28 @@
 import gym
 from gym.envs.registration import registry, make, spec
-def register(id,*args,**kvargs):
-	if id in registry.env_specs:
-		return
-	else:
-		return gym.envs.registration.register(id,*args,**kvargs)
+
+
+def register(id, *args, **kvargs):
+  if id in registry.env_specs:
+    return
+  else:
+    return gym.envs.registration.register(id, *args, **kvargs)
+
 
 # ------------bullet-------------
 
 register(
-        id='HumanoidDeepMimicBulletEnv-v1',
-        entry_point='pybullet_envs.deep_mimic:HumanoidDeepMimicGymEnv',
-        max_episode_steps=1000,
-        reward_threshold=20000.0,
+    id='HumanoidDeepMimicBulletEnv-v1',
+    entry_point='pybullet_envs.deep_mimic:HumanoidDeepMimicGymEnv',
+    max_episode_steps=1000,
+    reward_threshold=20000.0,
 )
 
 register(
-	id='CartPoleBulletEnv-v1',
-	entry_point='pybullet_envs.bullet:CartPoleBulletEnv',
-        max_episode_steps=200,
-	reward_threshold=190.0,
+    id='CartPoleBulletEnv-v1',
+    entry_point='pybullet_envs.bullet:CartPoleBulletEnv',
+    max_episode_steps=200,
+    reward_threshold=190.0,
 )
 
 register(
@@ -36,6 +39,13 @@ register(
     reward_threshold=5.0,
 )
 
+register(
+    id='MinitaurExtendedEnv-v0',
+    entry_point='pybullet_envs.minitaur.envs:MinitaurExtendedEnv',
+    max_episode_steps=1000,
+    reward_threshold=5.0,
+)
+
 
 register(
     id='MinitaurReactiveEnv-v0',
@@ -44,7 +54,6 @@ register(
     reward_threshold=5.0,
 )
 
-
 register(
     id='MinitaurBallGymEnv-v0',
     entry_point='pybullet_envs.minitaur.envs:MinitaurBallGymEnv',
@@ -52,7 +61,6 @@ register(
     reward_threshold=5.0,
 )
 
-
 register(
     id='MinitaurTrottingEnv-v0',
     entry_point='pybullet_envs.minitaur.envs:MinitaurTrottingEnv',
@@ -81,8 +89,6 @@ register(
     reward_threshold=5.0,
 )
 
-
-
 register(
     id='RacecarBulletEnv-v0',
     entry_point='pybullet_envs.bullet:RacecarGymEnv',
@@ -91,128 +97,113 @@ register(
 )
 
 register(
-	id='RacecarZedBulletEnv-v0',
-	entry_point='pybullet_envs.bullet:RacecarZEDGymEnv',
-	max_episode_steps=1000,
-	reward_threshold=5.0,
+    id='RacecarZedBulletEnv-v0',
+    entry_point='pybullet_envs.bullet:RacecarZEDGymEnv',
+    max_episode_steps=1000,
+    reward_threshold=5.0,
 )
 
-
 register(
-	id='KukaBulletEnv-v0',
-	entry_point='pybullet_envs.bullet:KukaGymEnv',
-	max_episode_steps=1000,
-	reward_threshold=5.0,
+    id='KukaBulletEnv-v0',
+    entry_point='pybullet_envs.bullet:KukaGymEnv',
+    max_episode_steps=1000,
+    reward_threshold=5.0,
 )
 
 register(
-	id='KukaCamBulletEnv-v0',
-	entry_point='pybullet_envs.bullet:KukaCamGymEnv',
-	max_episode_steps=1000,
-	reward_threshold=5.0,
+    id='KukaCamBulletEnv-v0',
+    entry_point='pybullet_envs.bullet:KukaCamGymEnv',
+    max_episode_steps=1000,
+    reward_threshold=5.0,
 )
 
 register(
-	id='KukaDiverseObjectGrasping-v0',
-	entry_point='pybullet_envs.bullet:KukaDiverseObjectEnv',
-	max_episode_steps=1000,
-	reward_threshold=5.0,
+    id='KukaDiverseObjectGrasping-v0',
+    entry_point='pybullet_envs.bullet:KukaDiverseObjectEnv',
+    max_episode_steps=1000,
+    reward_threshold=5.0,
 )
 
 register(
-	id='InvertedPendulumBulletEnv-v0',
-	entry_point='pybullet_envs.gym_pendulum_envs:InvertedPendulumBulletEnv',
-	max_episode_steps=1000,
-	reward_threshold=950.0,
-	)
+    id='InvertedPendulumBulletEnv-v0',
+    entry_point='pybullet_envs.gym_pendulum_envs:InvertedPendulumBulletEnv',
+    max_episode_steps=1000,
+    reward_threshold=950.0,
+)
 
 register(
-	id='InvertedDoublePendulumBulletEnv-v0',
-	entry_point='pybullet_envs.gym_pendulum_envs:InvertedDoublePendulumBulletEnv',
-	max_episode_steps=1000,
-	reward_threshold=9100.0,
-	)
+    id='InvertedDoublePendulumBulletEnv-v0',
+    entry_point='pybullet_envs.gym_pendulum_envs:InvertedDoublePendulumBulletEnv',
+    max_episode_steps=1000,
+    reward_threshold=9100.0,
+)
 
 register(
-	id='InvertedPendulumSwingupBulletEnv-v0',
-	entry_point='pybullet_envs.gym_pendulum_envs:InvertedPendulumSwingupBulletEnv',
-	max_episode_steps=1000,
-	reward_threshold=800.0,
-	)
+    id='InvertedPendulumSwingupBulletEnv-v0',
+    entry_point='pybullet_envs.gym_pendulum_envs:InvertedPendulumSwingupBulletEnv',
+    max_episode_steps=1000,
+    reward_threshold=800.0,
+)
 
 register(
-	id='ReacherBulletEnv-v0',
-	entry_point='pybullet_envs.gym_manipulator_envs:ReacherBulletEnv',
-	max_episode_steps=150,
-	reward_threshold=18.0,
-	)
+    id='ReacherBulletEnv-v0',
+    entry_point='pybullet_envs.gym_manipulator_envs:ReacherBulletEnv',
+    max_episode_steps=150,
+    reward_threshold=18.0,
+)
 
 register(
-	id='PusherBulletEnv-v0',
-	entry_point='pybullet_envs.gym_manipulator_envs:PusherBulletEnv',
-	max_episode_steps=150,
-	reward_threshold=18.0,
+    id='PusherBulletEnv-v0',
+    entry_point='pybullet_envs.gym_manipulator_envs:PusherBulletEnv',
+    max_episode_steps=150,
+    reward_threshold=18.0,
 )
 
 register(
-	id='ThrowerBulletEnv-v0',
-	entry_point='pybullet_envs.gym_manipulator_envs:ThrowerBulletEnv',
-	max_episode_steps=100,
-	reward_threshold=18.0,
+    id='ThrowerBulletEnv-v0',
+    entry_point='pybullet_envs.gym_manipulator_envs:ThrowerBulletEnv',
+    max_episode_steps=100,
+    reward_threshold=18.0,
 )
 
 register(
-	id='StrikerBulletEnv-v0',
-	entry_point='pybullet_envs.gym_manipulator_envs:StrikerBulletEnv',
-	max_episode_steps=100,
-	reward_threshold=18.0,
+    id='StrikerBulletEnv-v0',
+    entry_point='pybullet_envs.gym_manipulator_envs:StrikerBulletEnv',
+    max_episode_steps=100,
+    reward_threshold=18.0,
 )
 
-register(
-	id='Walker2DBulletEnv-v0',
-	entry_point='pybullet_envs.gym_locomotion_envs:Walker2DBulletEnv',
-	max_episode_steps=1000,
-	reward_threshold=2500.0
-	)
-register(
-	id='HalfCheetahBulletEnv-v0',
-	entry_point='pybullet_envs.gym_locomotion_envs:HalfCheetahBulletEnv',
-	max_episode_steps=1000,
-	reward_threshold=3000.0
-	)
+register(id='Walker2DBulletEnv-v0',
+         entry_point='pybullet_envs.gym_locomotion_envs:Walker2DBulletEnv',
+         max_episode_steps=1000,
+         reward_threshold=2500.0)
+register(id='HalfCheetahBulletEnv-v0',
+         entry_point='pybullet_envs.gym_locomotion_envs:HalfCheetahBulletEnv',
+         max_episode_steps=1000,
+         reward_threshold=3000.0)
 
-register(
-	id='AntBulletEnv-v0',
-	entry_point='pybullet_envs.gym_locomotion_envs:AntBulletEnv',
-	max_episode_steps=1000,
-	reward_threshold=2500.0
-	)
+register(id='AntBulletEnv-v0',
+         entry_point='pybullet_envs.gym_locomotion_envs:AntBulletEnv',
+         max_episode_steps=1000,
+         reward_threshold=2500.0)
 
-register(
-	id='HopperBulletEnv-v0',
-	entry_point='pybullet_envs.gym_locomotion_envs:HopperBulletEnv',
-	max_episode_steps=1000,
-	reward_threshold=2500.0
-	)
+register(id='HopperBulletEnv-v0',
+         entry_point='pybullet_envs.gym_locomotion_envs:HopperBulletEnv',
+         max_episode_steps=1000,
+         reward_threshold=2500.0)
 
-register(
-	id='HumanoidBulletEnv-v0',
-	entry_point='pybullet_envs.gym_locomotion_envs:HumanoidBulletEnv',
-	max_episode_steps=1000
-	)
+register(id='HumanoidBulletEnv-v0',
+         entry_point='pybullet_envs.gym_locomotion_envs:HumanoidBulletEnv',
+         max_episode_steps=1000)
 
-register(
-	id='HumanoidFlagrunBulletEnv-v0',
-	entry_point='pybullet_envs.gym_locomotion_envs:HumanoidFlagrunBulletEnv',
-	max_episode_steps=1000,
-	reward_threshold=2000.0
-	)
+register(id='HumanoidFlagrunBulletEnv-v0',
+         entry_point='pybullet_envs.gym_locomotion_envs:HumanoidFlagrunBulletEnv',
+         max_episode_steps=1000,
+         reward_threshold=2000.0)
 
-register(
-	id='HumanoidFlagrunHarderBulletEnv-v0',
-	entry_point='pybullet_envs.gym_locomotion_envs:HumanoidFlagrunHarderBulletEnv',
-	max_episode_steps=1000
-	)
+register(id='HumanoidFlagrunHarderBulletEnv-v0',
+         entry_point='pybullet_envs.gym_locomotion_envs:HumanoidFlagrunHarderBulletEnv',
+         max_episode_steps=1000)
 
 #register(
 #	id='AtlasBulletEnv-v0',
@@ -220,6 +211,7 @@ register(
 #	max_episode_steps=1000
 #	)
 
+
 def getList():
-	btenvs = ['- ' + spec.id for spec in gym.envs.registry.all() if spec.id.find('Bullet')>=0]
-	return btenvs
+  btenvs = ['- ' + spec.id for spec in gym.envs.registry.all() if spec.id.find('Bullet') >= 0]
+  return btenvs
diff --git a/examples/pybullet/gym/pybullet_envs/agents/__init__.py b/examples/pybullet/gym/pybullet_envs/agents/__init__.py
index c29ce33fe..9fc2d2951 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/__init__.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/__init__.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Executable scripts for reinforcement learning."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/agents/configs.py b/examples/pybullet/gym/pybullet_envs/agents/configs.py
index 6ac584db6..992d226ef 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/configs.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/configs.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Example configurations using the PPO algorithm."""
 
 from __future__ import absolute_import
@@ -29,6 +28,7 @@ import pybullet_envs.bullet.minitaur_gym_env as minitaur_gym_env
 import pybullet_envs
 import tensorflow as tf
 
+
 def default():
   """Default configuration for PPO."""
   # General
@@ -38,10 +38,7 @@ def default():
   use_gpu = False
   # Network
   network = networks.feed_forward_gaussian
-  weight_summaries = dict(
-      all=r'.*',
-      policy=r'.*/policy/.*',
-      value=r'.*/value/.*')
+  weight_summaries = dict(all=r'.*', policy=r'.*/policy/.*', value=r'.*/value/.*')
   policy_layers = 200, 100
   value_layers = 200, 100
   init_mean_factor = 0.1
@@ -52,7 +49,7 @@ def default():
   optimizer = tf.train.AdamOptimizer
   update_epochs_policy = 64
   update_epochs_value = 64
-  learning_rate = 1e-4  
+  learning_rate = 1e-4
   # Losses
   discount = 0.995
   kl_target = 1e-2
@@ -69,6 +66,7 @@ def pybullet_pendulum():
   steps = 5e7  # 50M
   return locals()
 
+
 def pybullet_doublependulum():
   locals().update(default())
   env = 'InvertedDoublePendulumBulletEnv-v0'
@@ -76,6 +74,7 @@ def pybullet_doublependulum():
   steps = 5e7  # 50M
   return locals()
 
+
 def pybullet_pendulumswingup():
   locals().update(default())
   env = 'InvertedPendulumSwingupBulletEnv-v0'
@@ -83,6 +82,7 @@ def pybullet_pendulumswingup():
   steps = 5e7  # 50M
   return locals()
 
+
 def pybullet_cheetah():
   """Configuration for MuJoCo's half cheetah task."""
   locals().update(default())
@@ -92,6 +92,7 @@ def pybullet_cheetah():
   steps = 1e8  # 100M
   return locals()
 
+
 def pybullet_ant():
   locals().update(default())
   env = 'AntBulletEnv-v0'
@@ -99,6 +100,7 @@ def pybullet_ant():
   steps = 5e7  # 50M
   return locals()
 
+
 def pybullet_kuka_grasping():
   """Configuration for Bullet Kuka grasping task."""
   locals().update(default())
@@ -113,7 +115,7 @@ def pybullet_racecar():
   """Configuration for Bullet MIT Racecar task."""
   locals().update(default())
   # Environment
-  env = 'RacecarBulletEnv-v0' #functools.partial(racecarGymEnv.RacecarGymEnv, isDiscrete=False, renders=True)
+  env = 'RacecarBulletEnv-v0'  #functools.partial(racecarGymEnv.RacecarGymEnv, isDiscrete=False, renders=True)
   max_length = 10
   steps = 1e7  # 10M
   return locals()
@@ -132,29 +134,27 @@ def pybullet_minitaur():
   """Configuration specific to minitaur_gym_env.MinitaurBulletEnv class."""
   locals().update(default())
   randomizer = (minitaur_env_randomizer.MinitaurEnvRandomizer())
-  env = functools.partial(
-      minitaur_gym_env.MinitaurBulletEnv,
-      accurate_motor_model_enabled=True,
-      motor_overheat_protection=True,
-      pd_control_enabled=True,
-      env_randomizer=randomizer,
-      render=False)
+  env = functools.partial(minitaur_gym_env.MinitaurBulletEnv,
+                          accurate_motor_model_enabled=True,
+                          motor_overheat_protection=True,
+                          pd_control_enabled=True,
+                          env_randomizer=randomizer,
+                          render=False)
   max_length = 1000
   steps = 3e7  # 30M
   return locals()
 
+
 def pybullet_duck_minitaur():
   """Configuration specific to minitaur_gym_env.MinitaurBulletDuckEnv class."""
   locals().update(default())
   randomizer = (minitaur_env_randomizer.MinitaurEnvRandomizer())
-  env = functools.partial(
-      minitaur_gym_env.MinitaurBulletDuckEnv,
-      accurate_motor_model_enabled=True,
-      motor_overheat_protection=True,
-      pd_control_enabled=True,
-      env_randomizer=randomizer,
-      render=False)
+  env = functools.partial(minitaur_gym_env.MinitaurBulletDuckEnv,
+                          accurate_motor_model_enabled=True,
+                          motor_overheat_protection=True,
+                          pd_control_enabled=True,
+                          env_randomizer=randomizer,
+                          render=False)
   max_length = 1000
   steps = 3e7  # 30M
   return locals()
-
diff --git a/examples/pybullet/gym/pybullet_envs/agents/networks.py b/examples/pybullet/gym/pybullet_envs/agents/networks.py
index 3d5de1fbb..3b6802d02 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/networks.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/networks.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Network definitions for the PPO algorithm."""
 
 from __future__ import absolute_import
@@ -24,13 +23,10 @@ import operator
 
 import tensorflow as tf
 
-
-NetworkOutput = collections.namedtuple(
-    'NetworkOutput', 'policy, mean, logstd, value, state')
+NetworkOutput = collections.namedtuple('NetworkOutput', 'policy, mean, logstd, value, state')
 
 
-def feed_forward_gaussian(
-    config, action_size, observations, unused_length, state=None):
+def feed_forward_gaussian(config, action_size, observations, unused_length, state=None):
   """Independent feed forward networks for policy and value.
 
   The policy network outputs the mean action and the log standard deviation
@@ -50,20 +46,22 @@ def feed_forward_gaussian(
       factor=config.init_mean_factor)
   logstd_initializer = tf.random_normal_initializer(config.init_logstd, 1e-10)
   flat_observations = tf.reshape(observations, [
-      tf.shape(observations)[0], tf.shape(observations)[1],
-      functools.reduce(operator.mul, observations.shape.as_list()[2:], 1)])
+      tf.shape(observations)[0],
+      tf.shape(observations)[1],
+      functools.reduce(operator.mul,
+                       observations.shape.as_list()[2:], 1)
+  ])
   with tf.variable_scope('policy'):
     x = flat_observations
     for size in config.policy_layers:
       x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
-    mean = tf.contrib.layers.fully_connected(
-        x, action_size, tf.tanh,
-        weights_initializer=mean_weights_initializer)
-    logstd = tf.get_variable(
-        'logstd', mean.shape[2:], tf.float32, logstd_initializer)
-    logstd = tf.tile(
-        logstd[None, None],
-        [tf.shape(mean)[0], tf.shape(mean)[1]] + [1] * (mean.shape.ndims - 2))
+    mean = tf.contrib.layers.fully_connected(x,
+                                             action_size,
+                                             tf.tanh,
+                                             weights_initializer=mean_weights_initializer)
+    logstd = tf.get_variable('logstd', mean.shape[2:], tf.float32, logstd_initializer)
+    logstd = tf.tile(logstd[None, None],
+                     [tf.shape(mean)[0], tf.shape(mean)[1]] + [1] * (mean.shape.ndims - 2))
   with tf.variable_scope('value'):
     x = flat_observations
     for size in config.value_layers:
@@ -72,13 +70,11 @@ def feed_forward_gaussian(
   mean = tf.check_numerics(mean, 'mean')
   logstd = tf.check_numerics(logstd, 'logstd')
   value = tf.check_numerics(value, 'value')
-  policy = tf.contrib.distributions.MultivariateNormalDiag(
-      mean, tf.exp(logstd))
+  policy = tf.contrib.distributions.MultivariateNormalDiag(mean, tf.exp(logstd))
   return NetworkOutput(policy, mean, logstd, value, state)
 
 
-def recurrent_gaussian(
-    config, action_size, observations, length, state=None):
+def recurrent_gaussian(config, action_size, observations, length, state=None):
   """Independent recurrent policy and feed forward value networks.
 
   The policy network outputs the mean action and the log standard deviation
@@ -100,21 +96,23 @@ def recurrent_gaussian(
   logstd_initializer = tf.random_normal_initializer(config.init_logstd, 1e-10)
   cell = tf.contrib.rnn.GRUBlockCell(config.policy_layers[-1])
   flat_observations = tf.reshape(observations, [
-      tf.shape(observations)[0], tf.shape(observations)[1],
-      functools.reduce(operator.mul, observations.shape.as_list()[2:], 1)])
+      tf.shape(observations)[0],
+      tf.shape(observations)[1],
+      functools.reduce(operator.mul,
+                       observations.shape.as_list()[2:], 1)
+  ])
   with tf.variable_scope('policy'):
     x = flat_observations
     for size in config.policy_layers[:-1]:
       x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
     x, state = tf.nn.dynamic_rnn(cell, x, length, state, tf.float32)
-    mean = tf.contrib.layers.fully_connected(
-        x, action_size, tf.tanh,
-        weights_initializer=mean_weights_initializer)
-    logstd = tf.get_variable(
-        'logstd', mean.shape[2:], tf.float32, logstd_initializer)
-    logstd = tf.tile(
-        logstd[None, None],
-        [tf.shape(mean)[0], tf.shape(mean)[1]] + [1] * (mean.shape.ndims - 2))
+    mean = tf.contrib.layers.fully_connected(x,
+                                             action_size,
+                                             tf.tanh,
+                                             weights_initializer=mean_weights_initializer)
+    logstd = tf.get_variable('logstd', mean.shape[2:], tf.float32, logstd_initializer)
+    logstd = tf.tile(logstd[None, None],
+                     [tf.shape(mean)[0], tf.shape(mean)[1]] + [1] * (mean.shape.ndims - 2))
   with tf.variable_scope('value'):
     x = flat_observations
     for size in config.value_layers:
@@ -123,7 +121,6 @@ def recurrent_gaussian(
   mean = tf.check_numerics(mean, 'mean')
   logstd = tf.check_numerics(logstd, 'logstd')
   value = tf.check_numerics(value, 'value')
-  policy = tf.contrib.distributions.MultivariateNormalDiag(
-      mean, tf.exp(logstd))
+  policy = tf.contrib.distributions.MultivariateNormalDiag(mean, tf.exp(logstd))
   # assert state.shape.as_list()[0] is not None
   return NetworkOutput(policy, mean, logstd, value, state)
diff --git a/examples/pybullet/gym/pybullet_envs/agents/ppo/__init__.py b/examples/pybullet/gym/pybullet_envs/agents/ppo/__init__.py
index 26a87baf9..6f4c222dd 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/ppo/__init__.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/ppo/__init__.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Proximal Policy Optimization algorithm."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/agents/ppo/algorithm.py b/examples/pybullet/gym/pybullet_envs/agents/ppo/algorithm.py
index 9e97425f9..c6921095f 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/ppo/algorithm.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/ppo/algorithm.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Proximal Policy Optimization algorithm.
 
 Based on John Schulman's implementation in Python and Theano:
@@ -49,51 +48,51 @@ class PPOAlgorithm(object):
     self._is_training = is_training
     self._should_log = should_log
     self._config = config
-    self._observ_filter = normalize.StreamingNormalize(
-        self._batch_env.observ[0], center=True, scale=True, clip=5,
-        name='normalize_observ')
-    self._reward_filter = normalize.StreamingNormalize(
-        self._batch_env.reward[0], center=False, scale=True, clip=10,
-        name='normalize_reward')
+    self._observ_filter = normalize.StreamingNormalize(self._batch_env.observ[0],
+                                                       center=True,
+                                                       scale=True,
+                                                       clip=5,
+                                                       name='normalize_observ')
+    self._reward_filter = normalize.StreamingNormalize(self._batch_env.reward[0],
+                                                       center=False,
+                                                       scale=True,
+                                                       clip=10,
+                                                       name='normalize_reward')
     # Memory stores tuple of observ, action, mean, logstd, reward.
-    template = (
-        self._batch_env.observ[0], self._batch_env.action[0],
-        self._batch_env.action[0], self._batch_env.action[0],
-        self._batch_env.reward[0])
-    self._memory = memory.EpisodeMemory(
-        template, config.update_every, config.max_length, 'memory')
+    template = (self._batch_env.observ[0], self._batch_env.action[0], self._batch_env.action[0],
+                self._batch_env.action[0], self._batch_env.reward[0])
+    self._memory = memory.EpisodeMemory(template, config.update_every, config.max_length, 'memory')
     self._memory_index = tf.Variable(0, False)
     use_gpu = self._config.use_gpu and utility.available_gpus()
     with tf.device('/gpu:0' if use_gpu else '/cpu:0'):
       # Create network variables for later calls to reuse.
       action_size = self._batch_env.action.shape[1].value
-      self._network = tf.make_template(
-          'network', functools.partial(config.network, config, action_size))
+      self._network = tf.make_template('network',
+                                       functools.partial(config.network, config, action_size))
       output = self._network(
-          tf.zeros_like(self._batch_env.observ)[:, None],
-          tf.ones(len(self._batch_env)))
+          tf.zeros_like(self._batch_env.observ)[:, None], tf.ones(len(self._batch_env)))
       with tf.variable_scope('ppo_temporary'):
-        self._episodes = memory.EpisodeMemory(
-            template, len(batch_env), config.max_length, 'episodes')
+        self._episodes = memory.EpisodeMemory(template, len(batch_env), config.max_length,
+                                              'episodes')
         if output.state is None:
           self._last_state = None
         else:
           # Ensure the batch dimension is set.
           tf.contrib.framework.nest.map_structure(
-              lambda x: x.set_shape([len(batch_env)] + x.shape.as_list()[1:]),
-              output.state)
+              lambda x: x.set_shape([len(batch_env)] + x.shape.as_list()[1:]), output.state)
           # pylint: disable=undefined-variable
           self._last_state = tf.contrib.framework.nest.map_structure(
-              lambda x: tf.Variable(lambda: tf.zeros_like(x), False),
-              output.state)
-        self._last_action = tf.Variable(
-            tf.zeros_like(self._batch_env.action), False, name='last_action')
-        self._last_mean = tf.Variable(
-            tf.zeros_like(self._batch_env.action), False, name='last_mean')
-        self._last_logstd = tf.Variable(
-            tf.zeros_like(self._batch_env.action), False, name='last_logstd')
-    self._penalty = tf.Variable(
-        self._config.kl_init_penalty, False, dtype=tf.float32)
+              lambda x: tf.Variable(lambda: tf.zeros_like(x), False), output.state)
+        self._last_action = tf.Variable(tf.zeros_like(self._batch_env.action),
+                                        False,
+                                        name='last_action')
+        self._last_mean = tf.Variable(tf.zeros_like(self._batch_env.action),
+                                      False,
+                                      name='last_mean')
+        self._last_logstd = tf.Variable(tf.zeros_like(self._batch_env.action),
+                                        False,
+                                        name='last_logstd')
+    self._penalty = tf.Variable(self._config.kl_init_penalty, False, dtype=tf.float32)
     self._optimizer = self._config.optimizer(self._config.learning_rate)
 
   def begin_episode(self, agent_indices):
@@ -109,8 +108,7 @@ class PPOAlgorithm(object):
       if self._last_state is None:
         reset_state = tf.no_op()
       else:
-        reset_state = utility.reinit_nested_vars(
-            self._last_state, agent_indices)
+        reset_state = utility.reinit_nested_vars(self._last_state, agent_indices)
       reset_buffer = self._episodes.clear(agent_indices)
       with tf.control_dependencies([reset_state, reset_buffer]):
         return tf.constant('')
@@ -130,36 +128,33 @@ class PPOAlgorithm(object):
       if self._last_state is None:
         state = None
       else:
-        state = tf.contrib.framework.nest.map_structure(
-            lambda x: tf.gather(x, agent_indices), self._last_state)
+        state = tf.contrib.framework.nest.map_structure(lambda x: tf.gather(x, agent_indices),
+                                                        self._last_state)
       output = self._network(observ[:, None], tf.ones(observ.shape[0]), state)
-      action = tf.cond(
-          self._is_training, output.policy.sample, lambda: output.mean)
+      action = tf.cond(self._is_training, output.policy.sample, lambda: output.mean)
       logprob = output.policy.log_prob(action)[:, 0]
       # pylint: disable=g-long-lambda
-      summary = tf.cond(self._should_log, lambda: tf.summary.merge([
-          tf.summary.histogram('mean', output.mean[:, 0]),
-          tf.summary.histogram('std', tf.exp(output.logstd[:, 0])),
-          tf.summary.histogram('action', action[:, 0]),
-          tf.summary.histogram('logprob', logprob)]), str)
+      summary = tf.cond(
+          self._should_log, lambda: tf.summary.merge([
+              tf.summary.histogram('mean', output.mean[:, 0]),
+              tf.summary.histogram('std', tf.exp(output.logstd[:, 0])),
+              tf.summary.histogram('action', action[:, 0]),
+              tf.summary.histogram('logprob', logprob)
+          ]), str)
       # Remember current policy to append to memory in the experience callback.
       if self._last_state is None:
         assign_state = tf.no_op()
       else:
-        assign_state = utility.assign_nested_vars(
-            self._last_state, output.state, agent_indices)
+        assign_state = utility.assign_nested_vars(self._last_state, output.state, agent_indices)
       with tf.control_dependencies([
           assign_state,
-          tf.scatter_update(
-              self._last_action, agent_indices, action[:, 0]),
-          tf.scatter_update(
-              self._last_mean, agent_indices, output.mean[:, 0]),
-          tf.scatter_update(
-              self._last_logstd, agent_indices, output.logstd[:, 0])]):
+          tf.scatter_update(self._last_action, agent_indices, action[:, 0]),
+          tf.scatter_update(self._last_mean, agent_indices, output.mean[:, 0]),
+          tf.scatter_update(self._last_logstd, agent_indices, output.logstd[:, 0])
+      ]):
         return tf.check_numerics(action[:, 0], 'action'), tf.identity(summary)
 
-  def experience(
-      self, agent_indices, observ, action, reward, unused_done, unused_nextob):
+  def experience(self, agent_indices, observ, action, reward, unused_done, unused_nextob):
     """Process the transition tuple of the current step.
 
     When training, add the current transition tuple to the memory and update
@@ -181,34 +176,36 @@ class PPOAlgorithm(object):
       return tf.cond(
           self._is_training,
           # pylint: disable=g-long-lambda
-          lambda: self._define_experience(
-              agent_indices, observ, action, reward), str)
+          lambda: self._define_experience(agent_indices, observ, action, reward),
+          str)
 
   def _define_experience(self, agent_indices, observ, action, reward):
     """Implement the branch of experience() entered during training."""
-    update_filters = tf.summary.merge([
-        self._observ_filter.update(observ),
-        self._reward_filter.update(reward)])
+    update_filters = tf.summary.merge(
+        [self._observ_filter.update(observ),
+         self._reward_filter.update(reward)])
     with tf.control_dependencies([update_filters]):
       if self._config.train_on_agent_action:
         # NOTE: Doesn't seem to change much.
         action = self._last_action
-      batch = (
-          observ, action, tf.gather(self._last_mean, agent_indices),
-          tf.gather(self._last_logstd, agent_indices), reward)
+      batch = (observ, action, tf.gather(self._last_mean,
+                                         agent_indices), tf.gather(self._last_logstd,
+                                                                   agent_indices), reward)
       append = self._episodes.append(batch, agent_indices)
     with tf.control_dependencies([append]):
       norm_observ = self._observ_filter.transform(observ)
       norm_reward = tf.reduce_mean(self._reward_filter.transform(reward))
       # pylint: disable=g-long-lambda
-      summary = tf.cond(self._should_log, lambda: tf.summary.merge([
-          update_filters,
-          self._observ_filter.summary(),
-          self._reward_filter.summary(),
-          tf.summary.scalar('memory_size', self._memory_index),
-          tf.summary.histogram('normalized_observ', norm_observ),
-          tf.summary.histogram('action', self._last_action),
-          tf.summary.scalar('normalized_reward', norm_reward)]), str)
+      summary = tf.cond(
+          self._should_log, lambda: tf.summary.merge([
+              update_filters,
+              self._observ_filter.summary(),
+              self._reward_filter.summary(),
+              tf.summary.scalar('memory_size', self._memory_index),
+              tf.summary.histogram('normalized_observ', norm_observ),
+              tf.summary.histogram('action', self._last_action),
+              tf.summary.scalar('normalized_reward', norm_reward)
+          ]), str)
       return summary
 
   def end_episode(self, agent_indices):
@@ -226,20 +223,16 @@ class PPOAlgorithm(object):
        Summary tensor.
     """
     with tf.name_scope('end_episode/'):
-      return tf.cond(
-          self._is_training,
-          lambda: self._define_end_episode(agent_indices), str)
+      return tf.cond(self._is_training, lambda: self._define_end_episode(agent_indices), str)
 
   def _define_end_episode(self, agent_indices):
     """Implement the branch of end_episode() entered during training."""
     episodes, length = self._episodes.data(agent_indices)
     space_left = self._config.update_every - self._memory_index
-    use_episodes = tf.range(tf.minimum(
-        tf.shape(agent_indices)[0], space_left))
+    use_episodes = tf.range(tf.minimum(tf.shape(agent_indices)[0], space_left))
     episodes = [tf.gather(elem, use_episodes) for elem in episodes]
-    append = self._memory.replace(
-        episodes, tf.gather(length, use_episodes),
-        use_episodes + self._memory_index)
+    append = self._memory.replace(episodes, tf.gather(length, use_episodes),
+                                  use_episodes + self._memory_index)
     with tf.control_dependencies([append]):
       inc_index = self._memory_index.assign_add(tf.shape(use_episodes)[0])
     with tf.control_dependencies([inc_index]):
@@ -256,8 +249,7 @@ class PPOAlgorithm(object):
       Summary tensor.
     """
     with tf.name_scope('training'):
-      assert_full = tf.assert_equal(
-          self._memory_index, self._config.update_every)
+      assert_full = tf.assert_equal(self._memory_index, self._config.update_every)
       with tf.control_dependencies([assert_full]):
         data = self._memory.data()
       (observ, action, old_mean, old_logstd, reward), length = data
@@ -265,22 +257,18 @@ class PPOAlgorithm(object):
         length = tf.identity(length)
       observ = self._observ_filter.transform(observ)
       reward = self._reward_filter.transform(reward)
-      update_summary = self._perform_update_steps(
-          observ, action, old_mean, old_logstd, reward, length)
+      update_summary = self._perform_update_steps(observ, action, old_mean, old_logstd, reward,
+                                                  length)
       with tf.control_dependencies([update_summary]):
-        penalty_summary = self._adjust_penalty(
-            observ, old_mean, old_logstd, length)
+        penalty_summary = self._adjust_penalty(observ, old_mean, old_logstd, length)
       with tf.control_dependencies([penalty_summary]):
-        clear_memory = tf.group(
-            self._memory.clear(), self._memory_index.assign(0))
+        clear_memory = tf.group(self._memory.clear(), self._memory_index.assign(0))
       with tf.control_dependencies([clear_memory]):
-        weight_summary = utility.variable_summaries(
-            tf.trainable_variables(), self._config.weight_summaries)
-        return tf.summary.merge([
-            update_summary, penalty_summary, weight_summary])
+        weight_summary = utility.variable_summaries(tf.trainable_variables(),
+                                                    self._config.weight_summaries)
+        return tf.summary.merge([update_summary, penalty_summary, weight_summary])
 
-  def _perform_update_steps(
-      self, observ, action, old_mean, old_logstd, reward, length):
+  def _perform_update_steps(self, observ, action, old_mean, old_logstd, reward, length):
     """Perform multiple update steps of value function and policy.
 
     The advantage is computed once at the beginning and shared across
@@ -298,37 +286,29 @@ class PPOAlgorithm(object):
     Returns:
       Summary tensor.
     """
-    return_ = utility.discounted_return(
-        reward, length, self._config.discount)
+    return_ = utility.discounted_return(reward, length, self._config.discount)
     value = self._network(observ, length).value
     if self._config.gae_lambda:
-      advantage = utility.lambda_return(
-          reward, value, length, self._config.discount,
-          self._config.gae_lambda)
+      advantage = utility.lambda_return(reward, value, length, self._config.discount,
+                                        self._config.gae_lambda)
     else:
       advantage = return_ - value
     mean, variance = tf.nn.moments(advantage, axes=[0, 1], keep_dims=True)
     advantage = (advantage - mean) / (tf.sqrt(variance) + 1e-8)
-    advantage = tf.Print(
-        advantage, [tf.reduce_mean(return_), tf.reduce_mean(value)],
-        'return and value: ')
-    advantage = tf.Print(
-        advantage, [tf.reduce_mean(advantage)],
-        'normalized advantage: ')
+    advantage = tf.Print(advantage,
+                         [tf.reduce_mean(return_), tf.reduce_mean(value)], 'return and value: ')
+    advantage = tf.Print(advantage, [tf.reduce_mean(advantage)], 'normalized advantage: ')
     # pylint: disable=g-long-lambda
-    value_loss, policy_loss, summary = tf.scan(
-        lambda _1, _2: self._update_step(
-            observ, action, old_mean, old_logstd, reward, advantage, length),
-        tf.range(self._config.update_epochs),
-        [0., 0., ''], parallel_iterations=1)
-    print_losses = tf.group(
-        tf.Print(0, [tf.reduce_mean(value_loss)], 'value loss: '),
-        tf.Print(0, [tf.reduce_mean(policy_loss)], 'policy loss: '))
+    value_loss, policy_loss, summary = tf.scan(lambda _1, _2: self._update_step(
+        observ, action, old_mean, old_logstd, reward, advantage, length),
+                                               tf.range(self._config.update_epochs), [0., 0., ''],
+                                               parallel_iterations=1)
+    print_losses = tf.group(tf.Print(0, [tf.reduce_mean(value_loss)], 'value loss: '),
+                            tf.Print(0, [tf.reduce_mean(policy_loss)], 'policy loss: '))
     with tf.control_dependencies([value_loss, policy_loss, print_losses]):
       return summary[self._config.update_epochs // 2]
 
-  def _update_step(
-      self, observ, action, old_mean, old_logstd, reward, advantage, length):
+  def _update_step(self, observ, action, old_mean, old_logstd, reward, advantage, length):
     """Compute the current combined loss and perform a gradient update step.
 
     Args:
@@ -345,27 +325,20 @@ class PPOAlgorithm(object):
     """
     value_loss, value_summary = self._value_loss(observ, reward, length)
     network = self._network(observ, length)
-    policy_loss, policy_summary = self._policy_loss(
-        network.mean, network.logstd, old_mean, old_logstd, action,
-        advantage, length)
-    value_gradients, value_variables = (
-        zip(*self._optimizer.compute_gradients(value_loss)))
-    policy_gradients, policy_variables = (
-        zip(*self._optimizer.compute_gradients(policy_loss)))
+    policy_loss, policy_summary = self._policy_loss(network.mean, network.logstd, old_mean,
+                                                    old_logstd, action, advantage, length)
+    value_gradients, value_variables = (zip(*self._optimizer.compute_gradients(value_loss)))
+    policy_gradients, policy_variables = (zip(*self._optimizer.compute_gradients(policy_loss)))
     all_gradients = value_gradients + policy_gradients
     all_variables = value_variables + policy_variables
-    optimize = self._optimizer.apply_gradients(
-        zip(all_gradients, all_variables))
+    optimize = self._optimizer.apply_gradients(zip(all_gradients, all_variables))
     summary = tf.summary.merge([
         value_summary, policy_summary,
-        tf.summary.scalar(
-            'value_gradient_norm', tf.global_norm(value_gradients)),
-        tf.summary.scalar(
-            'policy_gradient_norm', tf.global_norm(policy_gradients)),
-        utility.gradient_summaries(
-            zip(value_gradients, value_variables), dict(value=r'.*')),
-        utility.gradient_summaries(
-            zip(policy_gradients, policy_variables), dict(policy=r'.*'))])
+        tf.summary.scalar('value_gradient_norm', tf.global_norm(value_gradients)),
+        tf.summary.scalar('policy_gradient_norm', tf.global_norm(policy_gradients)),
+        utility.gradient_summaries(zip(value_gradients, value_variables), dict(value=r'.*')),
+        utility.gradient_summaries(zip(policy_gradients, policy_variables), dict(policy=r'.*'))
+    ])
     with tf.control_dependencies([optimize]):
       return [tf.identity(x) for x in (value_loss, policy_loss, summary)]
 
@@ -385,18 +358,17 @@ class PPOAlgorithm(object):
     """
     with tf.name_scope('value_loss'):
       value = self._network(observ, length).value
-      return_ = utility.discounted_return(
-          reward, length, self._config.discount)
+      return_ = utility.discounted_return(reward, length, self._config.discount)
       advantage = return_ - value
-      value_loss = 0.5 * self._mask(advantage ** 2, length)
+      value_loss = 0.5 * self._mask(advantage**2, length)
       summary = tf.summary.merge([
           tf.summary.histogram('value_loss', value_loss),
-          tf.summary.scalar('avg_value_loss', tf.reduce_mean(value_loss))])
+          tf.summary.scalar('avg_value_loss', tf.reduce_mean(value_loss))
+      ])
       value_loss = tf.reduce_mean(value_loss)
       return tf.check_numerics(value_loss, 'value_loss'), summary
 
-  def _policy_loss(
-      self, mean, logstd, old_mean, old_logstd, action, advantage, length):
+  def _policy_loss(self, mean, logstd, old_mean, old_logstd, action, advantage, length):
     """Compute the policy loss composed of multiple components.
 
     1. The policy gradient loss is importance sampled from the data-collecting
@@ -420,24 +392,20 @@ class PPOAlgorithm(object):
     """
     with tf.name_scope('policy_loss'):
       entropy = utility.diag_normal_entropy(mean, logstd)
-      kl = tf.reduce_mean(self._mask(utility.diag_normal_kl(
-          old_mean, old_logstd, mean, logstd), length), 1)
+      kl = tf.reduce_mean(
+          self._mask(utility.diag_normal_kl(old_mean, old_logstd, mean, logstd), length), 1)
       policy_gradient = tf.exp(
           utility.diag_normal_logpdf(mean, logstd, action) -
           utility.diag_normal_logpdf(old_mean, old_logstd, action))
-      surrogate_loss = -tf.reduce_mean(self._mask(
-          policy_gradient * tf.stop_gradient(advantage), length), 1)
+      surrogate_loss = -tf.reduce_mean(
+          self._mask(policy_gradient * tf.stop_gradient(advantage), length), 1)
       kl_penalty = self._penalty * kl
       cutoff_threshold = self._config.kl_target * self._config.kl_cutoff_factor
-      cutoff_count = tf.reduce_sum(
-          tf.cast(kl > cutoff_threshold, tf.int32))
-      with tf.control_dependencies([tf.cond(
-          cutoff_count > 0,
-          lambda: tf.Print(0, [cutoff_count], 'kl cutoff! '), int)]):
-        kl_cutoff = (
-            self._config.kl_cutoff_coef *
-            tf.cast(kl > cutoff_threshold, tf.float32) *
-            (kl - cutoff_threshold) ** 2)
+      cutoff_count = tf.reduce_sum(tf.cast(kl > cutoff_threshold, tf.int32))
+      with tf.control_dependencies(
+          [tf.cond(cutoff_count > 0, lambda: tf.Print(0, [cutoff_count], 'kl cutoff! '), int)]):
+        kl_cutoff = (self._config.kl_cutoff_coef * tf.cast(kl > cutoff_threshold, tf.float32) *
+                     (kl - cutoff_threshold)**2)
       policy_loss = surrogate_loss + kl_penalty + kl_cutoff
       summary = tf.summary.merge([
           tf.summary.histogram('entropy', entropy),
@@ -449,7 +417,8 @@ class PPOAlgorithm(object):
           tf.summary.histogram('policy_loss', policy_loss),
           tf.summary.scalar('avg_surr_loss', tf.reduce_mean(surrogate_loss)),
           tf.summary.scalar('avg_kl_penalty', tf.reduce_mean(kl_penalty)),
-          tf.summary.scalar('avg_policy_loss', tf.reduce_mean(policy_loss))])
+          tf.summary.scalar('avg_policy_loss', tf.reduce_mean(policy_loss))
+      ])
       policy_loss = tf.reduce_mean(policy_loss, 0)
       return tf.check_numerics(policy_loss, 'policy_loss'), summary
 
@@ -471,30 +440,30 @@ class PPOAlgorithm(object):
     """
     with tf.name_scope('adjust_penalty'):
       network = self._network(observ, length)
-      assert_change = tf.assert_equal(
-          tf.reduce_all(tf.equal(network.mean, old_mean)), False,
-          message='policy should change')
+      assert_change = tf.assert_equal(tf.reduce_all(tf.equal(network.mean, old_mean)),
+                                      False,
+                                      message='policy should change')
       print_penalty = tf.Print(0, [self._penalty], 'current penalty: ')
       with tf.control_dependencies([assert_change, print_penalty]):
-        kl_change = tf.reduce_mean(self._mask(utility.diag_normal_kl(
-            old_mean, old_logstd, network.mean, network.logstd), length))
+        kl_change = tf.reduce_mean(
+            self._mask(utility.diag_normal_kl(old_mean, old_logstd, network.mean, network.logstd),
+                       length))
         kl_change = tf.Print(kl_change, [kl_change], 'kl change: ')
         maybe_increase = tf.cond(
             kl_change > 1.3 * self._config.kl_target,
             # pylint: disable=g-long-lambda
-            lambda: tf.Print(self._penalty.assign(
-                self._penalty * 1.5), [0], 'increase penalty '),
+            lambda: tf.Print(self._penalty.assign(self._penalty * 1.5), [0], 'increase penalty '),
             float)
         maybe_decrease = tf.cond(
             kl_change < 0.7 * self._config.kl_target,
             # pylint: disable=g-long-lambda
-            lambda: tf.Print(self._penalty.assign(
-                self._penalty / 1.5), [0], 'decrease penalty '),
+            lambda: tf.Print(self._penalty.assign(self._penalty / 1.5), [0], 'decrease penalty '),
             float)
       with tf.control_dependencies([maybe_increase, maybe_decrease]):
         return tf.summary.merge([
             tf.summary.scalar('kl_change', kl_change),
-            tf.summary.scalar('penalty', self._penalty)])
+            tf.summary.scalar('penalty', self._penalty)
+        ])
 
   def _mask(self, tensor, length):
     """Set padding elements of a batch of sequences to zero.
diff --git a/examples/pybullet/gym/pybullet_envs/agents/ppo/memory.py b/examples/pybullet/gym/pybullet_envs/agents/ppo/memory.py
index 10e79ef72..eee19ac07 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/ppo/memory.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/ppo/memory.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Memory that stores episodes."""
 
 from __future__ import absolute_import
@@ -43,10 +42,9 @@ class EpisodeMemory(object):
       self._scope = var_scope
       self._length = tf.Variable(tf.zeros(capacity, tf.int32), False)
       self._buffers = [
-          tf.Variable(tf.zeros(
-              [capacity, max_length] + elem.shape.as_list(),
-              elem.dtype), False)
-          for elem in template]
+          tf.Variable(tf.zeros([capacity, max_length] + elem.shape.as_list(), elem.dtype), False)
+          for elem in template
+      ]
 
   def length(self, rows=None):
     """Tensor holding the current length of episodes.
@@ -72,13 +70,11 @@ class EpisodeMemory(object):
     """
     rows = tf.range(self._capacity) if rows is None else rows
     assert rows.shape.ndims == 1
-    assert_capacity = tf.assert_less(
-        rows, self._capacity,
-        message='capacity exceeded')
+    assert_capacity = tf.assert_less(rows, self._capacity, message='capacity exceeded')
     with tf.control_dependencies([assert_capacity]):
-      assert_max_length = tf.assert_less(
-          tf.gather(self._length, rows), self._max_length,
-          message='max length exceeded')
+      assert_max_length = tf.assert_less(tf.gather(self._length, rows),
+                                         self._max_length,
+                                         message='max length exceeded')
     append_ops = []
     with tf.control_dependencies([assert_max_length]):
       for buffer_, elements in zip(self._buffers, transitions):
@@ -86,8 +82,7 @@ class EpisodeMemory(object):
         indices = tf.stack([rows, timestep], 1)
         append_ops.append(tf.scatter_nd_update(buffer_, indices, elements))
     with tf.control_dependencies(append_ops):
-      episode_mask = tf.reduce_sum(tf.one_hot(
-          rows, self._capacity, dtype=tf.int32), 0)
+      episode_mask = tf.reduce_sum(tf.one_hot(rows, self._capacity, dtype=tf.int32), 0)
       return self._length.assign_add(episode_mask)
 
   def replace(self, episodes, length, rows=None):
@@ -103,11 +98,11 @@ class EpisodeMemory(object):
     """
     rows = tf.range(self._capacity) if rows is None else rows
     assert rows.shape.ndims == 1
-    assert_capacity = tf.assert_less(
-        rows, self._capacity, message='capacity exceeded')
+    assert_capacity = tf.assert_less(rows, self._capacity, message='capacity exceeded')
     with tf.control_dependencies([assert_capacity]):
-      assert_max_length = tf.assert_less_equal(
-          length, self._max_length, message='max length exceeded')
+      assert_max_length = tf.assert_less_equal(length,
+                                               self._max_length,
+                                               message='max length exceeded')
     replace_ops = []
     with tf.control_dependencies([assert_max_length]):
       for buffer_, elements in zip(self._buffers, episodes):
diff --git a/examples/pybullet/gym/pybullet_envs/agents/ppo/normalize.py b/examples/pybullet/gym/pybullet_envs/agents/ppo/normalize.py
index 6c4170519..4de38fd25 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/ppo/normalize.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/ppo/normalize.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Normalize tensors based on streaming estimates of mean and variance."""
 
 from __future__ import absolute_import
@@ -24,8 +23,7 @@ import tensorflow as tf
 class StreamingNormalize(object):
   """Normalize tensors based on streaming estimates of mean and variance."""
 
-  def __init__(
-      self, template, center=True, scale=True, clip=10, name='normalize'):
+  def __init__(self, template, center=True, scale=True, clip=10, name='normalize'):
     """Normalize tensors based on streaming estimates of mean and variance.
 
     Centering the value, scaling it by the standard deviation, and clipping
@@ -69,8 +67,7 @@ class StreamingNormalize(object):
       if self._scale:
         # We cannot scale before seeing at least two samples.
         value /= tf.cond(
-            self._count > 1, lambda: self._std() + 1e-8,
-            lambda: tf.ones_like(self._var_sum))[None]
+            self._count > 1, lambda: self._std() + 1e-8, lambda: tf.ones_like(self._var_sum))[None]
       if self._clip:
         value = tf.clip_by_value(value, -self._clip, self._clip)
       # Remove batch dimension if necessary.
@@ -97,8 +94,7 @@ class StreamingNormalize(object):
         mean_delta = tf.reduce_sum(value - self._mean[None, ...], 0)
         new_mean = self._mean + mean_delta / step
         new_mean = tf.cond(self._count > 1, lambda: new_mean, lambda: value[0])
-        var_delta = (
-            value - self._mean[None, ...]) * (value - new_mean[None, ...])
+        var_delta = (value - self._mean[None, ...]) * (value - new_mean[None, ...])
         new_var_sum = self._var_sum + tf.reduce_sum(var_delta, 0)
       with tf.control_dependencies([new_mean, new_var_sum]):
         update = self._mean.assign(new_mean), self._var_sum.assign(new_var_sum)
@@ -116,10 +112,8 @@ class StreamingNormalize(object):
       Operation.
     """
     with tf.name_scope(self._name + '/reset'):
-      return tf.group(
-          self._count.assign(0),
-          self._mean.assign(tf.zeros_like(self._mean)),
-          self._var_sum.assign(tf.zeros_like(self._var_sum)))
+      return tf.group(self._count.assign(0), self._mean.assign(tf.zeros_like(self._mean)),
+                      self._var_sum.assign(tf.zeros_like(self._var_sum)))
 
   def summary(self):
     """Summary string of mean and standard deviation.
@@ -128,10 +122,8 @@ class StreamingNormalize(object):
       Summary tensor.
     """
     with tf.name_scope(self._name + '/summary'):
-      mean_summary = tf.cond(
-          self._count > 0, lambda: self._summary('mean', self._mean), str)
-      std_summary = tf.cond(
-          self._count > 1, lambda: self._summary('stddev', self._std()), str)
+      mean_summary = tf.cond(self._count > 0, lambda: self._summary('mean', self._mean), str)
+      std_summary = tf.cond(self._count > 1, lambda: self._summary('stddev', self._std()), str)
       return tf.summary.merge([mean_summary, std_summary])
 
   def _std(self):
@@ -143,10 +135,8 @@ class StreamingNormalize(object):
     Returns:
       Tensor of current variance.
     """
-    variance = tf.cond(
-        self._count > 1,
-        lambda: self._var_sum / tf.cast(self._count - 1, tf.float32),
-        lambda: tf.ones_like(self._var_sum) * float('nan'))
+    variance = tf.cond(self._count > 1, lambda: self._var_sum / tf.cast(
+        self._count - 1, tf.float32), lambda: tf.ones_like(self._var_sum) * float('nan'))
     # The epsilon corrects for small negative variance values caused by
     # the algorithm. It was empirically chosen to work with all environments
     # tested.
diff --git a/examples/pybullet/gym/pybullet_envs/agents/ppo/utility.py b/examples/pybullet/gym/pybullet_envs/agents/ppo/utility.py
index c46934c49..29d9bf9ea 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/ppo/utility.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/ppo/utility.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Utilities for the PPO algorithm."""
 
 from __future__ import absolute_import
@@ -37,8 +36,7 @@ def reinit_nested_vars(variables, indices=None):
     Operation.
   """
   if isinstance(variables, (tuple, list)):
-    return tf.group(*[
-        reinit_nested_vars(variable, indices) for variable in variables])
+    return tf.group(*[reinit_nested_vars(variable, indices) for variable in variables])
   if indices is None:
     return variables.assign(tf.zeros_like(variables))
   else:
@@ -58,9 +56,8 @@ def assign_nested_vars(variables, tensors, indices=None):
     Operation.
   """
   if isinstance(variables, (tuple, list)):
-    return tf.group(*[
-        assign_nested_vars(variable, tensor)
-        for variable, tensor in zip(variables, tensors)])
+    return tf.group(
+        *[assign_nested_vars(variable, tensor) for variable, tensor in zip(variables, tensors)])
   if indices is None:
     return variables.assign(tensors)
   else:
@@ -71,10 +68,11 @@ def discounted_return(reward, length, discount):
   """Discounted Monte-Carlo returns."""
   timestep = tf.range(reward.shape[1].value)
   mask = tf.cast(timestep[None, :] < length[:, None], tf.float32)
-  return_ = tf.reverse(tf.transpose(tf.scan(
-      lambda agg, cur: cur + discount * agg,
-      tf.transpose(tf.reverse(mask * reward, [1]), [1, 0]),
-      tf.zeros_like(reward[:, -1]), 1, False), [1, 0]), [1])
+  return_ = tf.reverse(
+      tf.transpose(
+          tf.scan(lambda agg, cur: cur + discount * agg,
+                  tf.transpose(tf.reverse(mask * reward, [1]), [1, 0]),
+                  tf.zeros_like(reward[:, -1]), 1, False), [1, 0]), [1])
   return tf.check_numerics(tf.stop_gradient(return_), 'return')
 
 
@@ -85,9 +83,8 @@ def fixed_step_return(reward, value, length, discount, window):
   return_ = tf.zeros_like(reward)
   for _ in range(window):
     return_ += reward
-    reward = discount * tf.concat(
-        [reward[:, 1:], tf.zeros_like(reward[:, -1:])], 1)
-  return_ += discount ** window * tf.concat(
+    reward = discount * tf.concat([reward[:, 1:], tf.zeros_like(reward[:, -1:])], 1)
+  return_ += discount**window * tf.concat(
       [value[:, window:], tf.zeros_like(value[:, -window:]), 1])
   return tf.check_numerics(tf.stop_gradient(mask * return_), 'return')
 
@@ -99,10 +96,11 @@ def lambda_return(reward, value, length, discount, lambda_):
   sequence = mask * reward + discount * value * (1 - lambda_)
   discount = mask * discount * lambda_
   sequence = tf.stack([sequence, discount], 2)
-  return_ = tf.reverse(tf.transpose(tf.scan(
-      lambda agg, cur: cur[0] + cur[1] * agg,
-      tf.transpose(tf.reverse(sequence, [1]), [1, 2, 0]),
-      tf.zeros_like(value[:, -1]), 1, False), [1, 0]), [1])
+  return_ = tf.reverse(
+      tf.transpose(
+          tf.scan(lambda agg, cur: cur[0] + cur[1] * agg,
+                  tf.transpose(tf.reverse(sequence, [1]), [1, 2, 0]), tf.zeros_like(value[:, -1]),
+                  1, False), [1, 0]), [1])
   return tf.check_numerics(tf.stop_gradient(return_), 'return')
 
 
@@ -112,27 +110,26 @@ def lambda_advantage(reward, value, length, discount):
   mask = tf.cast(timestep[None, :] < length[:, None], tf.float32)
   next_value = tf.concat([value[:, 1:], tf.zeros_like(value[:, -1:])], 1)
   delta = reward + discount * next_value - value
-  advantage = tf.reverse(tf.transpose(tf.scan(
-      lambda agg, cur: cur + discount * agg,
-      tf.transpose(tf.reverse(mask * delta, [1]), [1, 0]),
-      tf.zeros_like(delta[:, -1]), 1, False), [1, 0]), [1])
+  advantage = tf.reverse(
+      tf.transpose(
+          tf.scan(lambda agg, cur: cur + discount * agg,
+                  tf.transpose(tf.reverse(mask * delta, [1]), [1, 0]), tf.zeros_like(delta[:, -1]),
+                  1, False), [1, 0]), [1])
   return tf.check_numerics(tf.stop_gradient(advantage), 'advantage')
 
 
 def diag_normal_kl(mean0, logstd0, mean1, logstd1):
   """Epirical KL divergence of two normals with diagonal covariance."""
   logstd0_2, logstd1_2 = 2 * logstd0, 2 * logstd1
-  return 0.5 * (
-      tf.reduce_sum(tf.exp(logstd0_2 - logstd1_2), -1) +
-      tf.reduce_sum((mean1 - mean0) ** 2 / tf.exp(logstd1_2), -1) +
-      tf.reduce_sum(logstd1_2, -1) - tf.reduce_sum(logstd0_2, -1) -
-      mean0.shape[-1].value)
+  return 0.5 * (tf.reduce_sum(tf.exp(logstd0_2 - logstd1_2), -1) + tf.reduce_sum(
+      (mean1 - mean0)**2 / tf.exp(logstd1_2), -1) + tf.reduce_sum(logstd1_2, -1) -
+                tf.reduce_sum(logstd0_2, -1) - mean0.shape[-1].value)
 
 
 def diag_normal_logpdf(mean, logstd, loc):
   """Log density of a normal with diagonal covariance."""
   constant = -0.5 * math.log(2 * math.pi) - logstd
-  value = -0.5 * ((loc - mean) / tf.exp(logstd)) ** 2
+  value = -0.5 * ((loc - mean) / tf.exp(logstd))**2
   return tf.reduce_sum(constant + value, -1)
 
 
diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/__init__.py b/examples/pybullet/gym/pybullet_envs/agents/tools/__init__.py
index 0201de98f..3baf78ae0 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/tools/__init__.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/tools/__init__.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Tools for reinforcement learning."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/attr_dict.py b/examples/pybullet/gym/pybullet_envs/agents/tools/attr_dict.py
index 1707486d8..fbb08290f 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/tools/attr_dict.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/tools/attr_dict.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Wrap a dictionary to access keys as attributes."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/batch_env.py b/examples/pybullet/gym/pybullet_envs/agents/tools/batch_env.py
index 946fd7211..ef8b7430e 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/tools/batch_env.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/tools/batch_env.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Combine multiple environments to step them in batch."""
 
 from __future__ import absolute_import
@@ -83,13 +82,9 @@ class BatchEnv(object):
         message = 'Invalid action at index {}: {}'
         raise ValueError(message.format(index, action))
     if self._blocking:
-      transitions = [
-          env.step(action)
-          for env, action in zip(self._envs, actions)]
+      transitions = [env.step(action) for env, action in zip(self._envs, actions)]
     else:
-      transitions = [
-          env.step(action, blocking=False)
-          for env, action in zip(self._envs, actions)]
+      transitions = [env.step(action, blocking=False) for env, action in zip(self._envs, actions)]
       transitions = [transition() for transition in transitions]
     observs, rewards, dones, infos = zip(*transitions)
     observ = np.stack(observs)
diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/count_weights.py b/examples/pybullet/gym/pybullet_envs/agents/tools/count_weights.py
index dd0d870f6..279df82a0 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/tools/count_weights.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/tools/count_weights.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Count learnable parameters."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_batch_env.py b/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_batch_env.py
index d4e1644d3..a4709f0a5 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_batch_env.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_batch_env.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Batch of environments inside the TensorFlow graph."""
 
 from __future__ import absolute_import
@@ -42,18 +41,18 @@ class InGraphBatchEnv(object):
     action_shape = self._parse_shape(self._batch_env.action_space)
     action_dtype = self._parse_dtype(self._batch_env.action_space)
     with tf.variable_scope('env_temporary'):
-      self._observ = tf.Variable(
-          tf.zeros((len(self._batch_env),) + observ_shape, observ_dtype),
-          name='observ', trainable=False)
-      self._action = tf.Variable(
-          tf.zeros((len(self._batch_env),) + action_shape, action_dtype),
-          name='action', trainable=False)
-      self._reward = tf.Variable(
-          tf.zeros((len(self._batch_env),), tf.float32),
-          name='reward', trainable=False)
-      self._done = tf.Variable(
-          tf.cast(tf.ones((len(self._batch_env),)), tf.bool),
-          name='done', trainable=False)
+      self._observ = tf.Variable(tf.zeros((len(self._batch_env),) + observ_shape, observ_dtype),
+                                 name='observ',
+                                 trainable=False)
+      self._action = tf.Variable(tf.zeros((len(self._batch_env),) + action_shape, action_dtype),
+                                 name='action',
+                                 trainable=False)
+      self._reward = tf.Variable(tf.zeros((len(self._batch_env),), tf.float32),
+                                 name='reward',
+                                 trainable=False)
+      self._done = tf.Variable(tf.cast(tf.ones((len(self._batch_env),)), tf.bool),
+                               name='done',
+                               trainable=False)
 
   def __getattr__(self, name):
     """Forward unimplemented attributes to one of the original environments.
@@ -89,16 +88,13 @@ class InGraphBatchEnv(object):
       if action.dtype in (tf.float16, tf.float32, tf.float64):
         action = tf.check_numerics(action, 'action')
       observ_dtype = self._parse_dtype(self._batch_env.observation_space)
-      observ, reward, done = tf.py_func(
-          lambda a: self._batch_env.step(a)[:3], [action],
-          [observ_dtype, tf.float32, tf.bool], name='step')
+      observ, reward, done = tf.py_func(lambda a: self._batch_env.step(a)[:3], [action],
+                                        [observ_dtype, tf.float32, tf.bool],
+                                        name='step')
       observ = tf.check_numerics(observ, 'observ')
       reward = tf.check_numerics(reward, 'reward')
-      return tf.group(
-          self._observ.assign(observ),
-          self._action.assign(action),
-          self._reward.assign(reward),
-          self._done.assign(done))
+      return tf.group(self._observ.assign(observ), self._action.assign(action),
+                      self._reward.assign(reward), self._done.assign(done))
 
   def reset(self, indices=None):
     """Reset the batch of environments.
@@ -112,15 +108,15 @@ class InGraphBatchEnv(object):
     if indices is None:
       indices = tf.range(len(self._batch_env))
     observ_dtype = self._parse_dtype(self._batch_env.observation_space)
-    observ = tf.py_func(
-        self._batch_env.reset, [indices], observ_dtype, name='reset')
+    observ = tf.py_func(self._batch_env.reset, [indices], observ_dtype, name='reset')
     observ = tf.check_numerics(observ, 'observ')
     reward = tf.zeros_like(indices, tf.float32)
     done = tf.zeros_like(indices, tf.bool)
     with tf.control_dependencies([
         tf.scatter_update(self._observ, indices, observ),
         tf.scatter_update(self._reward, indices, reward),
-        tf.scatter_update(self._done, indices, done)]):
+        tf.scatter_update(self._done, indices, done)
+    ]):
       return tf.identity(observ)
 
   @property
diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_env.py b/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_env.py
index 33ff31d07..6a71f4516 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_env.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_env.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Put an OpenAI Gym environment into the TensorFlow graph."""
 
 from __future__ import absolute_import
@@ -42,16 +41,15 @@ class InGraphEnv(object):
     action_shape = self._parse_shape(self._env.action_space)
     action_dtype = self._parse_dtype(self._env.action_space)
     with tf.name_scope('environment'):
-      self._observ = tf.Variable(
-          tf.zeros(observ_shape, observ_dtype), name='observ', trainable=False)
-      self._action = tf.Variable(
-          tf.zeros(action_shape, action_dtype), name='action', trainable=False)
-      self._reward = tf.Variable(
-          0.0, dtype=tf.float32, name='reward', trainable=False)
-      self._done = tf.Variable(
-          True, dtype=tf.bool, name='done', trainable=False)
-      self._step = tf.Variable(
-          0, dtype=tf.int32, name='step', trainable=False)
+      self._observ = tf.Variable(tf.zeros(observ_shape, observ_dtype),
+                                 name='observ',
+                                 trainable=False)
+      self._action = tf.Variable(tf.zeros(action_shape, action_dtype),
+                                 name='action',
+                                 trainable=False)
+      self._reward = tf.Variable(0.0, dtype=tf.float32, name='reward', trainable=False)
+      self._done = tf.Variable(True, dtype=tf.bool, name='done', trainable=False)
+      self._step = tf.Variable(0, dtype=tf.int32, name='step', trainable=False)
 
   def __getattr__(self, name):
     """Forward unimplemented attributes to the original environment.
@@ -79,17 +77,14 @@ class InGraphEnv(object):
       if action.dtype in (tf.float16, tf.float32, tf.float64):
         action = tf.check_numerics(action, 'action')
       observ_dtype = self._parse_dtype(self._env.observation_space)
-      observ, reward, done = tf.py_func(
-          lambda a: self._env.step(a)[:3], [action],
-          [observ_dtype, tf.float32, tf.bool], name='step')
+      observ, reward, done = tf.py_func(lambda a: self._env.step(a)[:3], [action],
+                                        [observ_dtype, tf.float32, tf.bool],
+                                        name='step')
       observ = tf.check_numerics(observ, 'observ')
       reward = tf.check_numerics(reward, 'reward')
-      return tf.group(
-          self._observ.assign(observ),
-          self._action.assign(action),
-          self._reward.assign(reward),
-          self._done.assign(done),
-          self._step.assign_add(1))
+      return tf.group(self._observ.assign(observ), self._action.assign(action),
+                      self._reward.assign(reward), self._done.assign(done),
+                      self._step.assign_add(1))
 
   def reset(self):
     """Reset the environment.
@@ -100,10 +95,10 @@ class InGraphEnv(object):
     observ_dtype = self._parse_dtype(self._env.observation_space)
     observ = tf.py_func(self._env.reset, [], observ_dtype, name='reset')
     observ = tf.check_numerics(observ, 'observ')
-    with tf.control_dependencies([
-        self._observ.assign(observ),
-        self._reward.assign(0),
-        self._done.assign(False)]):
+    with tf.control_dependencies(
+        [self._observ.assign(observ),
+         self._reward.assign(0),
+         self._done.assign(False)]):
       return tf.identity(observ)
 
   @property
diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/loop.py b/examples/pybullet/gym/pybullet_envs/agents/tools/loop.py
index b8f118c85..b70798c18 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/tools/loop.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/tools/loop.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Execute operations in a loop and coordinate logging and checkpoints."""
 
 from __future__ import absolute_import
@@ -25,10 +24,8 @@ import tensorflow as tf
 
 from . import streaming_mean
 
-
 _Phase = collections.namedtuple(
-    'Phase',
-    'name, writer, op, batch, steps, feed, report_every, log_every,'
+    'Phase', 'name, writer, op, batch, steps, feed, report_every, log_every,'
     'checkpoint_every')
 
 
@@ -56,16 +53,22 @@ class Loop(object):
       reset: Tensor indicating to the model to start a new computation.
     """
     self._logdir = logdir
-    self._step = (
-        tf.Variable(0, False, name='global_step') if step is None else step)
+    self._step = (tf.Variable(0, False, name='global_step') if step is None else step)
     self._log = tf.placeholder(tf.bool) if log is None else log
     self._report = tf.placeholder(tf.bool) if report is None else report
     self._reset = tf.placeholder(tf.bool) if reset is None else reset
     self._phases = []
 
-  def add_phase(
-      self, name, done, score, summary, steps,
-      report_every=None, log_every=None, checkpoint_every=None, feed=None):
+  def add_phase(self,
+                name,
+                done,
+                score,
+                summary,
+                steps,
+                report_every=None,
+                log_every=None,
+                checkpoint_every=None,
+                feed=None):
     """Add a phase to the loop protocol.
 
     If the model breaks long computation into multiple steps, the done tensor
@@ -97,13 +100,12 @@ class Loop(object):
     if done.shape.ndims is None or score.shape.ndims is None:
       raise ValueError("Rank of 'done' and 'score' tensors must be known.")
     writer = self._logdir and tf.summary.FileWriter(
-        os.path.join(self._logdir, name), tf.get_default_graph(),
-        flush_secs=60)
+        os.path.join(self._logdir, name), tf.get_default_graph(), flush_secs=60)
     op = self._define_step(done, score, summary)
     batch = 1 if score.shape.ndims == 0 else score.shape[0].value
-    self._phases.append(_Phase(
-        name, writer, op, batch, int(steps), feed, report_every,
-        log_every, checkpoint_every))
+    self._phases.append(
+        _Phase(name, writer, op, batch, int(steps), feed, report_every, log_every,
+               checkpoint_every))
 
   def run(self, sess, saver, max_step=None):
     """Run the loop schedule for a specified number of steps.
@@ -133,13 +135,11 @@ class Loop(object):
         tf.logging.info(message.format(phase.name, phase_step, global_step))
       # Populate book keeping tensors.
       phase.feed[self._reset] = (steps_in < steps_made)
-      phase.feed[self._log] = (
-          phase.writer and
-          self._is_every_steps(phase_step, phase.batch, phase.log_every))
-      phase.feed[self._report] = (
-          self._is_every_steps(phase_step, phase.batch, phase.report_every))
-      summary, mean_score, global_step, steps_made = sess.run(
-          phase.op, phase.feed)
+      phase.feed[self._log] = (phase.writer and
+                               self._is_every_steps(phase_step, phase.batch, phase.log_every))
+      phase.feed[self._report] = (self._is_every_steps(phase_step, phase.batch,
+                                                       phase.report_every))
+      summary, mean_score, global_step, steps_made = sess.run(phase.op, phase.feed)
       if self._is_every_steps(phase_step, phase.batch, phase.checkpoint_every):
         self._store_checkpoint(sess, saver, global_step)
       if self._is_every_steps(phase_step, phase.batch, phase.report_every):
@@ -207,8 +207,7 @@ class Loop(object):
     score_mean = streaming_mean.StreamingMean((), tf.float32)
     with tf.control_dependencies([done, score, summary]):
       done_score = tf.gather(score, tf.where(done)[:, 0])
-      submit_score = tf.cond(
-          tf.reduce_any(done), lambda: score_mean.submit(done_score), tf.no_op)
+      submit_score = tf.cond(tf.reduce_any(done), lambda: score_mean.submit(done_score), tf.no_op)
     with tf.control_dependencies([submit_score]):
       mean_score = tf.cond(self._report, score_mean.clear, float)
       steps_made = tf.shape(score)[0]
diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/mock_algorithm.py b/examples/pybullet/gym/pybullet_envs/agents/tools/mock_algorithm.py
index c60712d8b..c9c55ac60 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/tools/mock_algorithm.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/tools/mock_algorithm.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Mock algorithm for testing reinforcement learning code."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/mock_environment.py b/examples/pybullet/gym/pybullet_envs/agents/tools/mock_environment.py
index 248f515b1..9db14d56c 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/tools/mock_environment.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/tools/mock_environment.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Mock environment for testing reinforcement learning code."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/simulate.py b/examples/pybullet/gym/pybullet_envs/agents/tools/simulate.py
index ebe9a898b..128b83f00 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/tools/simulate.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/tools/simulate.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """In-graph simulation step of a vectorized algorithm with environments."""
 
 from __future__ import absolute_import
@@ -55,7 +54,8 @@ def simulate(batch_env, algo, log=True, reset=False):
     reset_ops = [
         batch_env.reset(agent_indices),
         tf.scatter_update(score, agent_indices, zero_scores),
-        tf.scatter_update(length, agent_indices, zero_durations)]
+        tf.scatter_update(length, agent_indices, zero_durations)
+    ]
     with tf.control_dependencies(reset_ops):
       return algo.begin_episode(agent_indices)
 
@@ -78,9 +78,8 @@ def simulate(batch_env, algo, log=True, reset=False):
       inc_length = length.assign_add(tf.ones(len(batch_env), tf.int32))
     with tf.control_dependencies([add_score, inc_length]):
       agent_indices = tf.range(len(batch_env))
-      experience_summary = algo.experience(
-          agent_indices, prevob, batch_env.action, batch_env.reward,
-          batch_env.done, batch_env.observ)
+      experience_summary = algo.experience(agent_indices, prevob, batch_env.action,
+                                           batch_env.reward, batch_env.done, batch_env.observ)
     return tf.summary.merge([step_summary, experience_summary])
 
   def _define_end_episode(agent_indices):
@@ -96,8 +95,7 @@ def simulate(batch_env, algo, log=True, reset=False):
     """
     assert agent_indices.shape.ndims == 1
     submit_score = mean_score.submit(tf.gather(score, agent_indices))
-    submit_length = mean_length.submit(
-        tf.cast(tf.gather(length, agent_indices), tf.float32))
+    submit_length = mean_length.submit(tf.cast(tf.gather(length, agent_indices), tf.float32))
     with tf.control_dependencies([submit_score, submit_length]):
       return algo.end_episode(agent_indices)
 
@@ -107,41 +105,34 @@ def simulate(batch_env, algo, log=True, reset=False):
     Returns:
       Summary string.
     """
-    score_summary = tf.cond(
-        tf.logical_and(log, tf.cast(mean_score.count, tf.bool)),
-        lambda: tf.summary.scalar('mean_score', mean_score.clear()), str)
-    length_summary = tf.cond(
-        tf.logical_and(log, tf.cast(mean_length.count, tf.bool)),
-        lambda: tf.summary.scalar('mean_length', mean_length.clear()), str)
+    score_summary = tf.cond(tf.logical_and(log, tf.cast(
+        mean_score.count, tf.bool)), lambda: tf.summary.scalar('mean_score', mean_score.clear()),
+                            str)
+    length_summary = tf.cond(tf.logical_and(
+        log, tf.cast(mean_length.count,
+                     tf.bool)), lambda: tf.summary.scalar('mean_length', mean_length.clear()), str)
     return tf.summary.merge([score_summary, length_summary])
 
   with tf.name_scope('simulate'):
     log = tf.convert_to_tensor(log)
     reset = tf.convert_to_tensor(reset)
     with tf.variable_scope('simulate_temporary'):
-      score = tf.Variable(
-          tf.zeros(len(batch_env), dtype=tf.float32), False, name='score')
-      length = tf.Variable(
-          tf.zeros(len(batch_env), dtype=tf.int32), False, name='length')
+      score = tf.Variable(tf.zeros(len(batch_env), dtype=tf.float32), False, name='score')
+      length = tf.Variable(tf.zeros(len(batch_env), dtype=tf.int32), False, name='length')
     mean_score = streaming_mean.StreamingMean((), tf.float32)
     mean_length = streaming_mean.StreamingMean((), tf.float32)
-    agent_indices = tf.cond(
-        reset,
-        lambda: tf.range(len(batch_env)),
-        lambda: tf.cast(tf.where(batch_env.done)[:, 0], tf.int32))
-    begin_episode = tf.cond(
-        tf.cast(tf.shape(agent_indices)[0], tf.bool),
-        lambda: _define_begin_episode(agent_indices), str)
+    agent_indices = tf.cond(reset, lambda: tf.range(len(batch_env)), lambda: tf.cast(
+        tf.where(batch_env.done)[:, 0], tf.int32))
+    begin_episode = tf.cond(tf.cast(tf.shape(agent_indices)[0],
+                                    tf.bool), lambda: _define_begin_episode(agent_indices), str)
     with tf.control_dependencies([begin_episode]):
       step = _define_step()
     with tf.control_dependencies([step]):
       agent_indices = tf.cast(tf.where(batch_env.done)[:, 0], tf.int32)
-      end_episode = tf.cond(
-          tf.cast(tf.shape(agent_indices)[0], tf.bool),
-          lambda: _define_end_episode(agent_indices), str)
+      end_episode = tf.cond(tf.cast(tf.shape(agent_indices)[0],
+                                    tf.bool), lambda: _define_end_episode(agent_indices), str)
     with tf.control_dependencies([end_episode]):
-      summary = tf.summary.merge([
-          _define_summaries(), begin_episode, step, end_episode])
+      summary = tf.summary.merge([_define_summaries(), begin_episode, step, end_episode])
     with tf.control_dependencies([summary]):
       done, score = tf.identity(batch_env.done), tf.identity(score)
     return done, score, summary
diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/streaming_mean.py b/examples/pybullet/gym/pybullet_envs/agents/tools/streaming_mean.py
index 3f620fe37..5992eaa6b 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/tools/streaming_mean.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/tools/streaming_mean.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Compute a streaming estimation of the mean of submitted tensors."""
 
 from __future__ import absolute_import
@@ -53,9 +52,8 @@ class StreamingMean(object):
     # Add a batch dimension if necessary.
     if value.shape.ndims == self._sum.shape.ndims:
       value = value[None, ...]
-    return tf.group(
-        self._sum.assign_add(tf.reduce_sum(value, 0)),
-        self._count.assign_add(tf.shape(value)[0]))
+    return tf.group(self._sum.assign_add(tf.reduce_sum(value, 0)),
+                    self._count.assign_add(tf.shape(value)[0]))
 
   def clear(self):
     """Return the mean estimate and reset the streaming statistics."""
diff --git a/examples/pybullet/gym/pybullet_envs/agents/tools/wrappers.py b/examples/pybullet/gym/pybullet_envs/agents/tools/wrappers.py
index e7c7543e3..0c3ff47b6 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/tools/wrappers.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/tools/wrappers.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Wrappers for OpenAI Gym environments."""
 
 from __future__ import absolute_import
@@ -149,8 +148,7 @@ class FrameHistory(object):
     return self._select_frames()
 
   def _select_frames(self):
-    indices = [
-        (self._step - index) % self._capacity for index in self._past_indices]
+    indices = [(self._step - index) % self._capacity for index in self._past_indices]
     observ = self._buffer[indices]
     if self._flatten:
       observ = np.reshape(observ, (-1,) + observ.shape[2:])
@@ -191,14 +189,14 @@ class RangeNormalize(object):
 
   def __init__(self, env, observ=None, action=None):
     self._env = env
-    self._should_normalize_observ = (
-        observ is not False and self._is_finite(self._env.observation_space))
+    self._should_normalize_observ = (observ is not False and
+                                     self._is_finite(self._env.observation_space))
     if observ is True and not self._should_normalize_observ:
       raise ValueError('Cannot normalize infinite observation range.')
     if observ is None and not self._should_normalize_observ:
       tf.logging.info('Not normalizing infinite observation range.')
-    self._should_normalize_action = (
-        action is not False and self._is_finite(self._env.action_space))
+    self._should_normalize_action = (action is not False and
+                                     self._is_finite(self._env.action_space))
     if action is True and not self._should_normalize_action:
       raise ValueError('Cannot normalize infinite action range.')
     if action is None and not self._should_normalize_action:
@@ -323,8 +321,7 @@ class ExternalProcess(object):
       action_space: The cached action space of the environment.
     """
     self._conn, conn = multiprocessing.Pipe()
-    self._process = multiprocessing.Process(
-        target=self._worker, args=(constructor, conn))
+    self._process = multiprocessing.Process(target=self._worker, args=(constructor, conn))
     atexit.register(self.close)
     self._process.start()
     self._observ_space = None
diff --git a/examples/pybullet/gym/pybullet_envs/agents/train_ppo.py b/examples/pybullet/gym/pybullet_envs/agents/train_ppo.py
index e2f3b4114..aa41634a2 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/train_ppo.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/train_ppo.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 r"""Script to train a batch reinforcement learning algorithm.
 
 Command line:
@@ -67,21 +66,25 @@ def _define_loop(graph, logdir, train_steps, eval_steps):
   Returns:
     Loop object.
   """
-  loop = tools.Loop(
-      logdir, graph.step, graph.should_log, graph.do_report,
-      graph.force_reset)
-  loop.add_phase(
-      'train', graph.done, graph.score, graph.summary, train_steps,
-      report_every=train_steps,
-      log_every=train_steps // 2,
-      checkpoint_every=None,
-      feed={graph.is_training: True})
-  loop.add_phase(
-      'eval', graph.done, graph.score, graph.summary, eval_steps,
-      report_every=eval_steps,
-      log_every=eval_steps // 2,
-      checkpoint_every=10 * eval_steps,
-      feed={graph.is_training: False})
+  loop = tools.Loop(logdir, graph.step, graph.should_log, graph.do_report, graph.force_reset)
+  loop.add_phase('train',
+                 graph.done,
+                 graph.score,
+                 graph.summary,
+                 train_steps,
+                 report_every=train_steps,
+                 log_every=train_steps // 2,
+                 checkpoint_every=None,
+                 feed={graph.is_training: True})
+  loop.add_phase('eval',
+                 graph.done,
+                 graph.score,
+                 graph.summary,
+                 eval_steps,
+                 report_every=eval_steps,
+                 log_every=eval_steps // 2,
+                 checkpoint_every=10 * eval_steps,
+                 feed={graph.is_training: False})
   return loop
 
 
@@ -102,18 +105,13 @@ def train(config, env_processes):
   if config.update_every % config.num_agents:
     tf.logging.warn('Number of agents should divide episodes per update.')
   with tf.device('/cpu:0'):
-    batch_env = utility.define_batch_env(
-        lambda: _create_environment(config),
-        config.num_agents, env_processes)
-    graph = utility.define_simulation_graph(
-        batch_env, config.algorithm, config)
-    loop = _define_loop(
-        graph, config.logdir,
-        config.update_every * config.max_length,
-        config.eval_episodes * config.max_length)
-    total_steps = int(
-        config.steps / config.update_every *
-        (config.update_every + config.eval_episodes))
+    batch_env = utility.define_batch_env(lambda: _create_environment(config), config.num_agents,
+                                         env_processes)
+    graph = utility.define_simulation_graph(batch_env, config.algorithm, config)
+    loop = _define_loop(graph, config.logdir, config.update_every * config.max_length,
+                        config.eval_episodes * config.max_length)
+    total_steps = int(config.steps / config.update_every *
+                      (config.update_every + config.eval_episodes))
   # Exclude episode related variables since the Python state of environments is
   # not checkpointed and thus new episodes start after resuming.
   saver = utility.define_saver(exclude=(r'.*_temporary/.*',))
@@ -131,8 +129,8 @@ def main(_):
   utility.set_up_logging()
   if not FLAGS.config:
     raise KeyError('You must specify a configuration.')
-  logdir = FLAGS.logdir and os.path.expanduser(os.path.join(
-      FLAGS.logdir, '{}-{}'.format(FLAGS.timestamp, FLAGS.config)))
+  logdir = FLAGS.logdir and os.path.expanduser(
+      os.path.join(FLAGS.logdir, '{}-{}'.format(FLAGS.timestamp, FLAGS.config)))
   try:
     config = utility.load_config(logdir)
   except IOError:
@@ -144,16 +142,11 @@ def main(_):
 
 if __name__ == '__main__':
   FLAGS = tf.app.flags.FLAGS
-  tf.app.flags.DEFINE_string(
-      'logdir', None,
-      'Base directory to store logs.')
-  tf.app.flags.DEFINE_string(
-      'timestamp', datetime.datetime.now().strftime('%Y%m%dT%H%M%S'),
-      'Sub directory to store logs.')
-  tf.app.flags.DEFINE_string(
-      'config', None,
-      'Configuration to execute.')
-  tf.app.flags.DEFINE_boolean(
-      'env_processes', True,
-      'Step environments in separate processes to circumvent the GIL.')
+  tf.app.flags.DEFINE_string('logdir', None, 'Base directory to store logs.')
+  tf.app.flags.DEFINE_string('timestamp',
+                             datetime.datetime.now().strftime('%Y%m%dT%H%M%S'),
+                             'Sub directory to store logs.')
+  tf.app.flags.DEFINE_string('config', None, 'Configuration to execute.')
+  tf.app.flags.DEFINE_boolean('env_processes', True,
+                              'Step environments in separate processes to circumvent the GIL.')
   tf.app.run()
diff --git a/examples/pybullet/gym/pybullet_envs/agents/utility.py b/examples/pybullet/gym/pybullet_envs/agents/utility.py
index 0836a2ed2..1ccebbd10 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/utility.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/utility.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Utilities for using reinforcement learning algorithms."""
 
 from __future__ import absolute_import
@@ -46,8 +45,7 @@ def define_simulation_graph(batch_env, algo_cls, config):
   do_report = tf.placeholder(tf.bool, name='do_report')
   force_reset = tf.placeholder(tf.bool, name='force_reset')
   algo = algo_cls(batch_env, step, is_training, should_log, config)
-  done, score, summary = tools.simulate(
-      batch_env, algo, should_log, force_reset)
+  done, score, summary = tools.simulate(batch_env, algo, should_log, force_reset)
   message = 'Graph contains {} trainable variables.'
   tf.logging.info(message.format(tools.count_weights()))
   # pylint: enable=unused-variable
@@ -67,9 +65,7 @@ def define_batch_env(constructor, num_agents, env_processes):
   """
   with tf.variable_scope('environments'):
     if env_processes:
-      envs = [
-          tools.wrappers.ExternalProcess(constructor)
-          for _ in range(num_agents)]
+      envs = [tools.wrappers.ExternalProcess(constructor) for _ in range(num_agents)]
     else:
       envs = [constructor() for _ in range(num_agents)]
     batch_env = tools.BatchEnv(envs, blocking=not env_processes)
@@ -111,9 +107,7 @@ def initialize_variables(sess, saver, logdir, checkpoint=None, resume=None):
     ValueError: If resume expected but no log directory specified.
     RuntimeError: If no resume expected but a checkpoint was found.
   """
-  sess.run(tf.group(
-      tf.local_variables_initializer(),
-      tf.global_variables_initializer()))
+  sess.run(tf.group(tf.local_variables_initializer(), tf.global_variables_initializer()))
   if resume and not (logdir or checkpoint):
     raise ValueError('Need to specify logdir to resume a checkpoint.')
   if logdir:
@@ -152,9 +146,8 @@ def save_config(config, logdir=None):
     with tf.gfile.GFile(config_path, 'w') as file_:
       yaml.dump(config, file_, default_flow_style=False)
   else:
-    message = (
-        'Start a new run without storing summaries and checkpoints since no '
-        'logging directory was specified.')
+    message = ('Start a new run without storing summaries and checkpoints since no '
+               'logging directory was specified.')
     tf.logging.info(message)
   return config
 
@@ -173,9 +166,8 @@ def load_config(logdir):
   """
   config_path = logdir and os.path.join(logdir, 'config.yaml')
   if not config_path or not tf.gfile.Exists(config_path):
-    message = (
-        'Cannot resume an existing run since the logging directory does not '
-        'contain a configuration file.')
+    message = ('Cannot resume an existing run since the logging directory does not '
+               'contain a configuration file.')
     raise IOError(message)
   with tf.gfile.FastGFile(config_path, 'r') as file_:
     config = yaml.load(file_, Loader=yaml.Loader)
diff --git a/examples/pybullet/gym/pybullet_envs/agents/visualize_ppo.py b/examples/pybullet/gym/pybullet_envs/agents/visualize_ppo.py
index dd27a546d..8524b6311 100644
--- a/examples/pybullet/gym/pybullet_envs/agents/visualize_ppo.py
+++ b/examples/pybullet/gym/pybullet_envs/agents/visualize_ppo.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 r"""Script to render videos of the Proximal Policy Gradient algorithm.
 
 Command line:
@@ -53,8 +52,7 @@ def _create_environment(config, outdir):
     setattr(env, 'spec', getattr(env, 'spec', None))
   if config.max_length:
     env = tools.wrappers.LimitDuration(env, config.max_length)
-  env = gym.wrappers.Monitor(
-      env, outdir, lambda unused_episode_number: True)
+  env = gym.wrappers.Monitor(env, outdir, lambda unused_episode_number: True)
   env = tools.wrappers.RangeNormalize(env)
   env = tools.wrappers.ClipAction(env)
   env = tools.wrappers.ConvertTo32Bit(env)
@@ -71,20 +69,20 @@ def _define_loop(graph, eval_steps):
   Returns:
     Loop object.
   """
-  loop = tools.Loop(
-      None, graph.step, graph.should_log, graph.do_report, graph.force_reset)
-  loop.add_phase(
-      'eval', graph.done, graph.score, graph.summary, eval_steps,
-      report_every=eval_steps,
-      log_every=None,
-      checkpoint_every=None,
-      feed={graph.is_training: False})
+  loop = tools.Loop(None, graph.step, graph.should_log, graph.do_report, graph.force_reset)
+  loop.add_phase('eval',
+                 graph.done,
+                 graph.score,
+                 graph.summary,
+                 eval_steps,
+                 report_every=eval_steps,
+                 log_every=None,
+                 checkpoint_every=None,
+                 feed={graph.is_training: False})
   return loop
 
 
-def visualize(
-    logdir, outdir, num_agents, num_episodes, checkpoint=None,
-    env_processes=True):
+def visualize(logdir, outdir, num_agents, num_episodes, checkpoint=None, env_processes=True):
   """Recover checkpoint and render videos from it.
 
   Args:
@@ -97,20 +95,16 @@ def visualize(
   """
   config = utility.load_config(logdir)
   with tf.device('/cpu:0'):
-    batch_env = utility.define_batch_env(
-        lambda: _create_environment(config, outdir),
-        num_agents, env_processes)
-    graph = utility.define_simulation_graph(
-        batch_env, config.algorithm, config)
+    batch_env = utility.define_batch_env(lambda: _create_environment(config, outdir), num_agents,
+                                         env_processes)
+    graph = utility.define_simulation_graph(batch_env, config.algorithm, config)
     total_steps = num_episodes * config.max_length
     loop = _define_loop(graph, total_steps)
-  saver = utility.define_saver(
-      exclude=(r'.*_temporary/.*', r'global_step'))
+  saver = utility.define_saver(exclude=(r'.*_temporary/.*', r'global_step'))
   sess_config = tf.ConfigProto(allow_soft_placement=True)
   sess_config.gpu_options.allow_growth = True
   with tf.Session(config=sess_config) as sess:
-    utility.initialize_variables(
-        sess, saver, config.logdir, checkpoint, resume=True)
+    utility.initialize_variables(sess, saver, config.logdir, checkpoint, resume=True)
     for unused_score in loop.run(sess, saver, total_steps):
       pass
   batch_env.close()
@@ -123,29 +117,18 @@ def main(_):
     raise KeyError('You must specify logging and outdirs directories.')
   FLAGS.logdir = os.path.expanduser(FLAGS.logdir)
   FLAGS.outdir = os.path.expanduser(FLAGS.outdir)
-  visualize(
-      FLAGS.logdir, FLAGS.outdir, FLAGS.num_agents, FLAGS.num_episodes,
-      FLAGS.checkpoint, FLAGS.env_processes)
+  visualize(FLAGS.logdir, FLAGS.outdir, FLAGS.num_agents, FLAGS.num_episodes, FLAGS.checkpoint,
+            FLAGS.env_processes)
 
 
 if __name__ == '__main__':
   FLAGS = tf.app.flags.FLAGS
-  tf.app.flags.DEFINE_string(
-      'logdir', None,
-      'Directory to the checkpoint of a training run.')
-  tf.app.flags.DEFINE_string(
-      'outdir', None,
-      'Local directory for storing the monitoring outdir.')
-  tf.app.flags.DEFINE_string(
-      'checkpoint', None,
-      'Checkpoint name to load; defaults to most recent.')
-  tf.app.flags.DEFINE_integer(
-      'num_agents', 1,
-      'How many environments to step in parallel.')
-  tf.app.flags.DEFINE_integer(
-      'num_episodes', 5,
-      'Minimum number of episodes to render.')
-  tf.app.flags.DEFINE_boolean(
-      'env_processes', True,
-      'Step environments in separate processes to circumvent the GIL.')
+  tf.app.flags.DEFINE_string('logdir', None, 'Directory to the checkpoint of a training run.')
+  tf.app.flags.DEFINE_string('outdir', None, 'Local directory for storing the monitoring outdir.')
+  tf.app.flags.DEFINE_string('checkpoint', None,
+                             'Checkpoint name to load; defaults to most recent.')
+  tf.app.flags.DEFINE_integer('num_agents', 1, 'How many environments to step in parallel.')
+  tf.app.flags.DEFINE_integer('num_episodes', 5, 'Minimum number of episodes to render.')
+  tf.app.flags.DEFINE_boolean('env_processes', True,
+                              'Step environments in separate processes to circumvent the GIL.')
   tf.app.run()
diff --git a/examples/pybullet/gym/pybullet_envs/baselines/__init__.py b/examples/pybullet/gym/pybullet_envs/baselines/__init__.py
index b67603479..e7d027b19 100644
--- a/examples/pybullet/gym/pybullet_envs/baselines/__init__.py
+++ b/examples/pybullet/gym/pybullet_envs/baselines/__init__.py
@@ -7,4 +7,3 @@ from . import train_kuka_grasping
 from . import train_pybullet_cartpole
 from . import train_pybullet_racecar
 from . import train_pybullet_zed_racecar
-
diff --git a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_diverse_object_grasping.py b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_diverse_object_grasping.py
index da40d6e94..509e33d1f 100644
--- a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_diverse_object_grasping.py
+++ b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_diverse_object_grasping.py
@@ -6,17 +6,17 @@ import numpy as np
 
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 from pybullet_envs.bullet.kuka_diverse_object_gym_env import KukaDiverseObjectEnv
 from gym import spaces
 
 
-
 class ContinuousDownwardBiasPolicy(object):
   """Policy which takes continuous actions, and is biased to move down.
   """
+
   def __init__(self, height_hack_prob=0.9):
     """Initializes the DownwardBiasPolicy.
 
@@ -36,25 +36,25 @@ class ContinuousDownwardBiasPolicy(object):
 
 
 def main():
-    
-    env = KukaDiverseObjectEnv(renders=True, isDiscrete=False)
-    policy = ContinuousDownwardBiasPolicy()
-
-    while True:
-        obs, done = env.reset(), False
-        print("===================================")        
-        print("obs")
-        print(obs)
-        episode_rew = 0
-        while not done:
-            env.render(mode='human')
-            act = policy.sample_action(obs, .1)
-            print("Action")
-            print(act)
-            obs, rew, done, _ = env.step([0, 0, 0, 0, 0])
-            episode_rew += rew
-        print("Episode reward", episode_rew)
+
+  env = KukaDiverseObjectEnv(renders=True, isDiscrete=False)
+  policy = ContinuousDownwardBiasPolicy()
+
+  while True:
+    obs, done = env.reset(), False
+    print("===================================")
+    print("obs")
+    print(obs)
+    episode_rew = 0
+    while not done:
+      env.render(mode='human')
+      act = policy.sample_action(obs, .1)
+      print("Action")
+      print(act)
+      obs, rew, done, _ = env.step([0, 0, 0, 0, 0])
+      episode_rew += rew
+    print("Episode reward", episode_rew)
 
 
 if __name__ == '__main__':
-    main()
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_grasping.py b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_grasping.py
index 1a5b7c2a8..422a7a621 100644
--- a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_grasping.py
+++ b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_kuka_grasping.py
@@ -2,7 +2,7 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 from pybullet_envs.bullet.kukaGymEnv import KukaGymEnv
@@ -11,22 +11,22 @@ from baselines import deepq
 
 
 def main():
-    
-    env = KukaGymEnv(renders=True, isDiscrete=True)
-    act = deepq.load("kuka_model.pkl")
-    print(act)
-    while True:
-        obs, done = env.reset(), False
-        print("===================================")        
-        print("obs")
-        print(obs)
-        episode_rew = 0
-        while not done:
-            env.render()
-            obs, rew, done, _ = env.step(act(obs[None])[0])
-            episode_rew += rew
-        print("Episode reward", episode_rew)
+
+  env = KukaGymEnv(renders=True, isDiscrete=True)
+  act = deepq.load("kuka_model.pkl")
+  print(act)
+  while True:
+    obs, done = env.reset(), False
+    print("===================================")
+    print("obs")
+    print(obs)
+    episode_rew = 0
+    while not done:
+      env.render()
+      obs, rew, done, _ = env.step(act(obs[None])[0])
+      episode_rew += rew
+    print("Episode reward", episode_rew)
 
 
 if __name__ == '__main__':
-    main()
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_cartpole.py b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_cartpole.py
index 0de7dc074..341029dd4 100644
--- a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_cartpole.py
+++ b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_cartpole.py
@@ -2,7 +2,7 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 import time
@@ -10,28 +10,29 @@ import time
 from baselines import deepq
 from pybullet_envs.bullet.cartpole_bullet import CartPoleBulletEnv
 
+
 def main():
-    env = gym.make('CartPoleBulletEnv-v1')
-    act = deepq.load("cartpole_model.pkl")
-
-    while True:
-        obs, done = env.reset(), False
-        print("obs")
-        print(obs)
-        print("type(obs)")
-        print(type(obs))
-        episode_rew = 0
-        while not done:
-            env.render()
-           
-            o = obs[None]
-            aa = act(o)
-            a = aa[0]
-            obs, rew, done, _ = env.step(a)
-            episode_rew += rew
-            time.sleep(1./240.)
-        print("Episode reward", episode_rew)
+  env = gym.make('CartPoleBulletEnv-v1')
+  act = deepq.load("cartpole_model.pkl")
+
+  while True:
+    obs, done = env.reset(), False
+    print("obs")
+    print(obs)
+    print("type(obs)")
+    print(type(obs))
+    episode_rew = 0
+    while not done:
+      env.render()
+
+      o = obs[None]
+      aa = act(o)
+      a = aa[0]
+      obs, rew, done, _ = env.step(a)
+      episode_rew += rew
+      time.sleep(1. / 240.)
+    print("Episode reward", episode_rew)
 
 
 if __name__ == '__main__':
-    main()
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_racecar.py b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_racecar.py
index ce3e74358..64936dd9a 100644
--- a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_racecar.py
+++ b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_racecar.py
@@ -2,7 +2,7 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 from pybullet_envs.bullet.racecarGymEnv import RacecarGymEnv
@@ -11,22 +11,22 @@ from baselines import deepq
 
 
 def main():
-    
-    env = RacecarGymEnv(renders=True,isDiscrete=True)
-    act = deepq.load("racecar_model.pkl")
-    print(act)
-    while True:
-        obs, done = env.reset(), False
-        print("===================================")        
-        print("obs")
-        print(obs)
-        episode_rew = 0
-        while not done:
-            env.render()
-            obs, rew, done, _ = env.step(act(obs[None])[0])
-            episode_rew += rew
-        print("Episode reward", episode_rew)
+
+  env = RacecarGymEnv(renders=True, isDiscrete=True)
+  act = deepq.load("racecar_model.pkl")
+  print(act)
+  while True:
+    obs, done = env.reset(), False
+    print("===================================")
+    print("obs")
+    print(obs)
+    episode_rew = 0
+    while not done:
+      env.render()
+      obs, rew, done, _ = env.step(act(obs[None])[0])
+      episode_rew += rew
+    print("Episode reward", episode_rew)
 
 
 if __name__ == '__main__':
-    main()
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_zed_racecar.py b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_zed_racecar.py
index acf6cf0f3..a8bcaa987 100644
--- a/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_zed_racecar.py
+++ b/examples/pybullet/gym/pybullet_envs/baselines/enjoy_pybullet_zed_racecar.py
@@ -2,7 +2,7 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 from pybullet_envs.bullet.racecarZEDGymEnv import RacecarZEDGymEnv
@@ -11,22 +11,22 @@ from baselines import deepq
 
 
 def main():
-    
-    env = RacecarZEDGymEnv(renders=True)
-    act = deepq.load("racecar_zed_model.pkl")
-    print(act)
-    while True:
-        obs, done = env.reset(), False
-        print("===================================")        
-        print("obs")
-        print(obs)
-        episode_rew = 0
-        while not done:
-            env.render()
-            obs, rew, done, _ = env.step(act(obs[None])[0])
-            episode_rew += rew
-        print("Episode reward", episode_rew)
+
+  env = RacecarZEDGymEnv(renders=True)
+  act = deepq.load("racecar_zed_model.pkl")
+  print(act)
+  while True:
+    obs, done = env.reset(), False
+    print("===================================")
+    print("obs")
+    print(obs)
+    episode_rew = 0
+    while not done:
+      env.render()
+      obs, rew, done, _ = env.step(act(obs[None])[0])
+      episode_rew += rew
+    print("Episode reward", episode_rew)
 
 
 if __name__ == '__main__':
-    main()
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_cam_grasping.py b/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_cam_grasping.py
index 4fd9f5a48..804be59f6 100644
--- a/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_cam_grasping.py
+++ b/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_cam_grasping.py
@@ -2,7 +2,7 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 from pybullet_envs.bullet.kukaCamGymEnv import KukaCamGymEnv
@@ -12,39 +12,34 @@ from baselines import deepq
 import datetime
 
 
-
 def callback(lcl, glb):
-    # stop training if reward exceeds 199
-    total = sum(lcl['episode_rewards'][-101:-1]) / 100
-    totalt = lcl['t']
-    #print("totalt")
-    #print(totalt)
-    is_solved = totalt > 2000 and total >= 10
-    return is_solved
+  # stop training if reward exceeds 199
+  total = sum(lcl['episode_rewards'][-101:-1]) / 100
+  totalt = lcl['t']
+  #print("totalt")
+  #print(totalt)
+  is_solved = totalt > 2000 and total >= 10
+  return is_solved
 
 
 def main():
-  	
-    env = KukaCamGymEnv(renders=False, isDiscrete=True)
-    model = deepq.models.cnn_to_mlp(
-        convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
-        hiddens=[256],
-        dueling=False
-    )
-    act = deepq.learn(
-        env,
-        q_func=model,
-        lr=1e-3,
-        max_timesteps=10000000,
-        buffer_size=50000,
-        exploration_fraction=0.1,
-        exploration_final_eps=0.02,
-        print_freq=10,
-        callback=callback
-    )
-    print("Saving model to kuka_cam_model.pkl")
-    act.save("kuka_cam_model.pkl")
+
+  env = KukaCamGymEnv(renders=False, isDiscrete=True)
+  model = deepq.models.cnn_to_mlp(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
+                                  hiddens=[256],
+                                  dueling=False)
+  act = deepq.learn(env,
+                    q_func=model,
+                    lr=1e-3,
+                    max_timesteps=10000000,
+                    buffer_size=50000,
+                    exploration_fraction=0.1,
+                    exploration_final_eps=0.02,
+                    print_freq=10,
+                    callback=callback)
+  print("Saving model to kuka_cam_model.pkl")
+  act.save("kuka_cam_model.pkl")
 
 
 if __name__ == '__main__':
-    main()
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_grasping.py b/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_grasping.py
index 932a08e77..10225a194 100644
--- a/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_grasping.py
+++ b/examples/pybullet/gym/pybullet_envs/baselines/train_kuka_grasping.py
@@ -2,7 +2,7 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 from pybullet_envs.bullet.kukaGymEnv import KukaGymEnv
@@ -12,35 +12,32 @@ from baselines import deepq
 import datetime
 
 
-
 def callback(lcl, glb):
-    # stop training if reward exceeds 199
-    total = sum(lcl['episode_rewards'][-101:-1]) / 100
-    totalt = lcl['t']
-    #print("totalt")
-    #print(totalt)
-    is_solved = totalt > 2000 and total >= 10
-    return is_solved
+  # stop training if reward exceeds 199
+  total = sum(lcl['episode_rewards'][-101:-1]) / 100
+  totalt = lcl['t']
+  #print("totalt")
+  #print(totalt)
+  is_solved = totalt > 2000 and total >= 10
+  return is_solved
 
 
 def main():
-  	
-    env = KukaGymEnv(renders=False, isDiscrete=True)
-    model = deepq.models.mlp([64])
-    act = deepq.learn(
-        env,
-        q_func=model,
-        lr=1e-3,
-        max_timesteps=10000000,
-        buffer_size=50000,
-        exploration_fraction=0.1,
-        exploration_final_eps=0.02,
-        print_freq=10,
-        callback=callback
-    )
-    print("Saving model to kuka_model.pkl")
-    act.save("kuka_model.pkl")
+
+  env = KukaGymEnv(renders=False, isDiscrete=True)
+  model = deepq.models.mlp([64])
+  act = deepq.learn(env,
+                    q_func=model,
+                    lr=1e-3,
+                    max_timesteps=10000000,
+                    buffer_size=50000,
+                    exploration_fraction=0.1,
+                    exploration_final_eps=0.02,
+                    print_freq=10,
+                    callback=callback)
+  print("Saving model to kuka_model.pkl")
+  act.save("kuka_model.pkl")
 
 
 if __name__ == '__main__':
-    main()
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_cartpole.py b/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_cartpole.py
index f730d9154..9c369a3a2 100644
--- a/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_cartpole.py
+++ b/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_cartpole.py
@@ -2,7 +2,7 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 from pybullet_envs.bullet.cartpole_bullet import CartPoleBulletEnv
@@ -11,29 +11,27 @@ from baselines import deepq
 
 
 def callback(lcl, glb):
-    # stop training if reward exceeds 199
-    is_solved = lcl['t'] > 100 and sum(lcl['episode_rewards'][-101:-1]) / 100 >= 199
-    return is_solved
+  # stop training if reward exceeds 199
+  is_solved = lcl['t'] > 100 and sum(lcl['episode_rewards'][-101:-1]) / 100 >= 199
+  return is_solved
 
 
 def main():
-	
-    env = CartPoleBulletEnv(renders=False)
-    model = deepq.models.mlp([64])
-    act = deepq.learn(
-        env,
-        q_func=model,
-        lr=1e-3,
-        max_timesteps=100000,
-        buffer_size=50000,
-        exploration_fraction=0.1,
-        exploration_final_eps=0.02,
-        print_freq=10,
-        callback=callback
-    )
-    print("Saving model to cartpole_model.pkl")
-    act.save("cartpole_model.pkl")
+
+  env = CartPoleBulletEnv(renders=False)
+  model = deepq.models.mlp([64])
+  act = deepq.learn(env,
+                    q_func=model,
+                    lr=1e-3,
+                    max_timesteps=100000,
+                    buffer_size=50000,
+                    exploration_fraction=0.1,
+                    exploration_final_eps=0.02,
+                    print_freq=10,
+                    callback=callback)
+  print("Saving model to cartpole_model.pkl")
+  act.save("cartpole_model.pkl")
 
 
 if __name__ == '__main__':
-    main()
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_racecar.py b/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_racecar.py
index c17bc11c4..9b06bcfb2 100644
--- a/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_racecar.py
+++ b/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_racecar.py
@@ -2,7 +2,7 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 from pybullet_envs.bullet.racecarGymEnv import RacecarGymEnv
@@ -12,33 +12,30 @@ from baselines import deepq
 import datetime
 
 
-
 def callback(lcl, glb):
-    # stop training if reward exceeds 199
-    total = sum(lcl['episode_rewards'][-101:-1]) / 100
-    totalt = lcl['t']
-    is_solved = totalt > 2000 and total >= -50
-    return is_solved
+  # stop training if reward exceeds 199
+  total = sum(lcl['episode_rewards'][-101:-1]) / 100
+  totalt = lcl['t']
+  is_solved = totalt > 2000 and total >= -50
+  return is_solved
 
 
 def main():
-  
-    env = RacecarGymEnv(renders=False,isDiscrete=True)
-    model = deepq.models.mlp([64])
-    act = deepq.learn(
-        env,
-        q_func=model,
-        lr=1e-3,
-        max_timesteps=10000,
-        buffer_size=50000,
-        exploration_fraction=0.1,
-        exploration_final_eps=0.02,
-        print_freq=10,
-        callback=callback
-    )
-    print("Saving model to racecar_model.pkl")
-    act.save("racecar_model.pkl")
+
+  env = RacecarGymEnv(renders=False, isDiscrete=True)
+  model = deepq.models.mlp([64])
+  act = deepq.learn(env,
+                    q_func=model,
+                    lr=1e-3,
+                    max_timesteps=10000,
+                    buffer_size=50000,
+                    exploration_fraction=0.1,
+                    exploration_final_eps=0.02,
+                    print_freq=10,
+                    callback=callback)
+  print("Saving model to racecar_model.pkl")
+  act.save("racecar_model.pkl")
 
 
 if __name__ == '__main__':
-    main()
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_zed_racecar.py b/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_zed_racecar.py
index 68c5b0046..825197b79 100644
--- a/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_zed_racecar.py
+++ b/examples/pybullet/gym/pybullet_envs/baselines/train_pybullet_zed_racecar.py
@@ -2,7 +2,7 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 from pybullet_envs.bullet.racecarZEDGymEnv import RacecarZEDGymEnv
@@ -12,36 +12,32 @@ from baselines import deepq
 import datetime
 
 
-
 def callback(lcl, glb):
-    # stop training if reward exceeds 199
-    total = sum(lcl['episode_rewards'][-101:-1]) / 100
-    totalt = lcl['t']
-    is_solved = totalt > 2000 and total >= -50
-    return is_solved
+  # stop training if reward exceeds 199
+  total = sum(lcl['episode_rewards'][-101:-1]) / 100
+  totalt = lcl['t']
+  is_solved = totalt > 2000 and total >= -50
+  return is_solved
+
 
 def main():
-  
-    env = RacecarZEDGymEnv(renders=False, isDiscrete=True)
-    model = deepq.models.cnn_to_mlp(
-        convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
-        hiddens=[256],
-        dueling=False
-    )
-    act = deepq.learn(
-        env,
-        q_func=model,
-        lr=1e-3,
-        max_timesteps=10000,
-        buffer_size=50000,
-        exploration_fraction=0.1,
-        exploration_final_eps=0.02,
-        print_freq=10,
-        callback=callback
-    )
-    print("Saving model to racecar_zed_model.pkl")
-    act.save("racecar_zed_model.pkl")
+
+  env = RacecarZEDGymEnv(renders=False, isDiscrete=True)
+  model = deepq.models.cnn_to_mlp(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
+                                  hiddens=[256],
+                                  dueling=False)
+  act = deepq.learn(env,
+                    q_func=model,
+                    lr=1e-3,
+                    max_timesteps=10000,
+                    buffer_size=50000,
+                    exploration_fraction=0.1,
+                    exploration_final_eps=0.02,
+                    print_freq=10,
+                    callback=callback)
+  print("Saving model to racecar_zed_model.pkl")
+  act.save("racecar_zed_model.pkl")
 
 
 if __name__ == '__main__':
-    main()
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/bullet/bullet_client.py b/examples/pybullet/gym/pybullet_envs/bullet/bullet_client.py
index 13a82eef5..bd248f1d8 100644
--- a/examples/pybullet/gym/pybullet_envs/bullet/bullet_client.py
+++ b/examples/pybullet/gym/pybullet_envs/bullet/bullet_client.py
@@ -9,9 +9,9 @@ class BulletClient(object):
   def __init__(self, connection_mode=pybullet.DIRECT, options=""):
     """Create a simulation and connect to it."""
     self._client = pybullet.connect(pybullet.SHARED_MEMORY)
-    if(self._client<0):
-      print("options=",options)
-      self._client = pybullet.connect(connection_mode,options=options)
+    if (self._client < 0):
+      print("options=", options)
+      self._client = pybullet.connect(connection_mode, options=options)
     self._shapes = {}
 
   def __del__(self):
@@ -25,5 +25,5 @@ class BulletClient(object):
     """Inject the client id into Bullet functions."""
     attribute = getattr(pybullet, name)
     if inspect.isbuiltin(attribute):
-        attribute = functools.partial(attribute, physicsClientId=self._client)
+      attribute = functools.partial(attribute, physicsClientId=self._client)
     return attribute
diff --git a/examples/pybullet/gym/pybullet_envs/bullet/cartpole_bullet.py b/examples/pybullet/gym/pybullet_envs/bullet/cartpole_bullet.py
index d3e945b1a..f1b594cde 100644
--- a/examples/pybullet/gym/pybullet_envs/bullet/cartpole_bullet.py
+++ b/examples/pybullet/gym/pybullet_envs/bullet/cartpole_bullet.py
@@ -2,10 +2,10 @@
 Classic cart-pole system implemented by Rich Sutton et al.
 Copied from https://webdocs.cs.ualberta.ca/~sutton/book/code/pole.c
 """
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import logging
 import math
@@ -21,26 +21,24 @@ from pkg_resources import parse_version
 
 logger = logging.getLogger(__name__)
 
+
 class CartPoleBulletEnv(gym.Env):
-  metadata = {
-    'render.modes': ['human', 'rgb_array'],
-    'video.frames_per_second' : 50
-  }
+  metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50}
 
   def __init__(self, renders=True):
     # start the bullet physics server
     self._renders = renders
     if (renders):
-	    p.connect(p.GUI)
+      p.connect(p.GUI)
     else:
-    	p.connect(p.DIRECT)
+      p.connect(p.DIRECT)
     self.theta_threshold_radians = 12 * 2 * math.pi / 360
-    self.x_threshold = 0.4 #2.4
+    self.x_threshold = 0.4  #2.4
     high = np.array([
-         self.x_threshold * 2,
-         np.finfo(np.float32).max,
-         self.theta_threshold_radians * 2,
-         np.finfo(np.float32).max])
+        self.x_threshold * 2,
+        np.finfo(np.float32).max, self.theta_threshold_radians * 2,
+        np.finfo(np.float32).max
+    ])
 
     self.force_mag = 10
 
@@ -60,7 +58,7 @@ class CartPoleBulletEnv(gym.Env):
     return [seed]
 
   def step(self, action):
-    force = self.force_mag if action==1 else -self.force_mag
+    force = self.force_mag if action == 1 else -self.force_mag
 
     p.setJointMotorControl2(self.cartpole, 0, p.TORQUE_CONTROL, force=force)
     p.stepSimulation()
@@ -78,16 +76,17 @@ class CartPoleBulletEnv(gym.Env):
     return np.array(self.state), reward, done, {}
 
   def reset(self):
-#    print("-----------reset simulation---------------")
+    #    print("-----------reset simulation---------------")
     p.resetSimulation()
-    self.cartpole = p.loadURDF(os.path.join(pybullet_data.getDataPath(),"cartpole.urdf"),[0,0,0])
+    self.cartpole = p.loadURDF(os.path.join(pybullet_data.getDataPath(), "cartpole.urdf"),
+                               [0, 0, 0])
     p.changeDynamics(self.cartpole, -1, linearDamping=0, angularDamping=0)
     p.changeDynamics(self.cartpole, 0, linearDamping=0, angularDamping=0)
     p.changeDynamics(self.cartpole, 1, linearDamping=0, angularDamping=0)
     self.timeStep = 0.02
     p.setJointMotorControl2(self.cartpole, 1, p.VELOCITY_CONTROL, force=0)
     p.setJointMotorControl2(self.cartpole, 0, p.VELOCITY_CONTROL, force=0)
-    p.setGravity(0,0, -9.8)
+    p.setGravity(0, 0, -9.8)
     p.setTimeStep(self.timeStep)
     p.setRealTimeSimulation(0)
 
@@ -100,4 +99,4 @@ class CartPoleBulletEnv(gym.Env):
     return np.array(self.state)
 
   def render(self, mode='human', close=False):
-      return
+    return
diff --git a/examples/pybullet/gym/pybullet_envs/bullet/env_randomizer_base.py b/examples/pybullet/gym/pybullet_envs/bullet/env_randomizer_base.py
index c39f3b888..49dfc5372 100644
--- a/examples/pybullet/gym/pybullet_envs/bullet/env_randomizer_base.py
+++ b/examples/pybullet/gym/pybullet_envs/bullet/env_randomizer_base.py
@@ -22,4 +22,3 @@ class EnvRandomizerBase(object):
       env: The environment to be randomized.
     """
     pass
-
diff --git a/examples/pybullet/gym/pybullet_envs/bullet/kuka.py b/examples/pybullet/gym/pybullet_envs/bullet/kuka.py
index f9752760d..b2a4ffb30 100644
--- a/examples/pybullet/gym/pybullet_envs/bullet/kuka.py
+++ b/examples/pybullet/gym/pybullet_envs/bullet/kuka.py
@@ -1,7 +1,7 @@
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import pybullet as p
 import numpy as np
@@ -17,49 +17,60 @@ class Kuka:
     self.timeStep = timeStep
     self.maxVelocity = .35
     self.maxForce = 200.
-    self.fingerAForce = 2 
+    self.fingerAForce = 2
     self.fingerBForce = 2.5
     self.fingerTipForce = 2
     self.useInverseKinematics = 1
     self.useSimulation = 1
-    self.useNullSpace =21
+    self.useNullSpace = 21
     self.useOrientation = 1
     self.kukaEndEffectorIndex = 6
     self.kukaGripperIndex = 7
     #lower limits for null space
-    self.ll=[-.967,-2 ,-2.96,0.19,-2.96,-2.09,-3.05]
+    self.ll = [-.967, -2, -2.96, 0.19, -2.96, -2.09, -3.05]
     #upper limits for null space
-    self.ul=[.967,2 ,2.96,2.29,2.96,2.09,3.05]
+    self.ul = [.967, 2, 2.96, 2.29, 2.96, 2.09, 3.05]
     #joint ranges for null space
-    self.jr=[5.8,4,5.8,4,5.8,4,6]
+    self.jr = [5.8, 4, 5.8, 4, 5.8, 4, 6]
     #restposes for null space
-    self.rp=[0,0,0,0.5*math.pi,0,-math.pi*0.5*0.66,0]
+    self.rp = [0, 0, 0, 0.5 * math.pi, 0, -math.pi * 0.5 * 0.66, 0]
     #joint damping coefficents
-    self.jd=[0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001,0.00001]
+    self.jd = [
+        0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001,
+        0.00001, 0.00001, 0.00001, 0.00001
+    ]
     self.reset()
-    
+
   def reset(self):
-    objects = p.loadSDF(os.path.join(self.urdfRootPath,"kuka_iiwa/kuka_with_gripper2.sdf"))
+    objects = p.loadSDF(os.path.join(self.urdfRootPath, "kuka_iiwa/kuka_with_gripper2.sdf"))
     self.kukaUid = objects[0]
     #for i in range (p.getNumJoints(self.kukaUid)):
     #  print(p.getJointInfo(self.kukaUid,i))
-    p.resetBasePositionAndOrientation(self.kukaUid,[-0.100000,0.000000,0.070000],[0.000000,0.000000,0.000000,1.000000])
-    self.jointPositions=[ 0.006418, 0.413184, -0.011401, -1.589317, 0.005379, 1.137684, -0.006539, 0.000048, -0.299912, 0.000000, -0.000043, 0.299960, 0.000000, -0.000200 ]
+    p.resetBasePositionAndOrientation(self.kukaUid, [-0.100000, 0.000000, 0.070000],
+                                      [0.000000, 0.000000, 0.000000, 1.000000])
+    self.jointPositions = [
+        0.006418, 0.413184, -0.011401, -1.589317, 0.005379, 1.137684, -0.006539, 0.000048,
+        -0.299912, 0.000000, -0.000043, 0.299960, 0.000000, -0.000200
+    ]
     self.numJoints = p.getNumJoints(self.kukaUid)
-    for jointIndex in range (self.numJoints):
-      p.resetJointState(self.kukaUid,jointIndex,self.jointPositions[jointIndex])
-      p.setJointMotorControl2(self.kukaUid,jointIndex,p.POSITION_CONTROL,targetPosition=self.jointPositions[jointIndex],force=self.maxForce)
-    
-    self.trayUid = p.loadURDF(os.path.join(self.urdfRootPath,"tray/tray.urdf"), 0.640000,0.075000,-0.190000,0.000000,0.000000,1.000000,0.000000)
-    self.endEffectorPos = [0.537,0.0,0.5]
+    for jointIndex in range(self.numJoints):
+      p.resetJointState(self.kukaUid, jointIndex, self.jointPositions[jointIndex])
+      p.setJointMotorControl2(self.kukaUid,
+                              jointIndex,
+                              p.POSITION_CONTROL,
+                              targetPosition=self.jointPositions[jointIndex],
+                              force=self.maxForce)
+
+    self.trayUid = p.loadURDF(os.path.join(self.urdfRootPath, "tray/tray.urdf"), 0.640000,
+                              0.075000, -0.190000, 0.000000, 0.000000, 1.000000, 0.000000)
+    self.endEffectorPos = [0.537, 0.0, 0.5]
     self.endEffectorAngle = 0
-    
-    
+
     self.motorNames = []
     self.motorIndices = []
-    
-    for i in range (self.numJoints):
-      jointInfo = p.getJointInfo(self.kukaUid,i)
+
+    for i in range(self.numJoints):
+      jointInfo = p.getJointInfo(self.kukaUid, i)
       qIndex = jointInfo[3]
       if qIndex > -1:
         #print("motorname")
@@ -70,98 +81,136 @@ class Kuka:
   def getActionDimension(self):
     if (self.useInverseKinematics):
       return len(self.motorIndices)
-    return 6 #position x,y,z and roll/pitch/yaw euler angles of end effector
+    return 6  #position x,y,z and roll/pitch/yaw euler angles of end effector
 
   def getObservationDimension(self):
     return len(self.getObservation())
 
   def getObservation(self):
     observation = []
-    state = p.getLinkState(self.kukaUid,self.kukaGripperIndex)
+    state = p.getLinkState(self.kukaUid, self.kukaGripperIndex)
     pos = state[0]
     orn = state[1]
     euler = p.getEulerFromQuaternion(orn)
-        
+
     observation.extend(list(pos))
     observation.extend(list(euler))
-    
+
     return observation
 
   def applyAction(self, motorCommands):
-    
+
     #print ("self.numJoints")
     #print (self.numJoints)
     if (self.useInverseKinematics):
-      
+
       dx = motorCommands[0]
       dy = motorCommands[1]
       dz = motorCommands[2]
       da = motorCommands[3]
       fingerAngle = motorCommands[4]
-      
-      state = p.getLinkState(self.kukaUid,self.kukaEndEffectorIndex)
+
+      state = p.getLinkState(self.kukaUid, self.kukaEndEffectorIndex)
       actualEndEffectorPos = state[0]
       #print("pos[2] (getLinkState(kukaEndEffectorIndex)")
       #print(actualEndEffectorPos[2])
-      
-    
-      
-      self.endEffectorPos[0] = self.endEffectorPos[0]+dx
-      if (self.endEffectorPos[0]>0.65):
-        self.endEffectorPos[0]=0.65
-      if (self.endEffectorPos[0]<0.50):
-        self.endEffectorPos[0]=0.50
-      self.endEffectorPos[1] = self.endEffectorPos[1]+dy
-      if (self.endEffectorPos[1]<-0.17):
-        self.endEffectorPos[1]=-0.17
-      if (self.endEffectorPos[1]>0.22):
-        self.endEffectorPos[1]=0.22
-      
+
+      self.endEffectorPos[0] = self.endEffectorPos[0] + dx
+      if (self.endEffectorPos[0] > 0.65):
+        self.endEffectorPos[0] = 0.65
+      if (self.endEffectorPos[0] < 0.50):
+        self.endEffectorPos[0] = 0.50
+      self.endEffectorPos[1] = self.endEffectorPos[1] + dy
+      if (self.endEffectorPos[1] < -0.17):
+        self.endEffectorPos[1] = -0.17
+      if (self.endEffectorPos[1] > 0.22):
+        self.endEffectorPos[1] = 0.22
+
       #print ("self.endEffectorPos[2]")
       #print (self.endEffectorPos[2])
       #print("actualEndEffectorPos[2]")
       #print(actualEndEffectorPos[2])
       #if (dz<0 or actualEndEffectorPos[2]<0.5):
-      self.endEffectorPos[2] = self.endEffectorPos[2]+dz
-    
-     
+      self.endEffectorPos[2] = self.endEffectorPos[2] + dz
+
       self.endEffectorAngle = self.endEffectorAngle + da
       pos = self.endEffectorPos
-      orn = p.getQuaternionFromEuler([0,-math.pi,0]) # -math.pi,yaw])
-      if (self.useNullSpace==1):
-        if (self.useOrientation==1):
-          jointPoses = p.calculateInverseKinematics(self.kukaUid,self.kukaEndEffectorIndex,pos,orn,self.ll,self.ul,self.jr,self.rp)
+      orn = p.getQuaternionFromEuler([0, -math.pi, 0])  # -math.pi,yaw])
+      if (self.useNullSpace == 1):
+        if (self.useOrientation == 1):
+          jointPoses = p.calculateInverseKinematics(self.kukaUid, self.kukaEndEffectorIndex, pos,
+                                                    orn, self.ll, self.ul, self.jr, self.rp)
         else:
-          jointPoses = p.calculateInverseKinematics(self.kukaUid,self.kukaEndEffectorIndex,pos,lowerLimits=self.ll, upperLimits=self.ul, jointRanges=self.jr, restPoses=self.rp)
+          jointPoses = p.calculateInverseKinematics(self.kukaUid,
+                                                    self.kukaEndEffectorIndex,
+                                                    pos,
+                                                    lowerLimits=self.ll,
+                                                    upperLimits=self.ul,
+                                                    jointRanges=self.jr,
+                                                    restPoses=self.rp)
       else:
-        if (self.useOrientation==1):
-          jointPoses = p.calculateInverseKinematics(self.kukaUid,self.kukaEndEffectorIndex,pos,orn,jointDamping=self.jd)
+        if (self.useOrientation == 1):
+          jointPoses = p.calculateInverseKinematics(self.kukaUid,
+                                                    self.kukaEndEffectorIndex,
+                                                    pos,
+                                                    orn,
+                                                    jointDamping=self.jd)
         else:
-          jointPoses = p.calculateInverseKinematics(self.kukaUid,self.kukaEndEffectorIndex,pos)
-    
+          jointPoses = p.calculateInverseKinematics(self.kukaUid, self.kukaEndEffectorIndex, pos)
+
       #print("jointPoses")
       #print(jointPoses)
       #print("self.kukaEndEffectorIndex")
       #print(self.kukaEndEffectorIndex)
       if (self.useSimulation):
-        for i in range (self.kukaEndEffectorIndex+1):
+        for i in range(self.kukaEndEffectorIndex + 1):
           #print(i)
-          p.setJointMotorControl2(bodyUniqueId=self.kukaUid,jointIndex=i,controlMode=p.POSITION_CONTROL,targetPosition=jointPoses[i],targetVelocity=0,force=self.maxForce,maxVelocity=self.maxVelocity, positionGain=0.3,velocityGain=1)
+          p.setJointMotorControl2(bodyUniqueId=self.kukaUid,
+                                  jointIndex=i,
+                                  controlMode=p.POSITION_CONTROL,
+                                  targetPosition=jointPoses[i],
+                                  targetVelocity=0,
+                                  force=self.maxForce,
+                                  maxVelocity=self.maxVelocity,
+                                  positionGain=0.3,
+                                  velocityGain=1)
       else:
         #reset the joint state (ignoring all dynamics, not recommended to use during simulation)
-        for i in range (self.numJoints):
-          p.resetJointState(self.kukaUid,i,jointPoses[i])
+        for i in range(self.numJoints):
+          p.resetJointState(self.kukaUid, i, jointPoses[i])
       #fingers
-      p.setJointMotorControl2(self.kukaUid,7,p.POSITION_CONTROL,targetPosition=self.endEffectorAngle,force=self.maxForce)
-      p.setJointMotorControl2(self.kukaUid,8,p.POSITION_CONTROL,targetPosition=-fingerAngle,force=self.fingerAForce)
-      p.setJointMotorControl2(self.kukaUid,11,p.POSITION_CONTROL,targetPosition=fingerAngle,force=self.fingerBForce)
-      
-      p.setJointMotorControl2(self.kukaUid,10,p.POSITION_CONTROL,targetPosition=0,force=self.fingerTipForce)
-      p.setJointMotorControl2(self.kukaUid,13,p.POSITION_CONTROL,targetPosition=0,force=self.fingerTipForce)
-      
-      
+      p.setJointMotorControl2(self.kukaUid,
+                              7,
+                              p.POSITION_CONTROL,
+                              targetPosition=self.endEffectorAngle,
+                              force=self.maxForce)
+      p.setJointMotorControl2(self.kukaUid,
+                              8,
+                              p.POSITION_CONTROL,
+                              targetPosition=-fingerAngle,
+                              force=self.fingerAForce)
+      p.setJointMotorControl2(self.kukaUid,
+                              11,
+                              p.POSITION_CONTROL,
+                              targetPosition=fingerAngle,
+                              force=self.fingerBForce)
+
+      p.setJointMotorControl2(self.kukaUid,
+                              10,
+                              p.POSITION_CONTROL,
+                              targetPosition=0,
+                              force=self.fingerTipForce)
+      p.setJointMotorControl2(self.kukaUid,
+                              13,
+                              p.POSITION_CONTROL,
+                              targetPosition=0,
+                              force=self.fingerTipForce)
+
     else:
-      for action in range (len(motorCommands)):
+      for action in range(len(motorCommands)):
         motor = self.motorIndices[action]
-        p.setJointMotorControl2(self.kukaUid,motor,p.POSITION_CONTROL,targetPosition=motorCommands[action],force=self.maxForce)
-      
+        p.setJointMotorControl2(self.kukaUid,
+                                motor,
+                                p.POSITION_CONTROL,
+                                targetPosition=motorCommands[action],
+                                force=self.maxForce)
diff --git a/examples/pybullet/gym/pybullet_envs/bullet/kukaCamGymEnv.py b/examples/pybullet/gym/pybullet_envs/bullet/kukaCamGymEnv.py
index d7c0cac51..235bfa42d 100644
--- a/examples/pybullet/gym/pybullet_envs/bullet/kukaCamGymEnv.py
+++ b/examples/pybullet/gym/pybullet_envs/bullet/kukaCamGymEnv.py
@@ -1,8 +1,7 @@
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
-
+os.sys.path.insert(0, parentdir)
 
 import math
 import gym
@@ -21,11 +20,9 @@ maxSteps = 1000
 RENDER_HEIGHT = 720
 RENDER_WIDTH = 960
 
+
 class KukaCamGymEnv(gym.Env):
-  metadata = {
-      'render.modes': ['human', 'rgb_array'],
-      'video.frames_per_second' : 50
-  }
+  metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50}
 
   def __init__(self,
                urdfRoot=pybullet_data.getDataPath(),
@@ -33,7 +30,7 @@ class KukaCamGymEnv(gym.Env):
                isEnableSelfCollision=True,
                renders=False,
                isDiscrete=False):
-    self._timeStep = 1./240.
+    self._timeStep = 1. / 240.
     self._urdfRoot = urdfRoot
     self._actionRepeat = actionRepeat
     self._isEnableSelfCollision = isEnableSelfCollision
@@ -42,14 +39,14 @@ class KukaCamGymEnv(gym.Env):
     self._renders = renders
     self._width = 341
     self._height = 256
-    self._isDiscrete=isDiscrete
+    self._isDiscrete = isDiscrete
     self.terminated = 0
     self._p = p
     if self._renders:
       cid = p.connect(p.SHARED_MEMORY)
-      if (cid<0):
-         p.connect(p.GUI)
-      p.resetDebugVisualizerCamera(1.3,180,-41,[0.52,-0.2,-0.33])
+      if (cid < 0):
+        p.connect(p.GUI)
+      p.resetDebugVisualizerCamera(1.3, 180, -41, [0.52, -0.2, -0.33])
     else:
       p.connect(p.DIRECT)
     #timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "kukaTimings.json")
@@ -67,7 +64,10 @@ class KukaCamGymEnv(gym.Env):
       self._action_bound = 1
       action_high = np.array([self._action_bound] * action_dim)
       self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32)
-    self.observation_space = spaces.Box(low=0, high=255, shape=(self._height, self._width, 4), dtype=np.uint8)
+    self.observation_space = spaces.Box(low=0,
+                                        high=255,
+                                        shape=(self._height, self._width, 4),
+                                        dtype=np.uint8)
     self.viewer = None
 
   def reset(self):
@@ -75,17 +75,19 @@ class KukaCamGymEnv(gym.Env):
     p.resetSimulation()
     p.setPhysicsEngineParameter(numSolverIterations=150)
     p.setTimeStep(self._timeStep)
-    p.loadURDF(os.path.join(self._urdfRoot,"plane.urdf"),[0,0,-1])
+    p.loadURDF(os.path.join(self._urdfRoot, "plane.urdf"), [0, 0, -1])
 
-    p.loadURDF(os.path.join(self._urdfRoot,"table/table.urdf"), 0.5000000,0.00000,-.820000,0.000000,0.000000,0.0,1.0)
+    p.loadURDF(os.path.join(self._urdfRoot, "table/table.urdf"), 0.5000000, 0.00000, -.820000,
+               0.000000, 0.000000, 0.0, 1.0)
 
-    xpos = 0.5 +0.2*random.random()
-    ypos = 0 +0.25*random.random()
-    ang = 3.1415925438*random.random()
-    orn = p.getQuaternionFromEuler([0,0,ang])
-    self.blockUid =p.loadURDF(os.path.join(self._urdfRoot,"block.urdf"), xpos,ypos,-0.1,orn[0],orn[1],orn[2],orn[3])
+    xpos = 0.5 + 0.2 * random.random()
+    ypos = 0 + 0.25 * random.random()
+    ang = 3.1415925438 * random.random()
+    orn = p.getQuaternionFromEuler([0, 0, ang])
+    self.blockUid = p.loadURDF(os.path.join(self._urdfRoot, "block.urdf"), xpos, ypos, -0.1,
+                               orn[0], orn[1], orn[2], orn[3])
 
-    p.setGravity(0,0,-10)
+    p.setGravity(0, 0, -10)
     self._kuka = kuka.Kuka(urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
     self._envStepCounter = 0
     p.stepSimulation()
@@ -101,49 +103,59 @@ class KukaCamGymEnv(gym.Env):
 
   def getExtendedObservation(self):
 
-     #camEyePos = [0.03,0.236,0.54]
-     #distance = 1.06
-     #pitch=-56
-     #yaw = 258
-     #roll=0
-     #upAxisIndex = 2
-     #camInfo = p.getDebugVisualizerCamera()
-     #print("width,height")
-     #print(camInfo[0])
-     #print(camInfo[1])
-     #print("viewMatrix")
-     #print(camInfo[2])
-     #print("projectionMatrix")
-     #print(camInfo[3])
-     #viewMat = camInfo[2]
-     #viewMat = p.computeViewMatrixFromYawPitchRoll(camEyePos,distance,yaw, pitch,roll,upAxisIndex)
-     viewMat = [-0.5120397806167603, 0.7171027660369873, -0.47284144163131714, 0.0, -0.8589617609977722, -0.42747554183006287, 0.28186774253845215, 0.0, 0.0, 0.5504802465438843, 0.8348482847213745, 0.0, 0.1925382763147354, -0.24935829639434814, -0.4401884973049164, 1.0]
-     #projMatrix = camInfo[3]#[0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0]
-     projMatrix = [0.75, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0]
-
-     img_arr = p.getCameraImage(width=self._width,height=self._height,viewMatrix=viewMat,projectionMatrix=projMatrix)
-     rgb=img_arr[2]
-     np_img_arr = np.reshape(rgb, (self._height, self._width, 4))
-     self._observation = np_img_arr
-     return self._observation
+    #camEyePos = [0.03,0.236,0.54]
+    #distance = 1.06
+    #pitch=-56
+    #yaw = 258
+    #roll=0
+    #upAxisIndex = 2
+    #camInfo = p.getDebugVisualizerCamera()
+    #print("width,height")
+    #print(camInfo[0])
+    #print(camInfo[1])
+    #print("viewMatrix")
+    #print(camInfo[2])
+    #print("projectionMatrix")
+    #print(camInfo[3])
+    #viewMat = camInfo[2]
+    #viewMat = p.computeViewMatrixFromYawPitchRoll(camEyePos,distance,yaw, pitch,roll,upAxisIndex)
+    viewMat = [
+        -0.5120397806167603, 0.7171027660369873, -0.47284144163131714, 0.0, -0.8589617609977722,
+        -0.42747554183006287, 0.28186774253845215, 0.0, 0.0, 0.5504802465438843,
+        0.8348482847213745, 0.0, 0.1925382763147354, -0.24935829639434814, -0.4401884973049164, 1.0
+    ]
+    #projMatrix = camInfo[3]#[0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0]
+    projMatrix = [
+        0.75, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0,
+        -0.02000020071864128, 0.0
+    ]
+
+    img_arr = p.getCameraImage(width=self._width,
+                               height=self._height,
+                               viewMatrix=viewMat,
+                               projectionMatrix=projMatrix)
+    rgb = img_arr[2]
+    np_img_arr = np.reshape(rgb, (self._height, self._width, 4))
+    self._observation = np_img_arr
+    return self._observation
 
   def step(self, action):
     if (self._isDiscrete):
       dv = 0.01
-      dx = [0,-dv,dv,0,0,0,0][action]
-      dy = [0,0,0,-dv,dv,0,0][action]
-      da = [0,0,0,0,0,-0.1,0.1][action]
+      dx = [0, -dv, dv, 0, 0, 0, 0][action]
+      dy = [0, 0, 0, -dv, dv, 0, 0][action]
+      da = [0, 0, 0, 0, 0, -0.1, 0.1][action]
       f = 0.3
-      realAction = [dx,dy,-0.002,da,f]
+      realAction = [dx, dy, -0.002, da, f]
     else:
       dv = 0.01
       dx = action[0] * dv
       dy = action[1] * dv
       da = action[2] * 0.1
       f = 0.3
-      realAction = [dx,dy,-0.002,da,f]
+      realAction = [dx, dy, -0.002, da, f]
 
-    return self.step2( realAction)
+    return self.step2(realAction)
 
   def step2(self, action):
     for i in range(self._actionRepeat):
@@ -156,7 +168,7 @@ class KukaCamGymEnv(gym.Env):
 
     self._observation = self.getExtendedObservation()
     if self._renders:
-        time.sleep(self._timeStep)
+      time.sleep(self._timeStep)
 
     #print("self._envStepCounter")
     #print(self._envStepCounter)
@@ -170,66 +182,67 @@ class KukaCamGymEnv(gym.Env):
   def render(self, mode='human', close=False):
     if mode != "rgb_array":
       return np.array([])
-    base_pos,orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
-    view_matrix = self._p.computeViewMatrixFromYawPitchRoll(
-        cameraTargetPosition=base_pos,
-        distance=self._cam_dist,
-        yaw=self._cam_yaw,
-        pitch=self._cam_pitch,
-        roll=0,
-        upAxisIndex=2)
-    proj_matrix = self._p.computeProjectionMatrixFOV(
-        fov=60, aspect=float(RENDER_WIDTH)/RENDER_HEIGHT,
-        nearVal=0.1, farVal=100.0)
-    (_, _, px, _, _) = self._p.getCameraImage(
-        width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix,
-        projectionMatrix=proj_matrix, renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
+    base_pos, orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
+    view_matrix = self._p.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=base_pos,
+                                                            distance=self._cam_dist,
+                                                            yaw=self._cam_yaw,
+                                                            pitch=self._cam_pitch,
+                                                            roll=0,
+                                                            upAxisIndex=2)
+    proj_matrix = self._p.computeProjectionMatrixFOV(fov=60,
+                                                     aspect=float(RENDER_WIDTH) / RENDER_HEIGHT,
+                                                     nearVal=0.1,
+                                                     farVal=100.0)
+    (_, _, px, _, _) = self._p.getCameraImage(width=RENDER_WIDTH,
+                                              height=RENDER_HEIGHT,
+                                              viewMatrix=view_matrix,
+                                              projectionMatrix=proj_matrix,
+                                              renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
     rgb_array = np.array(px)
     rgb_array = rgb_array[:, :, :3]
     return rgb_array
 
   def _termination(self):
     #print (self._kuka.endEffectorPos[2])
-    state = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaEndEffectorIndex)
+    state = p.getLinkState(self._kuka.kukaUid, self._kuka.kukaEndEffectorIndex)
     actualEndEffectorPos = state[0]
 
     #print("self._envStepCounter")
     #print(self._envStepCounter)
-    if (self.terminated or self._envStepCounter>maxSteps):
+    if (self.terminated or self._envStepCounter > maxSteps):
       self._observation = self.getExtendedObservation()
       return True
     maxDist = 0.005
-    closestPoints = p.getClosestPoints(self._kuka.trayUid, self._kuka.kukaUid,maxDist)
+    closestPoints = p.getClosestPoints(self._kuka.trayUid, self._kuka.kukaUid, maxDist)
 
-    if (len(closestPoints)):#(actualEndEffectorPos[2] <= -0.43):
+    if (len(closestPoints)):  #(actualEndEffectorPos[2] <= -0.43):
       self.terminated = 1
 
       #print("closing gripper, attempting grasp")
       #start grasp and terminate
       fingerAngle = 0.3
-      for i in range (100):
-        graspAction = [0,0,0.0001,0,fingerAngle]
+      for i in range(100):
+        graspAction = [0, 0, 0.0001, 0, fingerAngle]
         self._kuka.applyAction(graspAction)
         p.stepSimulation()
-        fingerAngle = fingerAngle-(0.3/100.)
-        if (fingerAngle<0):
-          fingerAngle=0
+        fingerAngle = fingerAngle - (0.3 / 100.)
+        if (fingerAngle < 0):
+          fingerAngle = 0
 
-      for i in range (1000):
-        graspAction = [0,0,0.001,0,fingerAngle]
+      for i in range(1000):
+        graspAction = [0, 0, 0.001, 0, fingerAngle]
         self._kuka.applyAction(graspAction)
         p.stepSimulation()
-        blockPos,blockOrn=p.getBasePositionAndOrientation(self.blockUid)
+        blockPos, blockOrn = p.getBasePositionAndOrientation(self.blockUid)
         if (blockPos[2] > 0.23):
           #print("BLOCKPOS!")
           #print(blockPos[2])
           break
-        state = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaEndEffectorIndex)
+        state = p.getLinkState(self._kuka.kukaUid, self._kuka.kukaEndEffectorIndex)
         actualEndEffectorPos = state[0]
-        if (actualEndEffectorPos[2]>0.5):
+        if (actualEndEffectorPos[2] > 0.5):
           break
 
-
       self._observation = self.getExtendedObservation()
       return True
     return False
@@ -237,20 +250,21 @@ class KukaCamGymEnv(gym.Env):
   def _reward(self):
 
     #rewards is height of target object
-    blockPos,blockOrn=p.getBasePositionAndOrientation(self.blockUid)
-    closestPoints = p.getClosestPoints(self.blockUid,self._kuka.kukaUid,1000, -1, self._kuka.kukaEndEffectorIndex)
+    blockPos, blockOrn = p.getBasePositionAndOrientation(self.blockUid)
+    closestPoints = p.getClosestPoints(self.blockUid, self._kuka.kukaUid, 1000, -1,
+                                       self._kuka.kukaEndEffectorIndex)
 
     reward = -1000
     numPt = len(closestPoints)
     #print(numPt)
-    if (numPt>0):
+    if (numPt > 0):
       #print("reward:")
-      reward = -closestPoints[0][8]*10
-    if (blockPos[2] >0.2):
+      reward = -closestPoints[0][8] * 10
+    if (blockPos[2] > 0.2):
       #print("grasped a block!!!")
       #print("self._envStepCounter")
       #print(self._envStepCounter)
-      reward = reward+1000
+      reward = reward + 1000
 
     #print("reward")
     #print(reward)
diff --git a/examples/pybullet/gym/pybullet_envs/bullet/kukaGymEnv.py b/examples/pybullet/gym/pybullet_envs/bullet/kukaGymEnv.py
index e1310c861..f47a0dd13 100644
--- a/examples/pybullet/gym/pybullet_envs/bullet/kukaGymEnv.py
+++ b/examples/pybullet/gym/pybullet_envs/bullet/kukaGymEnv.py
@@ -1,7 +1,7 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
-print ("current_dir=" + currentdir)
-os.sys.path.insert(0,currentdir)
+print("current_dir=" + currentdir)
+os.sys.path.insert(0, currentdir)
 
 import math
 import gym
@@ -20,11 +20,9 @@ largeValObservation = 100
 RENDER_HEIGHT = 720
 RENDER_WIDTH = 960
 
+
 class KukaGymEnv(gym.Env):
-  metadata = {
-      'render.modes': ['human', 'rgb_array'],
-      'video.frames_per_second' : 50
-  }
+  metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50}
 
   def __init__(self,
                urdfRoot=pybullet_data.getDataPath(),
@@ -32,10 +30,10 @@ class KukaGymEnv(gym.Env):
                isEnableSelfCollision=True,
                renders=False,
                isDiscrete=False,
-               maxSteps = 1000):
+               maxSteps=1000):
     #print("KukaGymEnv __init__")
     self._isDiscrete = isDiscrete
-    self._timeStep = 1./240.
+    self._timeStep = 1. / 240.
     self._urdfRoot = urdfRoot
     self._actionRepeat = actionRepeat
     self._isEnableSelfCollision = isEnableSelfCollision
@@ -51,9 +49,9 @@ class KukaGymEnv(gym.Env):
     self._p = p
     if self._renders:
       cid = p.connect(p.SHARED_MEMORY)
-      if (cid<0):
-         cid = p.connect(p.GUI)
-      p.resetDebugVisualizerCamera(1.3,180,-41,[0.52,-0.2,-0.33])
+      if (cid < 0):
+        cid = p.connect(p.GUI)
+      p.resetDebugVisualizerCamera(1.3, 180, -41, [0.52, -0.2, -0.33])
     else:
       p.connect(p.DIRECT)
     #timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "kukaTimings.json")
@@ -67,10 +65,10 @@ class KukaGymEnv(gym.Env):
     if (self._isDiscrete):
       self.action_space = spaces.Discrete(7)
     else:
-       action_dim = 3
-       self._action_bound = 1
-       action_high = np.array([self._action_bound] * action_dim)
-       self.action_space = spaces.Box(-action_high, action_high)
+      action_dim = 3
+      self._action_bound = 1
+      action_high = np.array([self._action_bound] * action_dim)
+      self.action_space = spaces.Box(-action_high, action_high)
     self.observation_space = spaces.Box(-observation_high, observation_high)
     self.viewer = None
 
@@ -80,17 +78,19 @@ class KukaGymEnv(gym.Env):
     p.resetSimulation()
     p.setPhysicsEngineParameter(numSolverIterations=150)
     p.setTimeStep(self._timeStep)
-    p.loadURDF(os.path.join(self._urdfRoot,"plane.urdf"),[0,0,-1])
+    p.loadURDF(os.path.join(self._urdfRoot, "plane.urdf"), [0, 0, -1])
 
-    p.loadURDF(os.path.join(self._urdfRoot,"table/table.urdf"), 0.5000000,0.00000,-.820000,0.000000,0.000000,0.0,1.0)
+    p.loadURDF(os.path.join(self._urdfRoot, "table/table.urdf"), 0.5000000, 0.00000, -.820000,
+               0.000000, 0.000000, 0.0, 1.0)
 
-    xpos = 0.55 +0.12*random.random()
-    ypos = 0 +0.2*random.random()
-    ang = 3.14*0.5+3.1415925438*random.random()
-    orn = p.getQuaternionFromEuler([0,0,ang])
-    self.blockUid =p.loadURDF(os.path.join(self._urdfRoot,"block.urdf"), xpos,ypos,-0.15,orn[0],orn[1],orn[2],orn[3])
+    xpos = 0.55 + 0.12 * random.random()
+    ypos = 0 + 0.2 * random.random()
+    ang = 3.14 * 0.5 + 3.1415925438 * random.random()
+    orn = p.getQuaternionFromEuler([0, 0, ang])
+    self.blockUid = p.loadURDF(os.path.join(self._urdfRoot, "block.urdf"), xpos, ypos, -0.15,
+                               orn[0], orn[1], orn[2], orn[3])
 
-    p.setGravity(0,0,-10)
+    p.setGravity(0, 0, -10)
     self._kuka = kuka.Kuka(urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
     self._envStepCounter = 0
     p.stepSimulation()
@@ -105,47 +105,48 @@ class KukaGymEnv(gym.Env):
     return [seed]
 
   def getExtendedObservation(self):
-     self._observation = self._kuka.getObservation()
-     gripperState  = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaGripperIndex)
-     gripperPos = gripperState[0]
-     gripperOrn = gripperState[1]
-     blockPos,blockOrn = p.getBasePositionAndOrientation(self.blockUid)
-
-     invGripperPos,invGripperOrn = p.invertTransform(gripperPos,gripperOrn)
-     gripperMat = p.getMatrixFromQuaternion(gripperOrn)
-     dir0 = [gripperMat[0],gripperMat[3],gripperMat[6]]
-     dir1 = [gripperMat[1],gripperMat[4],gripperMat[7]]
-     dir2 = [gripperMat[2],gripperMat[5],gripperMat[8]]
-
-     gripperEul =  p.getEulerFromQuaternion(gripperOrn)
-     #print("gripperEul")
-     #print(gripperEul)
-     blockPosInGripper,blockOrnInGripper = p.multiplyTransforms(invGripperPos,invGripperOrn,blockPos,blockOrn)
-     projectedBlockPos2D =[blockPosInGripper[0],blockPosInGripper[1]]
-     blockEulerInGripper = p.getEulerFromQuaternion(blockOrnInGripper)
-     #print("projectedBlockPos2D")
-     #print(projectedBlockPos2D)
-     #print("blockEulerInGripper")
-     #print(blockEulerInGripper)
-
-     #we return the relative x,y position and euler angle of block in gripper space
-     blockInGripperPosXYEulZ =[blockPosInGripper[0],blockPosInGripper[1],blockEulerInGripper[2]]
-
-     #p.addUserDebugLine(gripperPos,[gripperPos[0]+dir0[0],gripperPos[1]+dir0[1],gripperPos[2]+dir0[2]],[1,0,0],lifeTime=1)
-     #p.addUserDebugLine(gripperPos,[gripperPos[0]+dir1[0],gripperPos[1]+dir1[1],gripperPos[2]+dir1[2]],[0,1,0],lifeTime=1)
-     #p.addUserDebugLine(gripperPos,[gripperPos[0]+dir2[0],gripperPos[1]+dir2[1],gripperPos[2]+dir2[2]],[0,0,1],lifeTime=1)
-
-     self._observation.extend(list(blockInGripperPosXYEulZ))
-     return self._observation
+    self._observation = self._kuka.getObservation()
+    gripperState = p.getLinkState(self._kuka.kukaUid, self._kuka.kukaGripperIndex)
+    gripperPos = gripperState[0]
+    gripperOrn = gripperState[1]
+    blockPos, blockOrn = p.getBasePositionAndOrientation(self.blockUid)
+
+    invGripperPos, invGripperOrn = p.invertTransform(gripperPos, gripperOrn)
+    gripperMat = p.getMatrixFromQuaternion(gripperOrn)
+    dir0 = [gripperMat[0], gripperMat[3], gripperMat[6]]
+    dir1 = [gripperMat[1], gripperMat[4], gripperMat[7]]
+    dir2 = [gripperMat[2], gripperMat[5], gripperMat[8]]
+
+    gripperEul = p.getEulerFromQuaternion(gripperOrn)
+    #print("gripperEul")
+    #print(gripperEul)
+    blockPosInGripper, blockOrnInGripper = p.multiplyTransforms(invGripperPos, invGripperOrn,
+                                                                blockPos, blockOrn)
+    projectedBlockPos2D = [blockPosInGripper[0], blockPosInGripper[1]]
+    blockEulerInGripper = p.getEulerFromQuaternion(blockOrnInGripper)
+    #print("projectedBlockPos2D")
+    #print(projectedBlockPos2D)
+    #print("blockEulerInGripper")
+    #print(blockEulerInGripper)
+
+    #we return the relative x,y position and euler angle of block in gripper space
+    blockInGripperPosXYEulZ = [blockPosInGripper[0], blockPosInGripper[1], blockEulerInGripper[2]]
+
+    #p.addUserDebugLine(gripperPos,[gripperPos[0]+dir0[0],gripperPos[1]+dir0[1],gripperPos[2]+dir0[2]],[1,0,0],lifeTime=1)
+    #p.addUserDebugLine(gripperPos,[gripperPos[0]+dir1[0],gripperPos[1]+dir1[1],gripperPos[2]+dir1[2]],[0,1,0],lifeTime=1)
+    #p.addUserDebugLine(gripperPos,[gripperPos[0]+dir2[0],gripperPos[1]+dir2[1],gripperPos[2]+dir2[2]],[0,0,1],lifeTime=1)
+
+    self._observation.extend(list(blockInGripperPosXYEulZ))
+    return self._observation
 
   def step(self, action):
     if (self._isDiscrete):
       dv = 0.005
-      dx = [0,-dv,dv,0,0,0,0][action]
-      dy = [0,0,0,-dv,dv,0,0][action]
-      da = [0,0,0,0,0,-0.05,0.05][action]
+      dx = [0, -dv, dv, 0, 0, 0, 0][action]
+      dy = [0, 0, 0, -dv, dv, 0, 0][action]
+      da = [0, 0, 0, 0, 0, -0.05, 0.05][action]
       f = 0.3
-      realAction = [dx,dy,-0.002,da,f]
+      realAction = [dx, dy, -0.002, da, f]
     else:
       #print("action[0]=", str(action[0]))
       dv = 0.005
@@ -153,8 +154,8 @@ class KukaGymEnv(gym.Env):
       dy = action[1] * dv
       da = action[2] * 0.05
       f = 0.3
-      realAction = [dx,dy,-0.002,da,f]
-    return self.step2( realAction)
+      realAction = [dx, dy, -0.002, da, f]
+    return self.step2(realAction)
 
   def step2(self, action):
     for i in range(self._actionRepeat):
@@ -171,11 +172,13 @@ class KukaGymEnv(gym.Env):
     #print(self._envStepCounter)
 
     done = self._termination()
-    npaction = np.array([action[3]]) #only penalize rotation until learning works well [action[0],action[1],action[3]])
-    actionCost = np.linalg.norm(npaction)*10.
+    npaction = np.array([
+        action[3]
+    ])  #only penalize rotation until learning works well [action[0],action[1],action[3]])
+    actionCost = np.linalg.norm(npaction) * 10.
     #print("actionCost")
     #print(actionCost)
-    reward = self._reward()-actionCost
+    reward = self._reward() - actionCost
     #print("reward")
     #print(reward)
 
@@ -187,22 +190,23 @@ class KukaGymEnv(gym.Env):
     if mode != "rgb_array":
       return np.array([])
 
-    base_pos,orn = self._p.getBasePositionAndOrientation(self._kuka.kukaUid)
-    view_matrix = self._p.computeViewMatrixFromYawPitchRoll(
-        cameraTargetPosition=base_pos,
-        distance=self._cam_dist,
-        yaw=self._cam_yaw,
-        pitch=self._cam_pitch,
-        roll=0,
-        upAxisIndex=2)
-    proj_matrix = self._p.computeProjectionMatrixFOV(
-        fov=60, aspect=float(RENDER_WIDTH)/RENDER_HEIGHT,
-        nearVal=0.1, farVal=100.0)
-    (_, _, px, _, _) = self._p.getCameraImage(
-        width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix,
-        projectionMatrix=proj_matrix, renderer=self._p.ER_BULLET_HARDWARE_OPENGL)
-        #renderer=self._p.ER_TINY_RENDERER)
-
+    base_pos, orn = self._p.getBasePositionAndOrientation(self._kuka.kukaUid)
+    view_matrix = self._p.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=base_pos,
+                                                            distance=self._cam_dist,
+                                                            yaw=self._cam_yaw,
+                                                            pitch=self._cam_pitch,
+                                                            roll=0,
+                                                            upAxisIndex=2)
+    proj_matrix = self._p.computeProjectionMatrixFOV(fov=60,
+                                                     aspect=float(RENDER_WIDTH) / RENDER_HEIGHT,
+                                                     nearVal=0.1,
+                                                     farVal=100.0)
+    (_, _, px, _, _) = self._p.getCameraImage(width=RENDER_WIDTH,
+                                              height=RENDER_HEIGHT,
+                                              viewMatrix=view_matrix,
+                                              projectionMatrix=proj_matrix,
+                                              renderer=self._p.ER_BULLET_HARDWARE_OPENGL)
+    #renderer=self._p.ER_TINY_RENDERER)
 
     rgb_array = np.array(px, dtype=np.uint8)
     rgb_array = np.reshape(rgb_array, (RENDER_HEIGHT, RENDER_WIDTH, 4))
@@ -210,49 +214,47 @@ class KukaGymEnv(gym.Env):
     rgb_array = rgb_array[:, :, :3]
     return rgb_array
 
-
   def _termination(self):
     #print (self._kuka.endEffectorPos[2])
-    state = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaEndEffectorIndex)
+    state = p.getLinkState(self._kuka.kukaUid, self._kuka.kukaEndEffectorIndex)
     actualEndEffectorPos = state[0]
 
     #print("self._envStepCounter")
     #print(self._envStepCounter)
-    if (self.terminated or self._envStepCounter>self._maxSteps):
+    if (self.terminated or self._envStepCounter > self._maxSteps):
       self._observation = self.getExtendedObservation()
       return True
     maxDist = 0.005
-    closestPoints = p.getClosestPoints(self._kuka.trayUid, self._kuka.kukaUid,maxDist)
+    closestPoints = p.getClosestPoints(self._kuka.trayUid, self._kuka.kukaUid, maxDist)
 
-    if (len(closestPoints)):#(actualEndEffectorPos[2] <= -0.43):
+    if (len(closestPoints)):  #(actualEndEffectorPos[2] <= -0.43):
       self.terminated = 1
 
       #print("terminating, closing gripper, attempting grasp")
       #start grasp and terminate
       fingerAngle = 0.3
-      for i in range (100):
-        graspAction = [0,0,0.0001,0,fingerAngle]
+      for i in range(100):
+        graspAction = [0, 0, 0.0001, 0, fingerAngle]
         self._kuka.applyAction(graspAction)
         p.stepSimulation()
-        fingerAngle = fingerAngle-(0.3/100.)
-        if (fingerAngle<0):
-          fingerAngle=0
+        fingerAngle = fingerAngle - (0.3 / 100.)
+        if (fingerAngle < 0):
+          fingerAngle = 0
 
-      for i in range (1000):
-        graspAction = [0,0,0.001,0,fingerAngle]
+      for i in range(1000):
+        graspAction = [0, 0, 0.001, 0, fingerAngle]
         self._kuka.applyAction(graspAction)
         p.stepSimulation()
-        blockPos,blockOrn=p.getBasePositionAndOrientation(self.blockUid)
+        blockPos, blockOrn = p.getBasePositionAndOrientation(self.blockUid)
         if (blockPos[2] > 0.23):
           #print("BLOCKPOS!")
           #print(blockPos[2])
           break
-        state = p.getLinkState(self._kuka.kukaUid,self._kuka.kukaEndEffectorIndex)
+        state = p.getLinkState(self._kuka.kukaUid, self._kuka.kukaEndEffectorIndex)
         actualEndEffectorPos = state[0]
-        if (actualEndEffectorPos[2]>0.5):
+        if (actualEndEffectorPos[2] > 0.5):
           break
 
-
       self._observation = self.getExtendedObservation()
       return True
     return False
@@ -260,18 +262,19 @@ class KukaGymEnv(gym.Env):
   def _reward(self):
 
     #rewards is height of target object
-    blockPos,blockOrn=p.getBasePositionAndOrientation(self.blockUid)
-    closestPoints = p.getClosestPoints(self.blockUid,self._kuka.kukaUid,1000, -1, self._kuka.kukaEndEffectorIndex)
+    blockPos, blockOrn = p.getBasePositionAndOrientation(self.blockUid)
+    closestPoints = p.getClosestPoints(self.blockUid, self._kuka.kukaUid, 1000, -1,
+                                       self._kuka.kukaEndEffectorIndex)
 
     reward = -1000
 
     numPt = len(closestPoints)
     #print(numPt)
-    if (numPt>0):
+    if (numPt > 0):
       #print("reward:")
-      reward = -closestPoints[0][8]*10
-    if (blockPos[2] >0.2):
-      reward = reward+10000
+      reward = -closestPoints[0][8] * 10
+    if (blockPos[2] > 0.2):
+      reward = reward + 10000
       print("successfully grasped a block!!!")
       #print("self._envStepCounter")
       #print(self._envStepCounter)
@@ -279,7 +282,7 @@ class KukaGymEnv(gym.Env):
       #print(self._envStepCounter)
       #print("reward")
       #print(reward)
-     #print("reward")
+    #print("reward")
     #print(reward)
     return reward
 
diff --git a/examples/pybullet/gym/pybullet_envs/bullet/kuka_diverse_object_gym_env.py b/examples/pybullet/gym/pybullet_envs/bullet/kuka_diverse_object_gym_env.py
index c2c4d2acc..696dc268a 100644
--- a/examples/pybullet/gym/pybullet_envs/bullet/kuka_diverse_object_gym_env.py
+++ b/examples/pybullet/gym/pybullet_envs/bullet/kuka_diverse_object_gym_env.py
@@ -13,6 +13,7 @@ import glob
 from pkg_resources import parse_version
 import gym
 
+
 class KukaDiverseObjectEnv(KukaGymEnv):
   """Class for Kuka environment with diverse objects.
 
@@ -61,7 +62,7 @@ class KukaDiverseObjectEnv(KukaGymEnv):
     """
 
     self._isDiscrete = isDiscrete
-    self._timeStep = 1./240.
+    self._timeStep = 1. / 240.
     self._urdfRoot = urdfRoot
     self._actionRepeat = actionRepeat
     self._isEnableSelfCollision = isEnableSelfCollision
@@ -85,9 +86,9 @@ class KukaDiverseObjectEnv(KukaGymEnv):
 
     if self._renders:
       self.cid = p.connect(p.SHARED_MEMORY)
-      if (self.cid<0):
-         self.cid = p.connect(p.GUI)
-      p.resetDebugVisualizerCamera(1.3,180,-41,[0.52,-0.2,-0.33])
+      if (self.cid < 0):
+        self.cid = p.connect(p.GUI)
+      p.resetDebugVisualizerCamera(1.3, 180, -41, [0.52, -0.2, -0.33])
     else:
       self.cid = p.connect(p.DIRECT)
     self.seed()
@@ -100,9 +101,7 @@ class KukaDiverseObjectEnv(KukaGymEnv):
     else:
       self.action_space = spaces.Box(low=-1, high=1, shape=(3,))  # dx, dy, da
       if self._removeHeightHack:
-        self.action_space = spaces.Box(low=-1,
-                                       high=1,
-                                       shape=(4,))  # dx, dy, dz, da
+        self.action_space = spaces.Box(low=-1, high=1, shape=(4,))  # dx, dy, dz, da
     self.viewer = None
 
   def reset(self):
@@ -111,17 +110,15 @@ class KukaDiverseObjectEnv(KukaGymEnv):
     # Set the camera settings.
     look = [0.23, 0.2, 0.54]
     distance = 1.
-    pitch = -56 + self._cameraRandom*np.random.uniform(-3, 3)
-    yaw = 245 + self._cameraRandom*np.random.uniform(-3, 3)
+    pitch = -56 + self._cameraRandom * np.random.uniform(-3, 3)
+    yaw = 245 + self._cameraRandom * np.random.uniform(-3, 3)
     roll = 0
-    self._view_matrix = p.computeViewMatrixFromYawPitchRoll(
-        look, distance, yaw, pitch, roll, 2)
-    fov = 20. + self._cameraRandom*np.random.uniform(-2, 2)
+    self._view_matrix = p.computeViewMatrixFromYawPitchRoll(look, distance, yaw, pitch, roll, 2)
+    fov = 20. + self._cameraRandom * np.random.uniform(-2, 2)
     aspect = self._width / self._height
     near = 0.01
     far = 10
-    self._proj_matrix = p.computeProjectionMatrixFOV(
-        fov, aspect, near, far)
+    self._proj_matrix = p.computeProjectionMatrixFOV(fov, aspect, near, far)
 
     self._attempted_grasp = False
     self._env_step = 0
@@ -130,18 +127,18 @@ class KukaDiverseObjectEnv(KukaGymEnv):
     p.resetSimulation()
     p.setPhysicsEngineParameter(numSolverIterations=150)
     p.setTimeStep(self._timeStep)
-    p.loadURDF(os.path.join(self._urdfRoot,"plane.urdf"),[0,0,-1])
+    p.loadURDF(os.path.join(self._urdfRoot, "plane.urdf"), [0, 0, -1])
 
-    p.loadURDF(os.path.join(self._urdfRoot,"table/table.urdf"), 0.5000000,0.00000,-.820000,0.000000,0.000000,0.0,1.0)
+    p.loadURDF(os.path.join(self._urdfRoot, "table/table.urdf"), 0.5000000, 0.00000, -.820000,
+               0.000000, 0.000000, 0.0, 1.0)
 
-    p.setGravity(0,0,-10)
+    p.setGravity(0, 0, -10)
     self._kuka = kuka.Kuka(urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
     self._envStepCounter = 0
     p.stepSimulation()
 
     # Choose the objects in the bin.
-    urdfList = self._get_random_object(
-      self._numObjects, self._isTest)
+    urdfList = self._get_random_object(self._numObjects, self._isTest)
     self._objectUids = self._randomly_place_objects(urdfList)
     self._observation = self._get_observation()
     return np.array(self._observation)
@@ -156,17 +153,15 @@ class KukaDiverseObjectEnv(KukaGymEnv):
       The list of object unique ID's.
     """
 
-
     # Randomize positions of each object urdf.
     objectUids = []
     for urdf_name in urdfList:
-      xpos = 0.4 +self._blockRandom*random.random()
-      ypos = self._blockRandom*(random.random()-.5)
-      angle = np.pi/2 + self._blockRandom * np.pi * random.random()
+      xpos = 0.4 + self._blockRandom * random.random()
+      ypos = self._blockRandom * (random.random() - .5)
+      angle = np.pi / 2 + self._blockRandom * np.pi * random.random()
       orn = p.getQuaternionFromEuler([0, 0, angle])
       urdf_path = os.path.join(self._urdfRoot, urdf_name)
-      uid = p.loadURDF(urdf_path, [xpos, ypos, .15],
-        [orn[0], orn[1], orn[2], orn[3]])
+      uid = p.loadURDF(urdf_path, [xpos, ypos, .15], [orn[0], orn[1], orn[2], orn[3]])
       objectUids.append(uid)
       # Let each object fall to the tray individual, to prevent object
       # intersection.
@@ -178,9 +173,9 @@ class KukaDiverseObjectEnv(KukaGymEnv):
     """Return the observation as an image.
     """
     img_arr = p.getCameraImage(width=self._width,
-                                      height=self._height,
-                                      viewMatrix=self._view_matrix,
-                                      projectionMatrix=self._proj_matrix)
+                               height=self._height,
+                               viewMatrix=self._view_matrix,
+                               projectionMatrix=self._proj_matrix)
     rgb = img_arr[2]
     np_img_arr = np.reshape(rgb, (self._height, self._width, 4))
     return np_img_arr[:, :, :3]
@@ -246,8 +241,7 @@ class KukaDiverseObjectEnv(KukaGymEnv):
         break
 
     # If we are close to the bin, attempt grasp.
-    state = p.getLinkState(self._kuka.kukaUid,
-                                  self._kuka.kukaEndEffectorIndex)
+    state = p.getLinkState(self._kuka.kukaUid, self._kuka.kukaEndEffectorIndex)
     end_effector_pos = state[0]
     if end_effector_pos[2] <= 0.1:
       finger_angle = 0.3
@@ -257,7 +251,7 @@ class KukaDiverseObjectEnv(KukaGymEnv):
         p.stepSimulation()
         #if self._renders:
         #  time.sleep(self._timeStep)
-        finger_angle -= 0.3/100.
+        finger_angle -= 0.3 / 100.
         if finger_angle < 0:
           finger_angle = 0
       for _ in range(500):
@@ -266,7 +260,7 @@ class KukaDiverseObjectEnv(KukaGymEnv):
         p.stepSimulation()
         if self._renders:
           time.sleep(self._timeStep)
-        finger_angle -= 0.3/100.
+        finger_angle -= 0.3 / 100.
         if finger_angle < 0:
           finger_angle = 0
       self._attempted_grasp = True
@@ -274,9 +268,7 @@ class KukaDiverseObjectEnv(KukaGymEnv):
     done = self._termination()
     reward = self._reward()
 
-    debug = {
-        'grasp_success': self._graspSuccess
-    }
+    debug = {'grasp_success': self._graspSuccess}
     return observation, reward, done, debug
 
   def _reward(self):
@@ -288,8 +280,7 @@ class KukaDiverseObjectEnv(KukaGymEnv):
     reward = 0
     self._graspSuccess = 0
     for uid in self._objectUids:
-      pos, _ = p.getBasePositionAndOrientation(
-        uid)
+      pos, _ = p.getBasePositionAndOrientation(uid)
       # If any block is above height, provide reward.
       if pos[2] > 0.2:
         self._graspSuccess += 1
@@ -319,8 +310,7 @@ class KukaDiverseObjectEnv(KukaGymEnv):
       urdf_pattern = os.path.join(self._urdfRoot, 'random_urdfs/*[1-9]/*.urdf')
     found_object_directories = glob.glob(urdf_pattern)
     total_num_objects = len(found_object_directories)
-    selected_objects = np.random.choice(np.arange(total_num_objects),
-                                        num_objects)
+    selected_objects = np.random.choice(np.arange(total_num_objects), num_objects)
     selected_objects_filenames = []
     for object_index in selected_objects:
       selected_objects_filenames += [found_object_directories[object_index]]
diff --git a/examples/pybullet/gym/pybullet_envs/bullet/minitaur.py b/examples/pybullet/gym/pybullet_envs/bullet/minitaur.py
index 046370040..3f1c50a1f 100644
--- a/examples/pybullet/gym/pybullet_envs/bullet/minitaur.py
+++ b/examples/pybullet/gym/pybullet_envs/bullet/minitaur.py
@@ -15,9 +15,8 @@ OVERHEAT_SHUTDOWN_TORQUE = 2.45
 OVERHEAT_SHUTDOWN_TIME = 1.0
 LEG_POSITION = ["front_left", "back_left", "front_right", "back_right"]
 MOTOR_NAMES = [
-    "motor_front_leftL_joint", "motor_front_leftR_joint",
-    "motor_back_leftL_joint", "motor_back_leftR_joint",
-    "motor_front_rightL_joint", "motor_front_rightR_joint",
+    "motor_front_leftL_joint", "motor_front_leftR_joint", "motor_back_leftL_joint",
+    "motor_back_leftR_joint", "motor_front_rightL_joint", "motor_front_rightR_joint",
     "motor_back_rightL_joint", "motor_back_rightR_joint"
 ]
 LEG_LINK_ID = [2, 3, 5, 6, 8, 9, 11, 12, 15, 16, 18, 19, 21, 22, 24, 25]
@@ -33,7 +32,7 @@ class Minitaur(object):
 
   def __init__(self,
                pybullet_client,
-               urdf_root= os.path.join(os.path.dirname(__file__),"../data"),
+               urdf_root=os.path.join(os.path.dirname(__file__), "../data"),
                time_step=0.01,
                self_collision_enabled=False,
                motor_velocity_limit=np.inf,
@@ -87,10 +86,9 @@ class Minitaur(object):
     if self._accurate_motor_model_enabled:
       self._kp = motor_kp
       self._kd = motor_kd
-      self._motor_model = motor.MotorModel(
-          torque_control_enabled=self._torque_control_enabled,
-          kp=self._kp,
-          kd=self._kd)
+      self._motor_model = motor.MotorModel(torque_control_enabled=self._torque_control_enabled,
+                                           kp=self._kp,
+                                           kd=self._kd)
     elif self._pd_control_enabled:
       self._kp = 8
       self._kd = kd_for_pd_controllers
@@ -101,15 +99,12 @@ class Minitaur(object):
     self.Reset()
 
   def _RecordMassInfoFromURDF(self):
-    self._base_mass_urdf = self._pybullet_client.getDynamicsInfo(
-        self.quadruped, BASE_LINK_ID)[0]
+    self._base_mass_urdf = self._pybullet_client.getDynamicsInfo(self.quadruped, BASE_LINK_ID)[0]
     self._leg_masses_urdf = []
     self._leg_masses_urdf.append(
-        self._pybullet_client.getDynamicsInfo(self.quadruped, LEG_LINK_ID[0])[
-            0])
+        self._pybullet_client.getDynamicsInfo(self.quadruped, LEG_LINK_ID[0])[0])
     self._leg_masses_urdf.append(
-        self._pybullet_client.getDynamicsInfo(self.quadruped, MOTOR_LINK_ID[0])[
-            0])
+        self._pybullet_client.getDynamicsInfo(self.quadruped, MOTOR_LINK_ID[0])[0])
 
   def _BuildJointNameToIdDict(self):
     num_joints = self._pybullet_client.getNumJoints(self.quadruped)
@@ -119,9 +114,7 @@ class Minitaur(object):
       self._joint_name_to_id[joint_info[1].decode("UTF-8")] = joint_info[0]
 
   def _BuildMotorIdList(self):
-    self._motor_id_list = [
-        self._joint_name_to_id[motor_name] for motor_name in MOTOR_NAMES
-    ]
+    self._motor_id_list = [self._joint_name_to_id[motor_name] for motor_name in MOTOR_NAMES]
 
   def Reset(self, reload_urdf=True):
     """Reset the minitaur to its initial states.
@@ -144,39 +137,35 @@ class Minitaur(object):
       self._RecordMassInfoFromURDF()
       self.ResetPose(add_constraint=True)
       if self._on_rack:
-        self._pybullet_client.createConstraint(
-            self.quadruped, -1, -1, -1, self._pybullet_client.JOINT_FIXED,
-            [0, 0, 0], [0, 0, 0], [0, 0, 1])
+        self._pybullet_client.createConstraint(self.quadruped, -1, -1, -1,
+                                               self._pybullet_client.JOINT_FIXED, [0, 0, 0],
+                                               [0, 0, 0], [0, 0, 1])
     else:
-      self._pybullet_client.resetBasePositionAndOrientation(
-          self.quadruped, INIT_POSITION, INIT_ORIENTATION)
-      self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0],
-                                              [0, 0, 0])
+      self._pybullet_client.resetBasePositionAndOrientation(self.quadruped, INIT_POSITION,
+                                                            INIT_ORIENTATION)
+      self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0], [0, 0, 0])
       self.ResetPose(add_constraint=False)
 
     self._overheat_counter = np.zeros(self.num_motors)
     self._motor_enabled_list = [True] * self.num_motors
 
   def _SetMotorTorqueById(self, motor_id, torque):
-    self._pybullet_client.setJointMotorControl2(
-        bodyIndex=self.quadruped,
-        jointIndex=motor_id,
-        controlMode=self._pybullet_client.TORQUE_CONTROL,
-        force=torque)
+    self._pybullet_client.setJointMotorControl2(bodyIndex=self.quadruped,
+                                                jointIndex=motor_id,
+                                                controlMode=self._pybullet_client.TORQUE_CONTROL,
+                                                force=torque)
 
   def _SetDesiredMotorAngleById(self, motor_id, desired_angle):
-    self._pybullet_client.setJointMotorControl2(
-        bodyIndex=self.quadruped,
-        jointIndex=motor_id,
-        controlMode=self._pybullet_client.POSITION_CONTROL,
-        targetPosition=desired_angle,
-        positionGain=self._kp,
-        velocityGain=self._kd,
-        force=self._max_force)
+    self._pybullet_client.setJointMotorControl2(bodyIndex=self.quadruped,
+                                                jointIndex=motor_id,
+                                                controlMode=self._pybullet_client.POSITION_CONTROL,
+                                                targetPosition=desired_angle,
+                                                positionGain=self._kp,
+                                                velocityGain=self._kd,
+                                                force=self._max_force)
 
   def _SetDesiredMotorAngleByName(self, motor_name, desired_angle):
-    self._SetDesiredMotorAngleById(self._joint_name_to_id[motor_name],
-                                   desired_angle)
+    self._SetDesiredMotorAngleById(self._joint_name_to_id[motor_name], desired_angle)
 
   def ResetPose(self, add_constraint):
     """Reset the pose of the minitaur.
@@ -200,59 +189,53 @@ class Minitaur(object):
     knee_angle = -2.1834
 
     leg_position = LEG_POSITION[leg_id]
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["motor_" + leg_position + "L_joint"],
-        self._motor_direction[2 * leg_id] * half_pi,
-        targetVelocity=0)
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["knee_" + leg_position + "L_link"],
-        self._motor_direction[2 * leg_id] * knee_angle,
-        targetVelocity=0)
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["motor_" + leg_position + "R_joint"],
-        self._motor_direction[2 * leg_id + 1] * half_pi,
-        targetVelocity=0)
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["knee_" + leg_position + "R_link"],
-        self._motor_direction[2 * leg_id + 1] * knee_angle,
-        targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["motor_" + leg_position +
+                                                                 "L_joint"],
+                                          self._motor_direction[2 * leg_id] * half_pi,
+                                          targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["knee_" + leg_position +
+                                                                 "L_link"],
+                                          self._motor_direction[2 * leg_id] * knee_angle,
+                                          targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["motor_" + leg_position +
+                                                                 "R_joint"],
+                                          self._motor_direction[2 * leg_id + 1] * half_pi,
+                                          targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["knee_" + leg_position +
+                                                                 "R_link"],
+                                          self._motor_direction[2 * leg_id + 1] * knee_angle,
+                                          targetVelocity=0)
     if add_constraint:
       self._pybullet_client.createConstraint(
-          self.quadruped, self._joint_name_to_id["knee_"
-                                                 + leg_position + "R_link"],
-          self.quadruped, self._joint_name_to_id["knee_"
-                                                 + leg_position + "L_link"],
-          self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0],
-          KNEE_CONSTRAINT_POINT_RIGHT, KNEE_CONSTRAINT_POINT_LEFT)
+          self.quadruped, self._joint_name_to_id["knee_" + leg_position + "R_link"],
+          self.quadruped, self._joint_name_to_id["knee_" + leg_position + "L_link"],
+          self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], KNEE_CONSTRAINT_POINT_RIGHT,
+          KNEE_CONSTRAINT_POINT_LEFT)
 
     if self._accurate_motor_model_enabled or self._pd_control_enabled:
       # Disable the default motor in pybullet.
       self._pybullet_client.setJointMotorControl2(
           bodyIndex=self.quadruped,
-          jointIndex=(self._joint_name_to_id["motor_"
-                                             + leg_position + "L_joint"]),
+          jointIndex=(self._joint_name_to_id["motor_" + leg_position + "L_joint"]),
           controlMode=self._pybullet_client.VELOCITY_CONTROL,
           targetVelocity=0,
           force=knee_friction_force)
       self._pybullet_client.setJointMotorControl2(
           bodyIndex=self.quadruped,
-          jointIndex=(self._joint_name_to_id["motor_"
-                                             + leg_position + "R_joint"]),
+          jointIndex=(self._joint_name_to_id["motor_" + leg_position + "R_joint"]),
           controlMode=self._pybullet_client.VELOCITY_CONTROL,
           targetVelocity=0,
           force=knee_friction_force)
 
     else:
-      self._SetDesiredMotorAngleByName(
-          "motor_" + leg_position + "L_joint",
-          self._motor_direction[2 * leg_id] * half_pi)
+      self._SetDesiredMotorAngleByName("motor_" + leg_position + "L_joint",
+                                       self._motor_direction[2 * leg_id] * half_pi)
       self._SetDesiredMotorAngleByName("motor_" + leg_position + "R_joint",
-                                       self._motor_direction[2 * leg_id
-                                                             + 1] * half_pi)
+                                       self._motor_direction[2 * leg_id + 1] * half_pi)
 
     self._pybullet_client.setJointMotorControl2(
         bodyIndex=self.quadruped,
@@ -273,8 +256,7 @@ class Minitaur(object):
     Returns:
       The position of minitaur's base.
     """
-    position, _ = (
-        self._pybullet_client.getBasePositionAndOrientation(self.quadruped))
+    position, _ = (self._pybullet_client.getBasePositionAndOrientation(self.quadruped))
     return position
 
   def GetBaseOrientation(self):
@@ -283,8 +265,7 @@ class Minitaur(object):
     Returns:
       The orientation of minitaur's base.
     """
-    _, orientation = (
-        self._pybullet_client.getBasePositionAndOrientation(self.quadruped))
+    _, orientation = (self._pybullet_client.getBasePositionAndOrientation(self.quadruped))
     return orientation
 
   def GetActionDimension(self):
@@ -304,10 +285,9 @@ class Minitaur(object):
     """
     upper_bound = np.array([0.0] * self.GetObservationDimension())
     upper_bound[0:self.num_motors] = math.pi  # Joint angle.
-    upper_bound[self.num_motors:2 * self.num_motors] = (
-        motor.MOTOR_SPEED_LIMIT)  # Joint velocity.
-    upper_bound[2 * self.num_motors:3 * self.num_motors] = (
-        motor.OBSERVED_TORQUE_LIMIT)  # Joint torque.
+    upper_bound[self.num_motors:2 * self.num_motors] = (motor.MOTOR_SPEED_LIMIT)  # Joint velocity.
+    upper_bound[2 * self.num_motors:3 * self.num_motors] = (motor.OBSERVED_TORQUE_LIMIT
+                                                           )  # Joint torque.
     upper_bound[3 * self.num_motors:] = 1.0  # Quaternion of base orientation.
     return upper_bound
 
@@ -354,12 +334,9 @@ class Minitaur(object):
     """
     if self._motor_velocity_limit < np.inf:
       current_motor_angle = self.GetMotorAngles()
-      motor_commands_max = (
-          current_motor_angle + self.time_step * self._motor_velocity_limit)
-      motor_commands_min = (
-          current_motor_angle - self.time_step * self._motor_velocity_limit)
-      motor_commands = np.clip(motor_commands, motor_commands_min,
-                               motor_commands_max)
+      motor_commands_max = (current_motor_angle + self.time_step * self._motor_velocity_limit)
+      motor_commands_min = (current_motor_angle - self.time_step * self._motor_velocity_limit)
+      motor_commands = np.clip(motor_commands, motor_commands_min, motor_commands_max)
 
     if self._accurate_motor_model_enabled or self._pd_control_enabled:
       q = self.GetMotorAngles()
@@ -373,8 +350,7 @@ class Minitaur(object):
               self._overheat_counter[i] += 1
             else:
               self._overheat_counter[i] = 0
-            if (self._overheat_counter[i] >
-                OVERHEAT_SHUTDOWN_TIME / self.time_step):
+            if (self._overheat_counter[i] > OVERHEAT_SHUTDOWN_TIME / self.time_step):
               self._motor_enabled_list[i] = False
 
         # The torque is already in the observation space because we use
@@ -382,12 +358,11 @@ class Minitaur(object):
         self._observed_motor_torques = observed_torque
 
         # Transform into the motor space when applying the torque.
-        self._applied_motor_torque = np.multiply(actual_torque,
-                                                 self._motor_direction)
+        self._applied_motor_torque = np.multiply(actual_torque, self._motor_direction)
 
-        for motor_id, motor_torque, motor_enabled in zip(
-            self._motor_id_list, self._applied_motor_torque,
-            self._motor_enabled_list):
+        for motor_id, motor_torque, motor_enabled in zip(self._motor_id_list,
+                                                         self._applied_motor_torque,
+                                                         self._motor_enabled_list):
           if motor_enabled:
             self._SetMotorTorqueById(motor_id, motor_torque)
           else:
@@ -403,14 +378,12 @@ class Minitaur(object):
         self._applied_motor_torques = np.multiply(self._observed_motor_torques,
                                                   self._motor_direction)
 
-        for motor_id, motor_torque in zip(self._motor_id_list,
-                                          self._applied_motor_torques):
+        for motor_id, motor_torque in zip(self._motor_id_list, self._applied_motor_torques):
           self._SetMotorTorqueById(motor_id, motor_torque)
     else:
-      motor_commands_with_direction = np.multiply(motor_commands,
-                                                  self._motor_direction)
-      for motor_id, motor_command_with_direction in zip(
-          self._motor_id_list, motor_commands_with_direction):
+      motor_commands_with_direction = np.multiply(motor_commands, self._motor_direction)
+      for motor_id, motor_command_with_direction in zip(self._motor_id_list,
+                                                        motor_commands_with_direction):
         self._SetDesiredMotorAngleById(motor_id, motor_command_with_direction)
 
   def GetMotorAngles(self):
@@ -471,13 +444,13 @@ class Minitaur(object):
     quater_pi = math.pi / 4
     for i in range(self.num_motors):
       action_idx = i // 2
-      forward_backward_component = (-scale_for_singularity * quater_pi * (
-          actions[action_idx + half_num_motors] + offset_for_singularity))
+      forward_backward_component = (
+          -scale_for_singularity * quater_pi *
+          (actions[action_idx + half_num_motors] + offset_for_singularity))
       extension_component = (-1)**i * quater_pi * actions[action_idx]
       if i >= half_num_motors:
         extension_component = -extension_component
-      motor_angle[i] = (
-          math.pi + forward_backward_component + extension_component)
+      motor_angle[i] = (math.pi + forward_backward_component + extension_component)
     return motor_angle
 
   def GetBaseMassFromURDF(self):
@@ -489,8 +462,7 @@ class Minitaur(object):
     return self._leg_masses_urdf
 
   def SetBaseMass(self, base_mass):
-    self._pybullet_client.changeDynamics(
-        self.quadruped, BASE_LINK_ID, mass=base_mass)
+    self._pybullet_client.changeDynamics(self.quadruped, BASE_LINK_ID, mass=base_mass)
 
   def SetLegMasses(self, leg_masses):
     """Set the mass of the legs.
@@ -504,11 +476,9 @@ class Minitaur(object):
         leg_masses[1] is the mass of the motor.
     """
     for link_id in LEG_LINK_ID:
-      self._pybullet_client.changeDynamics(
-          self.quadruped, link_id, mass=leg_masses[0])
+      self._pybullet_client.changeDynamics(self.quadruped, link_id, mass=leg_masses[0])
     for link_id in MOTOR_LINK_ID:
-      self._pybullet_client.changeDynamics(
-          self.quadruped, link_id, mass=leg_masses[1])
+      self._pybullet_client.changeDynamics(self.quadruped, link_id, mass=leg_masses[1])
 
   def SetFootFriction(self, foot_friction):
     """Set the lateral friction of the feet.
@@ -518,8 +488,7 @@ class Minitaur(object):
         shared by all four feet.
     """
     for link_id in FOOT_LINK_ID:
-      self._pybullet_client.changeDynamics(
-          self.quadruped, link_id, lateralFriction=foot_friction)
+      self._pybullet_client.changeDynamics(self.quadruped, link_id, lateralFriction=foot_friction)
 
   def SetBatteryVoltage(self, voltage):
     if self._accurate_motor_model_enabled:
diff --git a/examples/pybullet/gym/pybullet_envs/bullet/minitaur_duck_gym_env.py b/examples/pybullet/gym/pybullet_envs/bullet/minitaur_duck_gym_env.py
index d2fb0c859..88d605f11 100644
--- a/examples/pybullet/gym/pybullet_envs/bullet/minitaur_duck_gym_env.py
+++ b/examples/pybullet/gym/pybullet_envs/bullet/minitaur_duck_gym_env.py
@@ -6,8 +6,7 @@ import os
 import inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
-
+os.sys.path.insert(0, parentdir)
 
 import math
 import time
@@ -34,8 +33,9 @@ OBSERVATION_EPS = 0.01
 RENDER_HEIGHT = 720
 RENDER_WIDTH = 960
 
-duckStartPos = [0,0,0.25]
-duckStartOrn = [0.5,0.5,0.5,0.5]
+duckStartPos = [0, 0, 0.25]
+duckStartOrn = [0.5, 0.5, 0.5, 0.5]
+
 
 class MinitaurBulletDuckEnv(gym.Env):
   """The gym environment for the minitaur.
@@ -47,34 +47,32 @@ class MinitaurBulletDuckEnv(gym.Env):
   expenditure.
 
   """
-  metadata = {
-      "render.modes": ["human", "rgb_array"],
-      "video.frames_per_second": 50
-  }
-
-  def __init__(self,
-               urdf_root=pybullet_data.getDataPath(),
-               action_repeat=1,
-               distance_weight=1.0,
-               energy_weight=0.005,
-               shake_weight=0.0,
-               drift_weight=0.0,
-               distance_limit=float("inf"),
-               observation_noise_stdev=0.0,
-               self_collision_enabled=True,
-               motor_velocity_limit=np.inf,
-               pd_control_enabled=False,#not needed to be true if accurate motor model is enabled (has its own better PD)
-               leg_model_enabled=True,
-               accurate_motor_model_enabled=True,
-               motor_kp=1.0,
-               motor_kd=0.02,
-               torque_control_enabled=False,
-               motor_overheat_protection=True,
-               hard_reset=True,
-               on_rack=False,
-               render=False,
-               kd_for_pd_controllers=0.3,
-               env_randomizer=minitaur_env_randomizer.MinitaurEnvRandomizer()):
+  metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 50}
+
+  def __init__(
+      self,
+      urdf_root=pybullet_data.getDataPath(),
+      action_repeat=1,
+      distance_weight=1.0,
+      energy_weight=0.005,
+      shake_weight=0.0,
+      drift_weight=0.0,
+      distance_limit=float("inf"),
+      observation_noise_stdev=0.0,
+      self_collision_enabled=True,
+      motor_velocity_limit=np.inf,
+      pd_control_enabled=False,  #not needed to be true if accurate motor model is enabled (has its own better PD)
+      leg_model_enabled=True,
+      accurate_motor_model_enabled=True,
+      motor_kp=1.0,
+      motor_kd=0.02,
+      torque_control_enabled=False,
+      motor_overheat_protection=True,
+      hard_reset=True,
+      on_rack=False,
+      render=False,
+      kd_for_pd_controllers=0.3,
+      env_randomizer=minitaur_env_randomizer.MinitaurEnvRandomizer()):
     """Initialize the minitaur gym environment.
 
     Args:
@@ -152,17 +150,14 @@ class MinitaurBulletDuckEnv(gym.Env):
       self._action_repeat *= NUM_SUBSTEPS
 
     if self._is_render:
-      self._pybullet_client = bullet_client.BulletClient(
-          connection_mode=pybullet.GUI)
+      self._pybullet_client = bullet_client.BulletClient(connection_mode=pybullet.GUI)
     else:
       self._pybullet_client = bullet_client.BulletClient()
 
     self.seed()
     self.reset()
-    observation_high = (
-        self.minitaur.GetObservationUpperBound() + OBSERVATION_EPS)
-    observation_low = (
-        self.minitaur.GetObservationLowerBound() - OBSERVATION_EPS)
+    observation_high = (self.minitaur.GetObservationUpperBound() + OBSERVATION_EPS)
+    observation_low = (self.minitaur.GetObservationLowerBound() - OBSERVATION_EPS)
     action_dim = 8
     action_high = np.array([self._action_bound] * action_dim)
     self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32)
@@ -183,35 +178,36 @@ class MinitaurBulletDuckEnv(gym.Env):
           numSolverIterations=int(self._num_bullet_solver_iterations))
       self._pybullet_client.setTimeStep(self._time_step)
       self._groundId = self._pybullet_client.loadURDF("%s/plane.urdf" % self._urdf_root)
-      self._duckId = self._pybullet_client.loadURDF("%s/duck_vhacd.urdf" % self._urdf_root,duckStartPos,duckStartOrn)
+      self._duckId = self._pybullet_client.loadURDF("%s/duck_vhacd.urdf" % self._urdf_root,
+                                                    duckStartPos, duckStartOrn)
       self._pybullet_client.setGravity(0, 0, -10)
       acc_motor = self._accurate_motor_model_enabled
       motor_protect = self._motor_overheat_protection
-      self.minitaur = (minitaur.Minitaur(
-          pybullet_client=self._pybullet_client,
-          urdf_root=self._urdf_root,
-          time_step=self._time_step,
-          self_collision_enabled=self._self_collision_enabled,
-          motor_velocity_limit=self._motor_velocity_limit,
-          pd_control_enabled=self._pd_control_enabled,
-          accurate_motor_model_enabled=acc_motor,
-          motor_kp=self._motor_kp,
-          motor_kd=self._motor_kd,
-          torque_control_enabled=self._torque_control_enabled,
-          motor_overheat_protection=motor_protect,
-          on_rack=self._on_rack,
-          kd_for_pd_controllers=self._kd_for_pd_controllers))
+      self.minitaur = (minitaur.Minitaur(pybullet_client=self._pybullet_client,
+                                         urdf_root=self._urdf_root,
+                                         time_step=self._time_step,
+                                         self_collision_enabled=self._self_collision_enabled,
+                                         motor_velocity_limit=self._motor_velocity_limit,
+                                         pd_control_enabled=self._pd_control_enabled,
+                                         accurate_motor_model_enabled=acc_motor,
+                                         motor_kp=self._motor_kp,
+                                         motor_kd=self._motor_kd,
+                                         torque_control_enabled=self._torque_control_enabled,
+                                         motor_overheat_protection=motor_protect,
+                                         on_rack=self._on_rack,
+                                         kd_for_pd_controllers=self._kd_for_pd_controllers))
     else:
       self.minitaur.Reset(reload_urdf=False)
-      self._pybullet_client.resetBasePositionAndOrientation(self._duckId,duckStartPos,duckStartOrn)
+      self._pybullet_client.resetBasePositionAndOrientation(self._duckId, duckStartPos,
+                                                            duckStartOrn)
     if self._env_randomizer is not None:
       self._env_randomizer.randomize_env(self)
 
     self._env_step_counter = 0
     self._last_base_position = [0, 0, 0]
     self._objectives = []
-    self._pybullet_client.resetDebugVisualizerCamera(
-        self._cam_dist, self._cam_yaw, self._cam_pitch, [0, 0, 0])
+    self._pybullet_client.resetDebugVisualizerCamera(self._cam_dist, self._cam_yaw,
+                                                     self._cam_pitch, [0, 0, 0])
     if not self._torque_control_enabled:
       for _ in range(100):
         if self._pd_control_enabled or self._accurate_motor_model_enabled:
@@ -228,8 +224,7 @@ class MinitaurBulletDuckEnv(gym.Env):
       for i, action_component in enumerate(action):
         if not (-self._action_bound - ACTION_EPS <= action_component <=
                 self._action_bound + ACTION_EPS):
-          raise ValueError(
-              "{}th action {} out of bounds.".format(i, action_component))
+          raise ValueError("{}th action {} out of bounds.".format(i, action_component))
       action = self.minitaur.ConvertFromLegModel(action)
     return action
 
@@ -258,8 +253,8 @@ class MinitaurBulletDuckEnv(gym.Env):
       if time_to_sleep > 0:
         time.sleep(time_to_sleep)
       base_pos = self.minitaur.GetBasePosition()
-      self._pybullet_client.resetDebugVisualizerCamera(
-          self._cam_dist, self._cam_yaw, self._cam_pitch, base_pos)
+      self._pybullet_client.resetDebugVisualizerCamera(self._cam_dist, self._cam_yaw,
+                                                       self._cam_pitch, base_pos)
     action = self._transform_action_to_motor_command(action)
     for _ in range(self._action_repeat):
       self.minitaur.ApplyAction(action)
@@ -281,12 +276,17 @@ class MinitaurBulletDuckEnv(gym.Env):
         pitch=self._cam_pitch,
         roll=0,
         upAxisIndex=2)
-    proj_matrix = self._pybullet_client.computeProjectionMatrixFOV(
-        fov=60, aspect=float(RENDER_WIDTH)/RENDER_HEIGHT,
-        nearVal=0.1, farVal=100.0)
-    (_, _, px, _, _) = self._pybullet_client.getCameraImage(
-        width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix,
-        projectionMatrix=proj_matrix, renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
+    proj_matrix = self._pybullet_client.computeProjectionMatrixFOV(fov=60,
+                                                                   aspect=float(RENDER_WIDTH) /
+                                                                   RENDER_HEIGHT,
+                                                                   nearVal=0.1,
+                                                                   farVal=100.0)
+    (_, _, px, _,
+     _) = self._pybullet_client.getCameraImage(width=RENDER_WIDTH,
+                                               height=RENDER_HEIGHT,
+                                               viewMatrix=view_matrix,
+                                               projectionMatrix=proj_matrix,
+                                               renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
     rgb_array = np.array(px)
     rgb_array = rgb_array[:, :, :3]
     return rgb_array
@@ -297,9 +297,8 @@ class MinitaurBulletDuckEnv(gym.Env):
     Returns:
       A numpy array of motor angles.
     """
-    return np.array(
-        self._observation[MOTOR_ANGLE_OBSERVATION_INDEX:
-                          MOTOR_ANGLE_OBSERVATION_INDEX + NUM_MOTORS])
+    return np.array(self._observation[MOTOR_ANGLE_OBSERVATION_INDEX:MOTOR_ANGLE_OBSERVATION_INDEX +
+                                      NUM_MOTORS])
 
   def get_minitaur_motor_velocities(self):
     """Get the minitaur's motor velocities.
@@ -308,8 +307,8 @@ class MinitaurBulletDuckEnv(gym.Env):
       A numpy array of motor velocities.
     """
     return np.array(
-        self._observation[MOTOR_VELOCITY_OBSERVATION_INDEX:
-                          MOTOR_VELOCITY_OBSERVATION_INDEX + NUM_MOTORS])
+        self._observation[MOTOR_VELOCITY_OBSERVATION_INDEX:MOTOR_VELOCITY_OBSERVATION_INDEX +
+                          NUM_MOTORS])
 
   def get_minitaur_motor_torques(self):
     """Get the minitaur's motor torques.
@@ -318,8 +317,8 @@ class MinitaurBulletDuckEnv(gym.Env):
       A numpy array of motor torques.
     """
     return np.array(
-        self._observation[MOTOR_TORQUE_OBSERVATION_INDEX:
-                          MOTOR_TORQUE_OBSERVATION_INDEX + NUM_MOTORS])
+        self._observation[MOTOR_TORQUE_OBSERVATION_INDEX:MOTOR_TORQUE_OBSERVATION_INDEX +
+                          NUM_MOTORS])
 
   def get_minitaur_base_orientation(self):
     """Get the minitaur's base orientation, represented by a quaternion.
@@ -330,8 +329,8 @@ class MinitaurBulletDuckEnv(gym.Env):
     return np.array(self._observation[BASE_ORIENTATION_OBSERVATION_INDEX:])
 
   def lost_duck(self):
-    points = self._pybullet_client.getContactPoints(self._duckId, self._groundId);
-    return len(points)>0
+    points = self._pybullet_client.getContactPoints(self._duckId, self._groundId)
+    return len(points) > 0
 
   def is_fallen(self):
     """Decide whether the minitaur has fallen.
@@ -347,8 +346,7 @@ class MinitaurBulletDuckEnv(gym.Env):
     rot_mat = self._pybullet_client.getMatrixFromQuaternion(orientation)
     local_up = rot_mat[6:]
     pos = self.minitaur.GetBasePosition()
-    return (np.dot(np.asarray([0, 0, 1]), np.asarray(local_up)) < 0.85 or
-            pos[2] < 0.13)
+    return (np.dot(np.asarray([0, 0, 1]), np.asarray(local_up)) < 0.85 or pos[2] < 0.13)
 
   def _termination(self):
     position = self.minitaur.GetBasePosition()
@@ -364,12 +362,9 @@ class MinitaurBulletDuckEnv(gym.Env):
     energy_reward = np.abs(
         np.dot(self.minitaur.GetMotorTorques(),
                self.minitaur.GetMotorVelocities())) * self._time_step
-    reward = (
-        self._distance_weight * forward_reward -
-        self._energy_weight * energy_reward + self._drift_weight * drift_reward
-        + self._shake_weight * shake_reward)
-    self._objectives.append(
-        [forward_reward, energy_reward, drift_reward, shake_reward])
+    reward = (self._distance_weight * forward_reward - self._energy_weight * energy_reward +
+              self._drift_weight * drift_reward + self._shake_weight * shake_reward)
+    self._objectives.append([forward_reward, energy_reward, drift_reward, shake_reward])
     return reward
 
   def get_objectives(self):
@@ -383,9 +378,9 @@ class MinitaurBulletDuckEnv(gym.Env):
     self._get_observation()
     observation = np.array(self._observation)
     if self._observation_noise_stdev > 0:
-      observation += (np.random.normal(
-          scale=self._observation_noise_stdev, size=observation.shape) *
-                      self.minitaur.GetObservationUpperBound())
+      observation += (
+          np.random.normal(scale=self._observation_noise_stdev, size=observation.shape) *
+          self.minitaur.GetObservationUpperBound())
     return observation
 
   if parse_version(gym.__version__) < parse_version('0.9.6'):
diff --git a/examples/pybullet/gym/pybullet_envs/bullet/minitaur_env_randomizer.py b/examples/pybullet/gym/pybullet_envs/bullet/minitaur_env_randomizer.py
index db139f3b0..cf2646683 100644
--- a/examples/pybullet/gym/pybullet_envs/bullet/minitaur_env_randomizer.py
+++ b/examples/pybullet/gym/pybullet_envs/bullet/minitaur_env_randomizer.py
@@ -45,24 +45,20 @@ class MinitaurEnvRandomizer(env_randomizer_base.EnvRandomizerBase):
     minitaur.SetBaseMass(randomized_base_mass)
 
     leg_masses = minitaur.GetLegMassesFromURDF()
-    leg_masses_lower_bound = np.array(leg_masses) * (
-        1.0 + self._minitaur_leg_mass_err_range[0])
-    leg_masses_upper_bound = np.array(leg_masses) * (
-        1.0 + self._minitaur_leg_mass_err_range[1])
+    leg_masses_lower_bound = np.array(leg_masses) * (1.0 + self._minitaur_leg_mass_err_range[0])
+    leg_masses_upper_bound = np.array(leg_masses) * (1.0 + self._minitaur_leg_mass_err_range[1])
     randomized_leg_masses = [
         np.random.uniform(leg_masses_lower_bound[i], leg_masses_upper_bound[i])
         for i in range(len(leg_masses))
     ]
     minitaur.SetLegMasses(randomized_leg_masses)
 
-    randomized_battery_voltage = random.uniform(BATTERY_VOLTAGE_RANGE[0],
-                                                BATTERY_VOLTAGE_RANGE[1])
+    randomized_battery_voltage = random.uniform(BATTERY_VOLTAGE_RANGE[0], BATTERY_VOLTAGE_RANGE[1])
     minitaur.SetBatteryVoltage(randomized_battery_voltage)
 
     randomized_motor_damping = random.uniform(MOTOR_VISCOUS_DAMPING_RANGE[0],
                                               MOTOR_VISCOUS_DAMPING_RANGE[1])
     minitaur.SetMotorViscousDamping(randomized_motor_damping)
 
-    randomized_foot_friction = random.uniform(MINITAUR_LEG_FRICTION[0],
-                                              MINITAUR_LEG_FRICTION[1])
+    randomized_foot_friction = random.uniform(MINITAUR_LEG_FRICTION[0], MINITAUR_LEG_FRICTION[1])
     minitaur.SetFootFriction(randomized_foot_friction)
diff --git a/examples/pybullet/gym/pybullet_envs/bullet/minitaur_gym_env.py b/examples/pybullet/gym/pybullet_envs/bullet/minitaur_gym_env.py
index 030840640..7a595b47d 100644
--- a/examples/pybullet/gym/pybullet_envs/bullet/minitaur_gym_env.py
+++ b/examples/pybullet/gym/pybullet_envs/bullet/minitaur_gym_env.py
@@ -5,8 +5,7 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
-
+os.sys.path.insert(0, parentdir)
 
 import math
 import time
@@ -33,6 +32,7 @@ OBSERVATION_EPS = 0.01
 RENDER_HEIGHT = 720
 RENDER_WIDTH = 960
 
+
 class MinitaurBulletEnv(gym.Env):
   """The gym environment for the minitaur.
 
@@ -43,34 +43,32 @@ class MinitaurBulletEnv(gym.Env):
   expenditure.
 
   """
-  metadata = {
-      "render.modes": ["human", "rgb_array"],
-      "video.frames_per_second": 50
-  }
-
-  def __init__(self,
-               urdf_root=pybullet_data.getDataPath(),
-               action_repeat=1,
-               distance_weight=1.0,
-               energy_weight=0.005,
-               shake_weight=0.0,
-               drift_weight=0.0,
-               distance_limit=float("inf"),
-               observation_noise_stdev=0.0,
-               self_collision_enabled=True,
-               motor_velocity_limit=np.inf,
-               pd_control_enabled=False,#not needed to be true if accurate motor model is enabled (has its own better PD)
-               leg_model_enabled=True,
-               accurate_motor_model_enabled=True,
-               motor_kp=1.0,
-               motor_kd=0.02,
-               torque_control_enabled=False,
-               motor_overheat_protection=True,
-               hard_reset=True,
-               on_rack=False,
-               render=False,
-               kd_for_pd_controllers=0.3,
-               env_randomizer=minitaur_env_randomizer.MinitaurEnvRandomizer()):
+  metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 50}
+
+  def __init__(
+      self,
+      urdf_root=pybullet_data.getDataPath(),
+      action_repeat=1,
+      distance_weight=1.0,
+      energy_weight=0.005,
+      shake_weight=0.0,
+      drift_weight=0.0,
+      distance_limit=float("inf"),
+      observation_noise_stdev=0.0,
+      self_collision_enabled=True,
+      motor_velocity_limit=np.inf,
+      pd_control_enabled=False,  #not needed to be true if accurate motor model is enabled (has its own better PD)
+      leg_model_enabled=True,
+      accurate_motor_model_enabled=True,
+      motor_kp=1.0,
+      motor_kd=0.02,
+      torque_control_enabled=False,
+      motor_overheat_protection=True,
+      hard_reset=True,
+      on_rack=False,
+      render=False,
+      kd_for_pd_controllers=0.3,
+      env_randomizer=minitaur_env_randomizer.MinitaurEnvRandomizer()):
     """Initialize the minitaur gym environment.
 
     Args:
@@ -147,17 +145,14 @@ class MinitaurBulletEnv(gym.Env):
       self._action_repeat *= NUM_SUBSTEPS
 
     if self._is_render:
-      self._pybullet_client = bullet_client.BulletClient(
-          connection_mode=pybullet.GUI)
+      self._pybullet_client = bullet_client.BulletClient(connection_mode=pybullet.GUI)
     else:
       self._pybullet_client = bullet_client.BulletClient()
 
     self.seed()
     self.reset()
-    observation_high = (
-        self.minitaur.GetObservationUpperBound() + OBSERVATION_EPS)
-    observation_low = (
-        self.minitaur.GetObservationLowerBound() - OBSERVATION_EPS)
+    observation_high = (self.minitaur.GetObservationUpperBound() + OBSERVATION_EPS)
+    observation_low = (self.minitaur.GetObservationLowerBound() - OBSERVATION_EPS)
     action_dim = 8
     action_high = np.array([self._action_bound] * action_dim)
     self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32)
@@ -178,25 +173,25 @@ class MinitaurBulletEnv(gym.Env):
           numSolverIterations=int(self._num_bullet_solver_iterations))
       self._pybullet_client.setTimeStep(self._time_step)
       plane = self._pybullet_client.loadURDF("%s/plane.urdf" % self._urdf_root)
-      self._pybullet_client.changeVisualShape(plane,-1,rgbaColor=[1,1,1,0.9])
-      self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_PLANAR_REFLECTION,0)
+      self._pybullet_client.changeVisualShape(plane, -1, rgbaColor=[1, 1, 1, 0.9])
+      self._pybullet_client.configureDebugVisualizer(
+          self._pybullet_client.COV_ENABLE_PLANAR_REFLECTION, 0)
       self._pybullet_client.setGravity(0, 0, -10)
       acc_motor = self._accurate_motor_model_enabled
       motor_protect = self._motor_overheat_protection
-      self.minitaur = (minitaur.Minitaur(
-          pybullet_client=self._pybullet_client,
-          urdf_root=self._urdf_root,
-          time_step=self._time_step,
-          self_collision_enabled=self._self_collision_enabled,
-          motor_velocity_limit=self._motor_velocity_limit,
-          pd_control_enabled=self._pd_control_enabled,
-          accurate_motor_model_enabled=acc_motor,
-          motor_kp=self._motor_kp,
-          motor_kd=self._motor_kd,
-          torque_control_enabled=self._torque_control_enabled,
-          motor_overheat_protection=motor_protect,
-          on_rack=self._on_rack,
-          kd_for_pd_controllers=self._kd_for_pd_controllers))
+      self.minitaur = (minitaur.Minitaur(pybullet_client=self._pybullet_client,
+                                         urdf_root=self._urdf_root,
+                                         time_step=self._time_step,
+                                         self_collision_enabled=self._self_collision_enabled,
+                                         motor_velocity_limit=self._motor_velocity_limit,
+                                         pd_control_enabled=self._pd_control_enabled,
+                                         accurate_motor_model_enabled=acc_motor,
+                                         motor_kp=self._motor_kp,
+                                         motor_kd=self._motor_kd,
+                                         torque_control_enabled=self._torque_control_enabled,
+                                         motor_overheat_protection=motor_protect,
+                                         on_rack=self._on_rack,
+                                         kd_for_pd_controllers=self._kd_for_pd_controllers))
     else:
       self.minitaur.Reset(reload_urdf=False)
 
@@ -206,8 +201,8 @@ class MinitaurBulletEnv(gym.Env):
     self._env_step_counter = 0
     self._last_base_position = [0, 0, 0]
     self._objectives = []
-    self._pybullet_client.resetDebugVisualizerCamera(
-        self._cam_dist, self._cam_yaw, self._cam_pitch, [0, 0, 0])
+    self._pybullet_client.resetDebugVisualizerCamera(self._cam_dist, self._cam_yaw,
+                                                     self._cam_pitch, [0, 0, 0])
     if not self._torque_control_enabled:
       for _ in range(100):
         if self._pd_control_enabled or self._accurate_motor_model_enabled:
@@ -224,8 +219,7 @@ class MinitaurBulletEnv(gym.Env):
       for i, action_component in enumerate(action):
         if not (-self._action_bound - ACTION_EPS <= action_component <=
                 self._action_bound + ACTION_EPS):
-          raise ValueError(
-              "{}th action {} out of bounds.".format(i, action_component))
+          raise ValueError("{}th action {} out of bounds.".format(i, action_component))
       action = self.minitaur.ConvertFromLegModel(action)
     return action
 
@@ -256,14 +250,15 @@ class MinitaurBulletEnv(gym.Env):
       base_pos = self.minitaur.GetBasePosition()
       camInfo = self._pybullet_client.getDebugVisualizerCamera()
       curTargetPos = camInfo[11]
-      distance=camInfo[10]
+      distance = camInfo[10]
       yaw = camInfo[8]
-      pitch=camInfo[9]
-      targetPos = [0.95*curTargetPos[0]+0.05*base_pos[0],0.95*curTargetPos[1]+0.05*base_pos[1],curTargetPos[2]]
+      pitch = camInfo[9]
+      targetPos = [
+          0.95 * curTargetPos[0] + 0.05 * base_pos[0], 0.95 * curTargetPos[1] + 0.05 * base_pos[1],
+          curTargetPos[2]
+      ]
 
-
-      self._pybullet_client.resetDebugVisualizerCamera(
-          distance, yaw, pitch, base_pos)
+      self._pybullet_client.resetDebugVisualizerCamera(distance, yaw, pitch, base_pos)
     action = self._transform_action_to_motor_command(action)
     for _ in range(self._action_repeat):
       self.minitaur.ApplyAction(action)
@@ -285,12 +280,17 @@ class MinitaurBulletEnv(gym.Env):
         pitch=self._cam_pitch,
         roll=0,
         upAxisIndex=2)
-    proj_matrix = self._pybullet_client.computeProjectionMatrixFOV(
-        fov=60, aspect=float(RENDER_WIDTH)/RENDER_HEIGHT,
-        nearVal=0.1, farVal=100.0)
-    (_, _, px, _, _) = self._pybullet_client.getCameraImage(
-        width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix,
-        projectionMatrix=proj_matrix, renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
+    proj_matrix = self._pybullet_client.computeProjectionMatrixFOV(fov=60,
+                                                                   aspect=float(RENDER_WIDTH) /
+                                                                   RENDER_HEIGHT,
+                                                                   nearVal=0.1,
+                                                                   farVal=100.0)
+    (_, _, px, _,
+     _) = self._pybullet_client.getCameraImage(width=RENDER_WIDTH,
+                                               height=RENDER_HEIGHT,
+                                               viewMatrix=view_matrix,
+                                               projectionMatrix=proj_matrix,
+                                               renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
     rgb_array = np.array(px)
     rgb_array = rgb_array[:, :, :3]
     return rgb_array
@@ -301,9 +301,8 @@ class MinitaurBulletEnv(gym.Env):
     Returns:
       A numpy array of motor angles.
     """
-    return np.array(
-        self._observation[MOTOR_ANGLE_OBSERVATION_INDEX:
-                          MOTOR_ANGLE_OBSERVATION_INDEX + NUM_MOTORS])
+    return np.array(self._observation[MOTOR_ANGLE_OBSERVATION_INDEX:MOTOR_ANGLE_OBSERVATION_INDEX +
+                                      NUM_MOTORS])
 
   def get_minitaur_motor_velocities(self):
     """Get the minitaur's motor velocities.
@@ -312,8 +311,8 @@ class MinitaurBulletEnv(gym.Env):
       A numpy array of motor velocities.
     """
     return np.array(
-        self._observation[MOTOR_VELOCITY_OBSERVATION_INDEX:
-                          MOTOR_VELOCITY_OBSERVATION_INDEX + NUM_MOTORS])
+        self._observation[MOTOR_VELOCITY_OBSERVATION_INDEX:MOTOR_VELOCITY_OBSERVATION_INDEX +
+                          NUM_MOTORS])
 
   def get_minitaur_motor_torques(self):
     """Get the minitaur's motor torques.
@@ -322,8 +321,8 @@ class MinitaurBulletEnv(gym.Env):
       A numpy array of motor torques.
     """
     return np.array(
-        self._observation[MOTOR_TORQUE_OBSERVATION_INDEX:
-                          MOTOR_TORQUE_OBSERVATION_INDEX + NUM_MOTORS])
+        self._observation[MOTOR_TORQUE_OBSERVATION_INDEX:MOTOR_TORQUE_OBSERVATION_INDEX +
+                          NUM_MOTORS])
 
   def get_minitaur_base_orientation(self):
     """Get the minitaur's base orientation, represented by a quaternion.
@@ -347,8 +346,7 @@ class MinitaurBulletEnv(gym.Env):
     rot_mat = self._pybullet_client.getMatrixFromQuaternion(orientation)
     local_up = rot_mat[6:]
     pos = self.minitaur.GetBasePosition()
-    return (np.dot(np.asarray([0, 0, 1]), np.asarray(local_up)) < 0.85 or
-            pos[2] < 0.13)
+    return (np.dot(np.asarray([0, 0, 1]), np.asarray(local_up)) < 0.85 or pos[2] < 0.13)
 
   def _termination(self):
     position = self.minitaur.GetBasePosition()
@@ -364,12 +362,9 @@ class MinitaurBulletEnv(gym.Env):
     energy_reward = np.abs(
         np.dot(self.minitaur.GetMotorTorques(),
                self.minitaur.GetMotorVelocities())) * self._time_step
-    reward = (
-        self._distance_weight * forward_reward -
-        self._energy_weight * energy_reward + self._drift_weight * drift_reward
-        + self._shake_weight * shake_reward)
-    self._objectives.append(
-        [forward_reward, energy_reward, drift_reward, shake_reward])
+    reward = (self._distance_weight * forward_reward - self._energy_weight * energy_reward +
+              self._drift_weight * drift_reward + self._shake_weight * shake_reward)
+    self._objectives.append([forward_reward, energy_reward, drift_reward, shake_reward])
     return reward
 
   def get_objectives(self):
@@ -383,9 +378,9 @@ class MinitaurBulletEnv(gym.Env):
     self._get_observation()
     observation = np.array(self._observation)
     if self._observation_noise_stdev > 0:
-      observation += (np.random.normal(
-          scale=self._observation_noise_stdev, size=observation.shape) *
-                      self.minitaur.GetObservationUpperBound())
+      observation += (
+          np.random.normal(scale=self._observation_noise_stdev, size=observation.shape) *
+          self.minitaur.GetObservationUpperBound())
     return observation
 
   if parse_version(gym.__version__) < parse_version('0.9.6'):
diff --git a/examples/pybullet/gym/pybullet_envs/bullet/motor.py b/examples/pybullet/gym/pybullet_envs/bullet/motor.py
index fa6b4f2da..db048c487 100644
--- a/examples/pybullet/gym/pybullet_envs/bullet/motor.py
+++ b/examples/pybullet/gym/pybullet_envs/bullet/motor.py
@@ -7,8 +7,7 @@ MOTOR_VOLTAGE = 16.0
 MOTOR_RESISTANCE = 0.186
 MOTOR_TORQUE_CONSTANT = 0.0954
 MOTOR_VISCOUS_DAMPING = 0
-MOTOR_SPEED_LIMIT = MOTOR_VOLTAGE / (MOTOR_VISCOUS_DAMPING
-                                     + MOTOR_TORQUE_CONSTANT)
+MOTOR_SPEED_LIMIT = MOTOR_VOLTAGE / (MOTOR_VISCOUS_DAMPING + MOTOR_TORQUE_CONSTANT)
 
 
 class MotorModel(object):
@@ -24,10 +23,7 @@ class MotorModel(object):
   pd gains, viscous friction, back-EMF voltage and current-torque profile.
   """
 
-  def __init__(self,
-               torque_control_enabled=False,
-               kp=1.2,
-               kd=0):
+  def __init__(self, torque_control_enabled=False, kp=1.2, kd=0):
     self._torque_control_enabled = torque_control_enabled
     self._kp = kp
     self._kd = kd
@@ -50,8 +46,7 @@ class MotorModel(object):
   def get_viscous_dampling(self):
     return self._viscous_damping
 
-  def convert_to_torque(self, motor_commands, current_motor_angle,
-                        current_motor_velocity):
+  def convert_to_torque(self, motor_commands, current_motor_angle, current_motor_velocity):
     """Convert the commands (position control or torque control) to torque.
 
     Args:
@@ -66,8 +61,8 @@ class MotorModel(object):
     if self._torque_control_enabled:
       pwm = motor_commands
     else:
-      pwm = (-self._kp * (current_motor_angle - motor_commands)
-             - self._kd * current_motor_velocity)
+      pwm = (-self._kp * (current_motor_angle - motor_commands) -
+             self._kd * current_motor_velocity)
     pwm = np.clip(pwm, -1.0, 1.0)
     return self._convert_to_torque_from_pwm(pwm, current_motor_velocity)
 
@@ -81,21 +76,19 @@ class MotorModel(object):
       actual_torque: The torque that needs to be applied to the motor.
       observed_torque: The torque observed by the sensor.
     """
-    observed_torque = np.clip(
-        self._torque_constant * (pwm * self._voltage / self._resistance),
-        -OBSERVED_TORQUE_LIMIT, OBSERVED_TORQUE_LIMIT)
+    observed_torque = np.clip(self._torque_constant * (pwm * self._voltage / self._resistance),
+                              -OBSERVED_TORQUE_LIMIT, OBSERVED_TORQUE_LIMIT)
 
     # Net voltage is clipped at 50V by diodes on the motor controller.
-    voltage_net = np.clip(pwm * self._voltage -
-                          (self._torque_constant + self._viscous_damping)
-                          * current_motor_velocity,
-                          -VOLTAGE_CLIPPING, VOLTAGE_CLIPPING)
+    voltage_net = np.clip(
+        pwm * self._voltage -
+        (self._torque_constant + self._viscous_damping) * current_motor_velocity,
+        -VOLTAGE_CLIPPING, VOLTAGE_CLIPPING)
     current = voltage_net / self._resistance
     current_sign = np.sign(current)
     current_magnitude = np.absolute(current)
 
     # Saturate torque based on empirical current relation.
-    actual_torque = np.interp(current_magnitude, self._current_table,
-                              self._torque_table)
+    actual_torque = np.interp(current_magnitude, self._current_table, self._torque_table)
     actual_torque = np.multiply(current_sign, actual_torque)
     return actual_torque, observed_torque
diff --git a/examples/pybullet/gym/pybullet_envs/bullet/racecar.py b/examples/pybullet/gym/pybullet_envs/bullet/racecar.py
index c4a47208a..7c1908f4f 100644
--- a/examples/pybullet/gym/pybullet_envs/bullet/racecar.py
+++ b/examples/pybullet/gym/pybullet_envs/bullet/racecar.py
@@ -4,80 +4,150 @@ import math
 
 import numpy as np
 
+
 class Racecar:
 
-	def __init__(self, bullet_client, urdfRootPath='', timeStep=0.01):
-		self.urdfRootPath = urdfRootPath
-		self.timeStep = timeStep
-		self._p = bullet_client
-		self.reset()
-
-	def reset(self):
-		car = self._p.loadURDF(os.path.join(self.urdfRootPath,"racecar/racecar_differential.urdf"), [0,0,.2],useFixedBase=False)
-		self.racecarUniqueId = car
-		#for i in range (self._p.getNumJoints(car)):
-		#	print (self._p.getJointInfo(car,i))
-		for wheel in range(self._p.getNumJoints(car)):
-				self._p.setJointMotorControl2(car,wheel,self._p.VELOCITY_CONTROL,targetVelocity=0,force=0)
-				self._p.getJointInfo(car,wheel)
-
-		#self._p.setJointMotorControl2(car,10,self._p.VELOCITY_CONTROL,targetVelocity=1,force=10)
-		c = self._p.createConstraint(car,9,car,11,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-		self._p.changeConstraint(c,gearRatio=1, maxForce=10000)
-
-		c = self._p.createConstraint(car,10,car,13,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-		self._p.changeConstraint(c,gearRatio=-1, maxForce=10000)
-
-		c = self._p.createConstraint(car,9,car,13,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-		self._p.changeConstraint(c,gearRatio=-1, maxForce=10000)
-
-		c = self._p.createConstraint(car,16,car,18,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-		self._p.changeConstraint(c,gearRatio=1, maxForce=10000)
-
-		c = self._p.createConstraint(car,16,car,19,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-		self._p.changeConstraint(c,gearRatio=-1, maxForce=10000)
-
-		c = self._p.createConstraint(car,17,car,19,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-		self._p.changeConstraint(c,gearRatio=-1, maxForce=10000)
-
-		c = self._p.createConstraint(car,1,car,18,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-		self._p.changeConstraint(c,gearRatio=-1, gearAuxLink = 15, maxForce=10000)
-		c = self._p.createConstraint(car,3,car,19,jointType=self._p.JOINT_GEAR,jointAxis =[0,1,0],parentFramePosition=[0,0,0],childFramePosition=[0,0,0])
-		self._p.changeConstraint(c,gearRatio=-1, gearAuxLink = 15,maxForce=10000)
-
-		self.steeringLinks = [0,2]
-		self.maxForce = 20
-		self.nMotors = 2
-		self.motorizedwheels=[8,15]
-		self.speedMultiplier = 20.
-		self.steeringMultiplier = 0.5
-
-	def getActionDimension(self):
-		return self.nMotors
-
-	def getObservationDimension(self):
-		return len(self.getObservation())
-
-	def getObservation(self):
-		observation = []
-		pos,orn=self._p.getBasePositionAndOrientation(self.racecarUniqueId)
-
-		observation.extend(list(pos))
-		observation.extend(list(orn))
-
-		return observation
-
-	def applyAction(self, motorCommands):
-		targetVelocity=motorCommands[0]*self.speedMultiplier
-		#print("targetVelocity")
-		#print(targetVelocity)
-		steeringAngle = motorCommands[1]*self.steeringMultiplier
-		#print("steeringAngle")
-		#print(steeringAngle)
-		#print("maxForce")
-		#print(self.maxForce)
-
-		for motor in self.motorizedwheels:
-			self._p.setJointMotorControl2(self.racecarUniqueId,motor,self._p.VELOCITY_CONTROL,targetVelocity=targetVelocity,force=self.maxForce)
-		for steer in self.steeringLinks:
-			self._p.setJointMotorControl2(self.racecarUniqueId,steer,self._p.POSITION_CONTROL,targetPosition=steeringAngle)
+  def __init__(self, bullet_client, urdfRootPath='', timeStep=0.01):
+    self.urdfRootPath = urdfRootPath
+    self.timeStep = timeStep
+    self._p = bullet_client
+    self.reset()
+
+  def reset(self):
+    car = self._p.loadURDF(os.path.join(self.urdfRootPath, "racecar/racecar_differential.urdf"),
+                           [0, 0, .2],
+                           useFixedBase=False)
+    self.racecarUniqueId = car
+    #for i in range (self._p.getNumJoints(car)):
+    #	print (self._p.getJointInfo(car,i))
+    for wheel in range(self._p.getNumJoints(car)):
+      self._p.setJointMotorControl2(car,
+                                    wheel,
+                                    self._p.VELOCITY_CONTROL,
+                                    targetVelocity=0,
+                                    force=0)
+      self._p.getJointInfo(car, wheel)
+
+    #self._p.setJointMotorControl2(car,10,self._p.VELOCITY_CONTROL,targetVelocity=1,force=10)
+    c = self._p.createConstraint(car,
+                                 9,
+                                 car,
+                                 11,
+                                 jointType=self._p.JOINT_GEAR,
+                                 jointAxis=[0, 1, 0],
+                                 parentFramePosition=[0, 0, 0],
+                                 childFramePosition=[0, 0, 0])
+    self._p.changeConstraint(c, gearRatio=1, maxForce=10000)
+
+    c = self._p.createConstraint(car,
+                                 10,
+                                 car,
+                                 13,
+                                 jointType=self._p.JOINT_GEAR,
+                                 jointAxis=[0, 1, 0],
+                                 parentFramePosition=[0, 0, 0],
+                                 childFramePosition=[0, 0, 0])
+    self._p.changeConstraint(c, gearRatio=-1, maxForce=10000)
+
+    c = self._p.createConstraint(car,
+                                 9,
+                                 car,
+                                 13,
+                                 jointType=self._p.JOINT_GEAR,
+                                 jointAxis=[0, 1, 0],
+                                 parentFramePosition=[0, 0, 0],
+                                 childFramePosition=[0, 0, 0])
+    self._p.changeConstraint(c, gearRatio=-1, maxForce=10000)
+
+    c = self._p.createConstraint(car,
+                                 16,
+                                 car,
+                                 18,
+                                 jointType=self._p.JOINT_GEAR,
+                                 jointAxis=[0, 1, 0],
+                                 parentFramePosition=[0, 0, 0],
+                                 childFramePosition=[0, 0, 0])
+    self._p.changeConstraint(c, gearRatio=1, maxForce=10000)
+
+    c = self._p.createConstraint(car,
+                                 16,
+                                 car,
+                                 19,
+                                 jointType=self._p.JOINT_GEAR,
+                                 jointAxis=[0, 1, 0],
+                                 parentFramePosition=[0, 0, 0],
+                                 childFramePosition=[0, 0, 0])
+    self._p.changeConstraint(c, gearRatio=-1, maxForce=10000)
+
+    c = self._p.createConstraint(car,
+                                 17,
+                                 car,
+                                 19,
+                                 jointType=self._p.JOINT_GEAR,
+                                 jointAxis=[0, 1, 0],
+                                 parentFramePosition=[0, 0, 0],
+                                 childFramePosition=[0, 0, 0])
+    self._p.changeConstraint(c, gearRatio=-1, maxForce=10000)
+
+    c = self._p.createConstraint(car,
+                                 1,
+                                 car,
+                                 18,
+                                 jointType=self._p.JOINT_GEAR,
+                                 jointAxis=[0, 1, 0],
+                                 parentFramePosition=[0, 0, 0],
+                                 childFramePosition=[0, 0, 0])
+    self._p.changeConstraint(c, gearRatio=-1, gearAuxLink=15, maxForce=10000)
+    c = self._p.createConstraint(car,
+                                 3,
+                                 car,
+                                 19,
+                                 jointType=self._p.JOINT_GEAR,
+                                 jointAxis=[0, 1, 0],
+                                 parentFramePosition=[0, 0, 0],
+                                 childFramePosition=[0, 0, 0])
+    self._p.changeConstraint(c, gearRatio=-1, gearAuxLink=15, maxForce=10000)
+
+    self.steeringLinks = [0, 2]
+    self.maxForce = 20
+    self.nMotors = 2
+    self.motorizedwheels = [8, 15]
+    self.speedMultiplier = 20.
+    self.steeringMultiplier = 0.5
+
+  def getActionDimension(self):
+    return self.nMotors
+
+  def getObservationDimension(self):
+    return len(self.getObservation())
+
+  def getObservation(self):
+    observation = []
+    pos, orn = self._p.getBasePositionAndOrientation(self.racecarUniqueId)
+
+    observation.extend(list(pos))
+    observation.extend(list(orn))
+
+    return observation
+
+  def applyAction(self, motorCommands):
+    targetVelocity = motorCommands[0] * self.speedMultiplier
+    #print("targetVelocity")
+    #print(targetVelocity)
+    steeringAngle = motorCommands[1] * self.steeringMultiplier
+    #print("steeringAngle")
+    #print(steeringAngle)
+    #print("maxForce")
+    #print(self.maxForce)
+
+    for motor in self.motorizedwheels:
+      self._p.setJointMotorControl2(self.racecarUniqueId,
+                                    motor,
+                                    self._p.VELOCITY_CONTROL,
+                                    targetVelocity=targetVelocity,
+                                    force=self.maxForce)
+    for steer in self.steeringLinks:
+      self._p.setJointMotorControl2(self.racecarUniqueId,
+                                    steer,
+                                    self._p.POSITION_CONTROL,
+                                    targetPosition=steeringAngle)
diff --git a/examples/pybullet/gym/pybullet_envs/bullet/racecarGymEnv.py b/examples/pybullet/gym/pybullet_envs/bullet/racecarGymEnv.py
index 63d9148d3..a0118d668 100644
--- a/examples/pybullet/gym/pybullet_envs/bullet/racecarGymEnv.py
+++ b/examples/pybullet/gym/pybullet_envs/bullet/racecarGymEnv.py
@@ -1,7 +1,7 @@
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import math
 import gym
@@ -19,11 +19,9 @@ from pkg_resources import parse_version
 RENDER_HEIGHT = 720
 RENDER_WIDTH = 960
 
+
 class RacecarGymEnv(gym.Env):
-  metadata = {
-      'render.modes': ['human', 'rgb_array'],
-      'video.frames_per_second' : 50
-  }
+  metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50}
 
   def __init__(self,
                urdfRoot=pybullet_data.getDataPath(),
@@ -42,25 +40,24 @@ class RacecarGymEnv(gym.Env):
     self._renders = renders
     self._isDiscrete = isDiscrete
     if self._renders:
-      self._p = bullet_client.BulletClient(
-          connection_mode=pybullet.GUI)
+      self._p = bullet_client.BulletClient(connection_mode=pybullet.GUI)
     else:
       self._p = bullet_client.BulletClient()
 
     self.seed()
     #self.reset()
-    observationDim = 2 #len(self.getExtendedObservation())
+    observationDim = 2  #len(self.getExtendedObservation())
     #print("observationDim")
     #print(observationDim)
     # observation_high = np.array([np.finfo(np.float32).max] * observationDim)
-    observation_high = np.ones(observationDim) * 1000 #np.inf
+    observation_high = np.ones(observationDim) * 1000  #np.inf
     if (isDiscrete):
       self.action_space = spaces.Discrete(9)
     else:
-       action_dim = 2
-       self._action_bound = 1
-       action_high = np.array([self._action_bound] * action_dim)
-       self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32)
+      action_dim = 2
+      self._action_bound = 1
+      action_high = np.array([self._action_bound] * action_dim)
+      self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32)
     self.observation_space = spaces.Box(-observation_high, observation_high, dtype=np.float32)
     self.viewer = None
 
@@ -69,23 +66,24 @@ class RacecarGymEnv(gym.Env):
     #p.setPhysicsEngineParameter(numSolverIterations=300)
     self._p.setTimeStep(self._timeStep)
     #self._p.loadURDF(os.path.join(self._urdfRoot,"plane.urdf"))
-    stadiumobjects = self._p.loadSDF(os.path.join(self._urdfRoot,"stadium.sdf"))
+    stadiumobjects = self._p.loadSDF(os.path.join(self._urdfRoot, "stadium.sdf"))
     #move the stadium objects slightly above 0
     #for i in stadiumobjects:
     #	pos,orn = self._p.getBasePositionAndOrientation(i)
     #	newpos = [pos[0],pos[1],pos[2]-0.1]
     #	self._p.resetBasePositionAndOrientation(i,newpos,orn)
 
-    dist = 5 +2.*random.random()
-    ang = 2.*3.1415925438*random.random()
+    dist = 5 + 2. * random.random()
+    ang = 2. * 3.1415925438 * random.random()
 
     ballx = dist * math.sin(ang)
     bally = dist * math.cos(ang)
     ballz = 1
 
-    self._ballUniqueId = self._p.loadURDF(os.path.join(self._urdfRoot,"sphere2.urdf"),[ballx,bally,ballz])
-    self._p.setGravity(0,0,-10)
-    self._racecar = racecar.Racecar(self._p,urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
+    self._ballUniqueId = self._p.loadURDF(os.path.join(self._urdfRoot, "sphere2.urdf"),
+                                          [ballx, bally, ballz])
+    self._p.setGravity(0, 0, -10)
+    self._racecar = racecar.Racecar(self._p, urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
     self._envStepCounter = 0
     for i in range(100):
       self._p.stepSimulation()
@@ -100,26 +98,26 @@ class RacecarGymEnv(gym.Env):
     return [seed]
 
   def getExtendedObservation(self):
-     self._observation = [] #self._racecar.getObservation()
-     carpos,carorn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
-     ballpos,ballorn = self._p.getBasePositionAndOrientation(self._ballUniqueId)
-     invCarPos,invCarOrn = self._p.invertTransform(carpos,carorn)
-     ballPosInCar,ballOrnInCar = self._p.multiplyTransforms(invCarPos,invCarOrn,ballpos,ballorn)
+    self._observation = []  #self._racecar.getObservation()
+    carpos, carorn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
+    ballpos, ballorn = self._p.getBasePositionAndOrientation(self._ballUniqueId)
+    invCarPos, invCarOrn = self._p.invertTransform(carpos, carorn)
+    ballPosInCar, ballOrnInCar = self._p.multiplyTransforms(invCarPos, invCarOrn, ballpos, ballorn)
 
-     self._observation.extend([ballPosInCar[0],ballPosInCar[1]])
-     return self._observation
+    self._observation.extend([ballPosInCar[0], ballPosInCar[1]])
+    return self._observation
 
   def step(self, action):
     if (self._renders):
-      basePos,orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
+      basePos, orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
       #self._p.resetDebugVisualizerCamera(1, 30, -40, basePos)
 
     if (self._isDiscrete):
-	    fwd = [-1,-1,-1,0,0,0,1,1,1]
-	    steerings = [-0.6,0,0.6,-0.6,0,0.6,-0.6,0,0.6]
-	    forward = fwd[action]
-	    steer = steerings[action]
-	    realaction = [forward,steer]
+      fwd = [-1, -1, -1, 0, 0, 0, 1, 1, 1]
+      steerings = [-0.6, 0, 0.6, -0.6, 0, 0.6, -0.6, 0, 0.6]
+      forward = fwd[action]
+      steer = steerings[action]
+      realaction = [forward, steer]
     else:
       realaction = action
 
@@ -142,35 +140,37 @@ class RacecarGymEnv(gym.Env):
   def render(self, mode='human', close=False):
     if mode != "rgb_array":
       return np.array([])
-    base_pos,orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
-    view_matrix = self._p.computeViewMatrixFromYawPitchRoll(
-        cameraTargetPosition=base_pos,
-        distance=self._cam_dist,
-        yaw=self._cam_yaw,
-        pitch=self._cam_pitch,
-        roll=0,
-        upAxisIndex=2)
-    proj_matrix = self._p.computeProjectionMatrixFOV(
-        fov=60, aspect=float(RENDER_WIDTH)/RENDER_HEIGHT,
-        nearVal=0.1, farVal=100.0)
-    (_, _, px, _, _) = self._p.getCameraImage(
-        width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix,
-        projectionMatrix=proj_matrix, renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
+    base_pos, orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
+    view_matrix = self._p.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=base_pos,
+                                                            distance=self._cam_dist,
+                                                            yaw=self._cam_yaw,
+                                                            pitch=self._cam_pitch,
+                                                            roll=0,
+                                                            upAxisIndex=2)
+    proj_matrix = self._p.computeProjectionMatrixFOV(fov=60,
+                                                     aspect=float(RENDER_WIDTH) / RENDER_HEIGHT,
+                                                     nearVal=0.1,
+                                                     farVal=100.0)
+    (_, _, px, _, _) = self._p.getCameraImage(width=RENDER_WIDTH,
+                                              height=RENDER_HEIGHT,
+                                              viewMatrix=view_matrix,
+                                              projectionMatrix=proj_matrix,
+                                              renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
     rgb_array = np.array(px)
     rgb_array = rgb_array[:, :, :3]
     return rgb_array
 
-
   def _termination(self):
-    return self._envStepCounter>1000
+    return self._envStepCounter > 1000
 
   def _reward(self):
-    closestPoints = self._p.getClosestPoints(self._racecar.racecarUniqueId,self._ballUniqueId,10000)
+    closestPoints = self._p.getClosestPoints(self._racecar.racecarUniqueId, self._ballUniqueId,
+                                             10000)
 
     numPt = len(closestPoints)
-    reward=-1000
+    reward = -1000
     #print(numPt)
-    if (numPt>0):
+    if (numPt > 0):
       #print("reward:")
       reward = -closestPoints[0][8]
       #print(reward)
diff --git a/examples/pybullet/gym/pybullet_envs/bullet/racecarZEDGymEnv.py b/examples/pybullet/gym/pybullet_envs/bullet/racecarZEDGymEnv.py
index 6a7ce3400..8137708ba 100644
--- a/examples/pybullet/gym/pybullet_envs/bullet/racecarZEDGymEnv.py
+++ b/examples/pybullet/gym/pybullet_envs/bullet/racecarZEDGymEnv.py
@@ -1,7 +1,7 @@
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import math
 import gym
@@ -19,11 +19,9 @@ from pkg_resources import parse_version
 RENDER_HEIGHT = 720
 RENDER_WIDTH = 960
 
+
 class RacecarZEDGymEnv(gym.Env):
-  metadata = {
-      'render.modes': ['human', 'rgb_array'],
-      'video.frames_per_second' : 50
-  }
+  metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50}
 
   def __init__(self,
                urdfRoot=pybullet_data.getDataPath(),
@@ -44,8 +42,7 @@ class RacecarZEDGymEnv(gym.Env):
 
     self._isDiscrete = isDiscrete
     if self._renders:
-      self._p = bullet_client.BulletClient(
-          connection_mode=pybullet.GUI)
+      self._p = bullet_client.BulletClient(connection_mode=pybullet.GUI)
     else:
       self._p = bullet_client.BulletClient()
 
@@ -59,11 +56,14 @@ class RacecarZEDGymEnv(gym.Env):
     if (isDiscrete):
       self.action_space = spaces.Discrete(9)
     else:
-       action_dim = 2
-       self._action_bound = 1
-       action_high = np.array([self._action_bound] * action_dim)
-       self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32)
-    self.observation_space = spaces.Box(low=0, high=255, shape=(self._height, self._width, 4), dtype=np.uint8)
+      action_dim = 2
+      self._action_bound = 1
+      action_high = np.array([self._action_bound] * action_dim)
+      self.action_space = spaces.Box(-action_high, action_high, dtype=np.float32)
+    self.observation_space = spaces.Box(low=0,
+                                        high=255,
+                                        shape=(self._height, self._width, 4),
+                                        dtype=np.uint8)
 
     self.viewer = None
 
@@ -72,23 +72,24 @@ class RacecarZEDGymEnv(gym.Env):
     #p.setPhysicsEngineParameter(numSolverIterations=300)
     self._p.setTimeStep(self._timeStep)
     #self._p.loadURDF(os.path.join(os.path.dirname(__file__),"../data","plane.urdf"))
-    stadiumobjects = self._p.loadSDF(os.path.join(self._urdfRoot,"stadium.sdf"))
+    stadiumobjects = self._p.loadSDF(os.path.join(self._urdfRoot, "stadium.sdf"))
     #move the stadium objects slightly above 0
     for i in stadiumobjects:
-      pos,orn = self._p.getBasePositionAndOrientation(i)
-      newpos = [pos[0],pos[1],pos[2]+0.1]
-      self._p.resetBasePositionAndOrientation(i,newpos,orn)
+      pos, orn = self._p.getBasePositionAndOrientation(i)
+      newpos = [pos[0], pos[1], pos[2] + 0.1]
+      self._p.resetBasePositionAndOrientation(i, newpos, orn)
 
-    dist = 5 +2.*random.random()
-    ang = 2.*3.1415925438*random.random()
+    dist = 5 + 2. * random.random()
+    ang = 2. * 3.1415925438 * random.random()
 
     ballx = dist * math.sin(ang)
     bally = dist * math.cos(ang)
     ballz = 1
 
-    self._ballUniqueId = self._p.loadURDF(os.path.join(self._urdfRoot,"sphere2red.urdf"),[ballx,bally,ballz])
-    self._p.setGravity(0,0,-10)
-    self._racecar = racecar.Racecar(self._p,urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
+    self._ballUniqueId = self._p.loadURDF(os.path.join(self._urdfRoot, "sphere2red.urdf"),
+                                          [ballx, bally, ballz])
+    self._p.setGravity(0, 0, -10)
+    self._racecar = racecar.Racecar(self._p, urdfRootPath=self._urdfRoot, timeStep=self._timeStep)
     self._envStepCounter = 0
     for i in range(100):
       self._p.stepSimulation()
@@ -103,38 +104,50 @@ class RacecarZEDGymEnv(gym.Env):
     return [seed]
 
   def getExtendedObservation(self):
-     carpos,carorn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
-     carmat = self._p.getMatrixFromQuaternion(carorn)
-     ballpos,ballorn = self._p.getBasePositionAndOrientation(self._ballUniqueId)
-     invCarPos,invCarOrn = self._p.invertTransform(carpos,carorn)
-     ballPosInCar,ballOrnInCar = self._p.multiplyTransforms(invCarPos,invCarOrn,ballpos,ballorn)
-     dist0 = 0.3
-     dist1 = 1.
-     eyePos = [carpos[0]+dist0*carmat[0],carpos[1]+dist0*carmat[3],carpos[2]+dist0*carmat[6]+0.3]
-     targetPos = [carpos[0]+dist1*carmat[0],carpos[1]+dist1*carmat[3],carpos[2]+dist1*carmat[6]+0.3]
-     up = [carmat[2],carmat[5],carmat[8]]
-     viewMat = self._p.computeViewMatrix(eyePos,targetPos,up)
-     #viewMat = self._p.computeViewMatrixFromYawPitchRoll(carpos,1,0,0,0,2)
-     #print("projectionMatrix:")
-     #print(self._p.getDebugVisualizerCamera()[3])
-     projMatrix = [0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0]
-     img_arr = self._p.getCameraImage(width=self._width,height=self._height,viewMatrix=viewMat,projectionMatrix=projMatrix)
-     rgb=img_arr[2]
-     np_img_arr = np.reshape(rgb, (self._height, self._width, 4))
-     self._observation = np_img_arr
-     return self._observation
+    carpos, carorn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
+    carmat = self._p.getMatrixFromQuaternion(carorn)
+    ballpos, ballorn = self._p.getBasePositionAndOrientation(self._ballUniqueId)
+    invCarPos, invCarOrn = self._p.invertTransform(carpos, carorn)
+    ballPosInCar, ballOrnInCar = self._p.multiplyTransforms(invCarPos, invCarOrn, ballpos, ballorn)
+    dist0 = 0.3
+    dist1 = 1.
+    eyePos = [
+        carpos[0] + dist0 * carmat[0], carpos[1] + dist0 * carmat[3],
+        carpos[2] + dist0 * carmat[6] + 0.3
+    ]
+    targetPos = [
+        carpos[0] + dist1 * carmat[0], carpos[1] + dist1 * carmat[3],
+        carpos[2] + dist1 * carmat[6] + 0.3
+    ]
+    up = [carmat[2], carmat[5], carmat[8]]
+    viewMat = self._p.computeViewMatrix(eyePos, targetPos, up)
+    #viewMat = self._p.computeViewMatrixFromYawPitchRoll(carpos,1,0,0,0,2)
+    #print("projectionMatrix:")
+    #print(self._p.getDebugVisualizerCamera()[3])
+    projMatrix = [
+        0.7499999403953552, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0,
+        0.0, 0.0, -0.02000020071864128, 0.0
+    ]
+    img_arr = self._p.getCameraImage(width=self._width,
+                                     height=self._height,
+                                     viewMatrix=viewMat,
+                                     projectionMatrix=projMatrix)
+    rgb = img_arr[2]
+    np_img_arr = np.reshape(rgb, (self._height, self._width, 4))
+    self._observation = np_img_arr
+    return self._observation
 
   def step(self, action):
     if (self._renders):
-      basePos,orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
+      basePos, orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
       #self._p.resetDebugVisualizerCamera(1, 30, -40, basePos)
 
     if (self._isDiscrete):
-            fwd = [-1,-1,-1,0,0,0,1,1,1]
-            steerings = [-0.6,0,0.6,-0.6,0,0.6,-0.6,0,0.6]
-            forward = fwd[action]
-            steer = steerings[action]
-            realaction = [forward,steer]
+      fwd = [-1, -1, -1, 0, 0, 0, 1, 1, 1]
+      steerings = [-0.6, 0, 0.6, -0.6, 0, 0.6, -0.6, 0, 0.6]
+      forward = fwd[action]
+      steer = steerings[action]
+      realaction = [forward, steer]
     else:
       realaction = action
 
@@ -157,35 +170,37 @@ class RacecarZEDGymEnv(gym.Env):
   def render(self, mode='human', close=False):
     if mode != "rgb_array":
       return np.array([])
-    base_pos,orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
-    view_matrix = self._p.computeViewMatrixFromYawPitchRoll(
-        cameraTargetPosition=base_pos,
-        distance=self._cam_dist,
-        yaw=self._cam_yaw,
-        pitch=self._cam_pitch,
-        roll=0,
-        upAxisIndex=2)
-    proj_matrix = self._p.computeProjectionMatrixFOV(
-        fov=60, aspect=float(RENDER_WIDTH)/RENDER_HEIGHT,
-        nearVal=0.1, farVal=100.0)
-    (_, _, px, _, _) = self._p.getCameraImage(
-        width=RENDER_WIDTH, height=RENDER_HEIGHT, viewMatrix=view_matrix,
-        projectionMatrix=proj_matrix, renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
+    base_pos, orn = self._p.getBasePositionAndOrientation(self._racecar.racecarUniqueId)
+    view_matrix = self._p.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=base_pos,
+                                                            distance=self._cam_dist,
+                                                            yaw=self._cam_yaw,
+                                                            pitch=self._cam_pitch,
+                                                            roll=0,
+                                                            upAxisIndex=2)
+    proj_matrix = self._p.computeProjectionMatrixFOV(fov=60,
+                                                     aspect=float(RENDER_WIDTH) / RENDER_HEIGHT,
+                                                     nearVal=0.1,
+                                                     farVal=100.0)
+    (_, _, px, _, _) = self._p.getCameraImage(width=RENDER_WIDTH,
+                                              height=RENDER_HEIGHT,
+                                              viewMatrix=view_matrix,
+                                              projectionMatrix=proj_matrix,
+                                              renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
     rgb_array = np.array(px)
     rgb_array = rgb_array[:, :, :3]
     return rgb_array
 
-
   def _termination(self):
-    return self._envStepCounter>1000
+    return self._envStepCounter > 1000
 
   def _reward(self):
-    closestPoints = self._p.getClosestPoints(self._racecar.racecarUniqueId,self._ballUniqueId,10000)
+    closestPoints = self._p.getClosestPoints(self._racecar.racecarUniqueId, self._ballUniqueId,
+                                             10000)
 
     numPt = len(closestPoints)
-    reward=-1000
+    reward = -1000
     #print(numPt)
-    if (numPt>0):
+    if (numPt > 0):
       #print("reward:")
       reward = -closestPoints[0][8]
       #print(reward)
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/DeepMimic_Optimizer.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/DeepMimic_Optimizer.py
index 9aaeb2b90..b6e9e6607 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/DeepMimic_Optimizer.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/DeepMimic_Optimizer.py
@@ -4,11 +4,10 @@ import os
 import inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
-print("parentdir=",parentdir)
+os.sys.path.insert(0, parentdir)
+print("parentdir=", parentdir)
 
-
-from pybullet_envs.deep_mimic.env.pybullet_deep_mimic_env  import PyBulletDeepMimicEnv
+from pybullet_envs.deep_mimic.env.pybullet_deep_mimic_env import PyBulletDeepMimicEnv
 from pybullet_envs.deep_mimic.learning.rl_world import RLWorld
 from pybullet_utils.logger import Logger
 from pybullet_envs.deep_mimic.testrl import update_world, update_timestep, build_world
@@ -17,36 +16,40 @@ import pybullet_utils.mpi_util as MPIUtil
 args = []
 world = None
 
+
 def run():
-    global update_timestep
-    global world
+  global update_timestep
+  global world
+
+  done = False
+  while not (done):
+    update_world(world, update_timestep)
 
-    done = False
-    while not (done):
-        update_world(world, update_timestep)
+  return
 
-    return
 
 def shutdown():
-    global world
+  global world
+
+  Logger.print2('Shutting down...')
+  world.shutdown()
+  return
 
-    Logger.print2('Shutting down...')
-    world.shutdown()
-    return
 
 def main():
-    global args
-    global world
+  global args
+  global world
+
+  # Command line arguments
+  args = sys.argv[1:]
+  enable_draw = False
+  world = build_world(args, enable_draw)
 
-    # Command line arguments
-    args = sys.argv[1:]
-    enable_draw = False
-    world = build_world(args, enable_draw)
+  run()
+  shutdown()
 
-    run()
-    shutdown()
+  return
 
-    return
 
 if __name__ == '__main__':
-    main()
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/action_space.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/action_space.py
index 1014f7497..f28621417 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/action_space.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/action_space.py
@@ -1,6 +1,7 @@
 from enum import Enum
 
+
 class ActionSpace(Enum):
-    Null = 0
-    Continuous = 1
-    Discrete = 2
\ No newline at end of file
+  Null = 0
+  Continuous = 1
+  Discrete = 2
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/env.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/env.py
index c8c17df5d..900702bdd 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/env.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/env.py
@@ -1,19 +1,21 @@
-from abc import  abstractmethod
+from abc import abstractmethod
 import sys, abc
 if sys.version_info >= (3, 4):
-    ABC = abc.ABC
+  ABC = abc.ABC
 else:
-    ABC = abc.ABCMeta('ABC', (), {})
+  ABC = abc.ABCMeta('ABC', (), {})
 
 import numpy as np
 from enum import Enum
 
+
 class Env(ABC):
-    class Terminate(Enum):
-        Null = 0
-        Fail = 1
-        Succ = 2
-        
-    def __init__(self, args, enable_draw):
-        self.enable_draw = enable_draw
-        return
+
+  class Terminate(Enum):
+    Null = 0
+    Fail = 1
+    Succ = 2
+
+  def __init__(self, args, enable_draw):
+    self.enable_draw = enable_draw
+    return
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_pose_interpolator.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_pose_interpolator.py
index af0870ff5..15839ccf1 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_pose_interpolator.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_pose_interpolator.py
@@ -1,36 +1,61 @@
 from pybullet_utils import bullet_client
 import math
-	
+
+
 class HumanoidPoseInterpolator(object):
+
   def __init__(self):
     pass
-  
-  def Reset(self,basePos=[0,0,0], baseOrn=[0,0,0,1],chestRot=[0,0,0,1], neckRot=[0,0,0,1],rightHipRot= [0,0,0,1], rightKneeRot=[0],rightAnkleRot = [0,0,0,1],
-            rightShoulderRot = [0,0,0,1],rightElbowRot = [0], leftHipRot = [0,0,0,1], leftKneeRot = [0],leftAnkleRot = [0,0,0,1],
-           leftShoulderRot = [0,0,0,1] ,leftElbowRot = [0],
-           baseLinVel = [0,0,0],baseAngVel = [0,0,0], chestVel = [0,0,0],neckVel = [0,0,0],rightHipVel = [0,0,0],rightKneeVel = [0],
-           rightAnkleVel = [0,0,0],rightShoulderVel = [0,0,0],rightElbowVel = [0],leftHipVel = [0,0,0],leftKneeVel = [0],leftAnkleVel = [0,0,0],leftShoulderVel = [0,0,0],leftElbowVel = [0]
-            ):
-    
+
+  def Reset(self,
+            basePos=[0, 0, 0],
+            baseOrn=[0, 0, 0, 1],
+            chestRot=[0, 0, 0, 1],
+            neckRot=[0, 0, 0, 1],
+            rightHipRot=[0, 0, 0, 1],
+            rightKneeRot=[0],
+            rightAnkleRot=[0, 0, 0, 1],
+            rightShoulderRot=[0, 0, 0, 1],
+            rightElbowRot=[0],
+            leftHipRot=[0, 0, 0, 1],
+            leftKneeRot=[0],
+            leftAnkleRot=[0, 0, 0, 1],
+            leftShoulderRot=[0, 0, 0, 1],
+            leftElbowRot=[0],
+            baseLinVel=[0, 0, 0],
+            baseAngVel=[0, 0, 0],
+            chestVel=[0, 0, 0],
+            neckVel=[0, 0, 0],
+            rightHipVel=[0, 0, 0],
+            rightKneeVel=[0],
+            rightAnkleVel=[0, 0, 0],
+            rightShoulderVel=[0, 0, 0],
+            rightElbowVel=[0],
+            leftHipVel=[0, 0, 0],
+            leftKneeVel=[0],
+            leftAnkleVel=[0, 0, 0],
+            leftShoulderVel=[0, 0, 0],
+            leftElbowVel=[0]):
+
     self._basePos = basePos
     self._baseLinVel = baseLinVel
     #print("HumanoidPoseInterpolator.Reset: baseLinVel = ", baseLinVel)
-    self._baseOrn =baseOrn
+    self._baseOrn = baseOrn
     self._baseAngVel = baseAngVel
-    
+
     self._chestRot = chestRot
-    self._chestVel =chestVel
+    self._chestVel = chestVel
     self._neckRot = neckRot
     self._neckVel = neckVel
-    
+
     self._rightHipRot = rightHipRot
     self._rightHipVel = rightHipVel
-    self._rightKneeRot =rightKneeRot
+    self._rightKneeRot = rightKneeRot
     self._rightKneeVel = rightKneeVel
     self._rightAnkleRot = rightAnkleRot
     self._rightAnkleVel = rightAnkleVel
-    
-    self._rightShoulderRot =rightShoulderRot
+
+    self._rightShoulderRot = rightShoulderRot
     self._rightShoulderVel = rightShoulderVel
     self._rightElbowRot = rightElbowRot
     self._rightElbowVel = rightElbowVel
@@ -39,225 +64,253 @@ class HumanoidPoseInterpolator(object):
     self._leftHipVel = leftHipVel
     self._leftKneeRot = leftKneeRot
     self._leftKneeVel = leftKneeVel
-    self._leftAnkleRot =leftAnkleRot
+    self._leftAnkleRot = leftAnkleRot
     self._leftAnkleVel = leftAnkleVel
-    
+
     self._leftShoulderRot = leftShoulderRot
     self._leftShoulderVel = leftShoulderVel
-    self._leftElbowRot =leftElbowRot
+    self._leftElbowRot = leftElbowRot
     self._leftElbowVel = leftElbowVel
 
-  def ComputeLinVel(self,posStart, posEnd, deltaTime):
-    vel = [(posEnd[0]-posStart[0])/deltaTime,(posEnd[1]-posStart[1])/deltaTime,(posEnd[2]-posStart[2])/deltaTime]
+  def ComputeLinVel(self, posStart, posEnd, deltaTime):
+    vel = [(posEnd[0] - posStart[0]) / deltaTime, (posEnd[1] - posStart[1]) / deltaTime,
+           (posEnd[2] - posStart[2]) / deltaTime]
     return vel
-  
-  def ComputeAngVel(self,ornStart, ornEnd, deltaTime, bullet_client):
-    dorn = bullet_client.getDifferenceQuaternion(ornStart,ornEnd)
-    axis,angle = bullet_client.getAxisAngleFromQuaternion(dorn)
-    angVel = [(axis[0]*angle)/deltaTime,(axis[1]*angle)/deltaTime,(axis[2]*angle)/deltaTime]
+
+  def ComputeAngVel(self, ornStart, ornEnd, deltaTime, bullet_client):
+    dorn = bullet_client.getDifferenceQuaternion(ornStart, ornEnd)
+    axis, angle = bullet_client.getAxisAngleFromQuaternion(dorn)
+    angVel = [(axis[0] * angle) / deltaTime, (axis[1] * angle) / deltaTime,
+              (axis[2] * angle) / deltaTime]
     return angVel
-  
-  def ComputeAngVelRel(self,ornStart, ornEnd, deltaTime, bullet_client):
-    ornStartConjugate = [-ornStart[0],-ornStart[1],-ornStart[2],ornStart[3]]
-    pos_diff, q_diff =bullet_client.multiplyTransforms([0,0,0], ornStartConjugate, [0,0,0], ornEnd)
-    axis,angle = bullet_client.getAxisAngleFromQuaternion(q_diff)
-    angVel = [(axis[0]*angle)/deltaTime,(axis[1]*angle)/deltaTime,(axis[2]*angle)/deltaTime]
+
+  def ComputeAngVelRel(self, ornStart, ornEnd, deltaTime, bullet_client):
+    ornStartConjugate = [-ornStart[0], -ornStart[1], -ornStart[2], ornStart[3]]
+    pos_diff, q_diff = bullet_client.multiplyTransforms([0, 0, 0], ornStartConjugate, [0, 0, 0],
+                                                        ornEnd)
+    axis, angle = bullet_client.getAxisAngleFromQuaternion(q_diff)
+    angVel = [(axis[0] * angle) / deltaTime, (axis[1] * angle) / deltaTime,
+              (axis[2] * angle) / deltaTime]
     return angVel
-  
-    
+
   def NormalizeVector(self, vec):
-    length2 = orn[0]*orn[0]+orn[1]*orn[1]+orn[2]*orn[2]
-    if (length2>0):
+    length2 = orn[0] * orn[0] + orn[1] * orn[1] + orn[2] * orn[2]
+    if (length2 > 0):
       length = math.sqrt(length2)
-      
+
   def NormalizeQuaternion(self, orn):
-    length2 = orn[0]*orn[0]+orn[1]*orn[1]+orn[2]*orn[2]+orn[3]*orn[3]
-    if (length2>0):
+    length2 = orn[0] * orn[0] + orn[1] * orn[1] + orn[2] * orn[2] + orn[3] * orn[3]
+    if (length2 > 0):
       length = math.sqrt(length2)
-      orn[0]/=length
-      orn[1]/=length
-      orn[2]/=length
-      orn[3]/=length
+      orn[0] /= length
+      orn[1] /= length
+      orn[2] /= length
+      orn[3] /= length
       return orn
-      
+
     #print("Normalize? length=",length)
 
-    
   def PostProcessMotionData(self, frameData):
-    baseOrn1Start = [frameData[5],frameData[6], frameData[7],frameData[4]]
-    
-    chestRotStart = [frameData[9],frameData[10],frameData[11],frameData[8]]
-    
-    neckRotStart = [frameData[13],frameData[14],frameData[15],frameData[12]]
-    rightHipRotStart = [frameData[17],frameData[18],frameData[19],frameData[16]]
-    rightAnkleRotStart = [frameData[22],frameData[23],frameData[24],frameData[21]]
-    rightShoulderRotStart = [frameData[26],frameData[27],frameData[28],frameData[25]]
-    leftHipRotStart = [frameData[31],frameData[32],frameData[33],frameData[30]]
-    leftAnkleRotStart = [frameData[36],frameData[37],frameData[38],frameData[35]]
-    leftShoulderRotStart = [frameData[40],frameData[41],frameData[42],frameData[39]]
-    
-    
+    baseOrn1Start = [frameData[5], frameData[6], frameData[7], frameData[4]]
+
+    chestRotStart = [frameData[9], frameData[10], frameData[11], frameData[8]]
+
+    neckRotStart = [frameData[13], frameData[14], frameData[15], frameData[12]]
+    rightHipRotStart = [frameData[17], frameData[18], frameData[19], frameData[16]]
+    rightAnkleRotStart = [frameData[22], frameData[23], frameData[24], frameData[21]]
+    rightShoulderRotStart = [frameData[26], frameData[27], frameData[28], frameData[25]]
+    leftHipRotStart = [frameData[31], frameData[32], frameData[33], frameData[30]]
+    leftAnkleRotStart = [frameData[36], frameData[37], frameData[38], frameData[35]]
+    leftShoulderRotStart = [frameData[40], frameData[41], frameData[42], frameData[39]]
+
   def GetPose(self):
-     pose = [ self._basePos[0],self._basePos[1],self._basePos[2],
-       self._baseOrn[0],self._baseOrn[1],self._baseOrn[2],self._baseOrn[3],
-       self._chestRot[0],self._chestRot[1],self._chestRot[2],self._chestRot[3],
-       self._neckRot[0],self._neckRot[1],self._neckRot[2],self._neckRot[3],
-       self._rightHipRot[0],self._rightHipRot[1],self._rightHipRot[2],self._rightHipRot[3],
-       self._rightKneeRot[0],
-       self._rightAnkleRot[0],self._rightAnkleRot[1],self._rightAnkleRot[2],self._rightAnkleRot[3],
-       self._rightShoulderRot[0],self._rightShoulderRot[1],self._rightShoulderRot[2],self._rightShoulderRot[3],
-       self._rightElbowRot[0],
-       self._leftHipRot[0],self._leftHipRot[1],self._leftHipRot[2],self._leftHipRot[3],
-       self._leftKneeRot[0],
-       self._leftAnkleRot[0],self._leftAnkleRot[1],self._leftAnkleRot[2],self._leftAnkleRot[3],
-       self._leftShoulderRot[0],self._leftShoulderRot[1],self._leftShoulderRot[2],self._leftShoulderRot[3],
-       self._leftElbowRot[0]	]
-     return pose
-
-  def Slerp(self, frameFraction, frameData, frameDataNext,bullet_client ):
+    pose = [
+        self._basePos[0], self._basePos[1], self._basePos[2], self._baseOrn[0], self._baseOrn[1],
+        self._baseOrn[2], self._baseOrn[3], self._chestRot[0], self._chestRot[1],
+        self._chestRot[2], self._chestRot[3], self._neckRot[0], self._neckRot[1], self._neckRot[2],
+        self._neckRot[3], self._rightHipRot[0], self._rightHipRot[1], self._rightHipRot[2],
+        self._rightHipRot[3], self._rightKneeRot[0], self._rightAnkleRot[0],
+        self._rightAnkleRot[1], self._rightAnkleRot[2], self._rightAnkleRot[3],
+        self._rightShoulderRot[0], self._rightShoulderRot[1], self._rightShoulderRot[2],
+        self._rightShoulderRot[3], self._rightElbowRot[0], self._leftHipRot[0],
+        self._leftHipRot[1], self._leftHipRot[2], self._leftHipRot[3], self._leftKneeRot[0],
+        self._leftAnkleRot[0], self._leftAnkleRot[1], self._leftAnkleRot[2], self._leftAnkleRot[3],
+        self._leftShoulderRot[0], self._leftShoulderRot[1], self._leftShoulderRot[2],
+        self._leftShoulderRot[3], self._leftElbowRot[0]
+    ]
+    return pose
+
+  def Slerp(self, frameFraction, frameData, frameDataNext, bullet_client):
     keyFrameDuration = frameData[0]
-    basePos1Start = [frameData[1],frameData[2],frameData[3]]
-    basePos1End = [frameDataNext[1],frameDataNext[2],frameDataNext[3]]
-    self._basePos = [basePos1Start[0]+frameFraction*(basePos1End[0]-basePos1Start[0]), 
-      basePos1Start[1]+frameFraction*(basePos1End[1]-basePos1Start[1]), 
-      basePos1Start[2]+frameFraction*(basePos1End[2]-basePos1Start[2])]
-    self._baseLinVel = self.ComputeLinVel(basePos1Start,basePos1End, keyFrameDuration)
-    baseOrn1Start = [frameData[5],frameData[6], frameData[7],frameData[4]]
-    baseOrn1Next = [frameDataNext[5],frameDataNext[6], frameDataNext[7],frameDataNext[4]]
-    self._baseOrn = bullet_client.getQuaternionSlerp(baseOrn1Start,baseOrn1Next,frameFraction)
-    self._baseAngVel = self.ComputeAngVel(baseOrn1Start,baseOrn1Next, keyFrameDuration, bullet_client)
-    
+    basePos1Start = [frameData[1], frameData[2], frameData[3]]
+    basePos1End = [frameDataNext[1], frameDataNext[2], frameDataNext[3]]
+    self._basePos = [
+        basePos1Start[0] + frameFraction * (basePos1End[0] - basePos1Start[0]),
+        basePos1Start[1] + frameFraction * (basePos1End[1] - basePos1Start[1]),
+        basePos1Start[2] + frameFraction * (basePos1End[2] - basePos1Start[2])
+    ]
+    self._baseLinVel = self.ComputeLinVel(basePos1Start, basePos1End, keyFrameDuration)
+    baseOrn1Start = [frameData[5], frameData[6], frameData[7], frameData[4]]
+    baseOrn1Next = [frameDataNext[5], frameDataNext[6], frameDataNext[7], frameDataNext[4]]
+    self._baseOrn = bullet_client.getQuaternionSlerp(baseOrn1Start, baseOrn1Next, frameFraction)
+    self._baseAngVel = self.ComputeAngVel(baseOrn1Start, baseOrn1Next, keyFrameDuration,
+                                          bullet_client)
+
     ##pre-rotate to make z-up
     #y2zPos=[0,0,0.0]
     #y2zOrn = p.getQuaternionFromEuler([1.57,0,0])
     #basePos,baseOrn = p.multiplyTransforms(y2zPos, y2zOrn,basePos1,baseOrn1)
 
-    chestRotStart = [frameData[9],frameData[10],frameData[11],frameData[8]]
-    chestRotEnd = [frameDataNext[9],frameDataNext[10],frameDataNext[11],frameDataNext[8]]
-    self._chestRot = bullet_client.getQuaternionSlerp(chestRotStart,chestRotEnd,frameFraction)
-    self._chestVel = self.ComputeAngVelRel(chestRotStart,chestRotEnd,keyFrameDuration,bullet_client)
-    
-    neckRotStart = [frameData[13],frameData[14],frameData[15],frameData[12]]
-    neckRotEnd= [frameDataNext[13],frameDataNext[14],frameDataNext[15],frameDataNext[12]]
-    self._neckRot =  bullet_client.getQuaternionSlerp(neckRotStart,neckRotEnd,frameFraction)
-    self._neckVel = self.ComputeAngVelRel(neckRotStart,neckRotEnd,keyFrameDuration,bullet_client)
-    
-    rightHipRotStart = [frameData[17],frameData[18],frameData[19],frameData[16]]
-    rightHipRotEnd = [frameDataNext[17],frameDataNext[18],frameDataNext[19],frameDataNext[16]]
-    self._rightHipRot = bullet_client.getQuaternionSlerp(rightHipRotStart,rightHipRotEnd,frameFraction)
-    self._rightHipVel = self.ComputeAngVelRel(rightHipRotStart,rightHipRotEnd,keyFrameDuration,bullet_client)
-    
+    chestRotStart = [frameData[9], frameData[10], frameData[11], frameData[8]]
+    chestRotEnd = [frameDataNext[9], frameDataNext[10], frameDataNext[11], frameDataNext[8]]
+    self._chestRot = bullet_client.getQuaternionSlerp(chestRotStart, chestRotEnd, frameFraction)
+    self._chestVel = self.ComputeAngVelRel(chestRotStart, chestRotEnd, keyFrameDuration,
+                                           bullet_client)
+
+    neckRotStart = [frameData[13], frameData[14], frameData[15], frameData[12]]
+    neckRotEnd = [frameDataNext[13], frameDataNext[14], frameDataNext[15], frameDataNext[12]]
+    self._neckRot = bullet_client.getQuaternionSlerp(neckRotStart, neckRotEnd, frameFraction)
+    self._neckVel = self.ComputeAngVelRel(neckRotStart, neckRotEnd, keyFrameDuration,
+                                          bullet_client)
+
+    rightHipRotStart = [frameData[17], frameData[18], frameData[19], frameData[16]]
+    rightHipRotEnd = [frameDataNext[17], frameDataNext[18], frameDataNext[19], frameDataNext[16]]
+    self._rightHipRot = bullet_client.getQuaternionSlerp(rightHipRotStart, rightHipRotEnd,
+                                                         frameFraction)
+    self._rightHipVel = self.ComputeAngVelRel(rightHipRotStart, rightHipRotEnd, keyFrameDuration,
+                                              bullet_client)
+
     rightKneeRotStart = [frameData[20]]
     rightKneeRotEnd = [frameDataNext[20]]
-    self._rightKneeRot = [rightKneeRotStart[0]+frameFraction*(rightKneeRotEnd[0]-rightKneeRotStart[0])]
-    self._rightKneeVel = [(rightKneeRotEnd[0]-rightKneeRotStart[0])/keyFrameDuration]
-    
-    rightAnkleRotStart = [frameData[22],frameData[23],frameData[24],frameData[21]]
-    rightAnkleRotEnd = [frameDataNext[22],frameDataNext[23],frameDataNext[24],frameDataNext[21]]
-    self._rightAnkleRot =  bullet_client.getQuaternionSlerp(rightAnkleRotStart,rightAnkleRotEnd,frameFraction)
-    self._rightAnkleVel = self.ComputeAngVelRel(rightAnkleRotStart,rightAnkleRotEnd,keyFrameDuration,bullet_client)
-      
-    rightShoulderRotStart = [frameData[26],frameData[27],frameData[28],frameData[25]]
-    rightShoulderRotEnd = [frameDataNext[26],frameDataNext[27],frameDataNext[28],frameDataNext[25]]
-    self._rightShoulderRot = bullet_client.getQuaternionSlerp(rightShoulderRotStart,rightShoulderRotEnd,frameFraction)
-    self._rightShoulderVel = self.ComputeAngVelRel(rightShoulderRotStart,rightShoulderRotEnd, keyFrameDuration,bullet_client)
-    
+    self._rightKneeRot = [
+        rightKneeRotStart[0] + frameFraction * (rightKneeRotEnd[0] - rightKneeRotStart[0])
+    ]
+    self._rightKneeVel = [(rightKneeRotEnd[0] - rightKneeRotStart[0]) / keyFrameDuration]
+
+    rightAnkleRotStart = [frameData[22], frameData[23], frameData[24], frameData[21]]
+    rightAnkleRotEnd = [frameDataNext[22], frameDataNext[23], frameDataNext[24], frameDataNext[21]]
+    self._rightAnkleRot = bullet_client.getQuaternionSlerp(rightAnkleRotStart, rightAnkleRotEnd,
+                                                           frameFraction)
+    self._rightAnkleVel = self.ComputeAngVelRel(rightAnkleRotStart, rightAnkleRotEnd,
+                                                keyFrameDuration, bullet_client)
+
+    rightShoulderRotStart = [frameData[26], frameData[27], frameData[28], frameData[25]]
+    rightShoulderRotEnd = [
+        frameDataNext[26], frameDataNext[27], frameDataNext[28], frameDataNext[25]
+    ]
+    self._rightShoulderRot = bullet_client.getQuaternionSlerp(rightShoulderRotStart,
+                                                              rightShoulderRotEnd, frameFraction)
+    self._rightShoulderVel = self.ComputeAngVelRel(rightShoulderRotStart, rightShoulderRotEnd,
+                                                   keyFrameDuration, bullet_client)
+
     rightElbowRotStart = [frameData[29]]
     rightElbowRotEnd = [frameDataNext[29]]
-    self._rightElbowRot = [rightElbowRotStart[0]+frameFraction*(rightElbowRotEnd[0]-rightElbowRotStart[0])]
-    self._rightElbowVel = [(rightElbowRotEnd[0]-rightElbowRotStart[0])/keyFrameDuration]
-    
-    leftHipRotStart = [frameData[31],frameData[32],frameData[33],frameData[30]]
-    leftHipRotEnd = [frameDataNext[31],frameDataNext[32],frameDataNext[33],frameDataNext[30]]
-    self._leftHipRot = bullet_client.getQuaternionSlerp(leftHipRotStart,leftHipRotEnd,frameFraction)
-    self._leftHipVel = self.ComputeAngVelRel(leftHipRotStart, leftHipRotEnd,keyFrameDuration,bullet_client)
-    
+    self._rightElbowRot = [
+        rightElbowRotStart[0] + frameFraction * (rightElbowRotEnd[0] - rightElbowRotStart[0])
+    ]
+    self._rightElbowVel = [(rightElbowRotEnd[0] - rightElbowRotStart[0]) / keyFrameDuration]
+
+    leftHipRotStart = [frameData[31], frameData[32], frameData[33], frameData[30]]
+    leftHipRotEnd = [frameDataNext[31], frameDataNext[32], frameDataNext[33], frameDataNext[30]]
+    self._leftHipRot = bullet_client.getQuaternionSlerp(leftHipRotStart, leftHipRotEnd,
+                                                        frameFraction)
+    self._leftHipVel = self.ComputeAngVelRel(leftHipRotStart, leftHipRotEnd, keyFrameDuration,
+                                             bullet_client)
+
     leftKneeRotStart = [frameData[34]]
     leftKneeRotEnd = [frameDataNext[34]]
-    self._leftKneeRot = [leftKneeRotStart[0] +frameFraction*(leftKneeRotEnd[0]-leftKneeRotStart[0]) ]
-    self._leftKneeVel = [(leftKneeRotEnd[0]-leftKneeRotStart[0])/keyFrameDuration]
-    
-    leftAnkleRotStart = [frameData[36],frameData[37],frameData[38],frameData[35]]
-    leftAnkleRotEnd = [frameDataNext[36],frameDataNext[37],frameDataNext[38],frameDataNext[35]]
-    self._leftAnkleRot = bullet_client.getQuaternionSlerp(leftAnkleRotStart,leftAnkleRotEnd,frameFraction)
-    self._leftAnkleVel = self.ComputeAngVelRel(leftAnkleRotStart,leftAnkleRotEnd,keyFrameDuration,bullet_client)
-
-    leftShoulderRotStart = [frameData[40],frameData[41],frameData[42],frameData[39]]
-    leftShoulderRotEnd = [frameDataNext[40],frameDataNext[41],frameDataNext[42],frameDataNext[39]]
-    self._leftShoulderRot = bullet_client.getQuaternionSlerp(leftShoulderRotStart,leftShoulderRotEnd,frameFraction)
-    self._leftShoulderVel = self.ComputeAngVelRel(leftShoulderRotStart,leftShoulderRotEnd,keyFrameDuration,bullet_client)
+    self._leftKneeRot = [
+        leftKneeRotStart[0] + frameFraction * (leftKneeRotEnd[0] - leftKneeRotStart[0])
+    ]
+    self._leftKneeVel = [(leftKneeRotEnd[0] - leftKneeRotStart[0]) / keyFrameDuration]
+
+    leftAnkleRotStart = [frameData[36], frameData[37], frameData[38], frameData[35]]
+    leftAnkleRotEnd = [frameDataNext[36], frameDataNext[37], frameDataNext[38], frameDataNext[35]]
+    self._leftAnkleRot = bullet_client.getQuaternionSlerp(leftAnkleRotStart, leftAnkleRotEnd,
+                                                          frameFraction)
+    self._leftAnkleVel = self.ComputeAngVelRel(leftAnkleRotStart, leftAnkleRotEnd,
+                                               keyFrameDuration, bullet_client)
+
+    leftShoulderRotStart = [frameData[40], frameData[41], frameData[42], frameData[39]]
+    leftShoulderRotEnd = [
+        frameDataNext[40], frameDataNext[41], frameDataNext[42], frameDataNext[39]
+    ]
+    self._leftShoulderRot = bullet_client.getQuaternionSlerp(leftShoulderRotStart,
+                                                             leftShoulderRotEnd, frameFraction)
+    self._leftShoulderVel = self.ComputeAngVelRel(leftShoulderRotStart, leftShoulderRotEnd,
+                                                  keyFrameDuration, bullet_client)
 
     leftElbowRotStart = [frameData[43]]
     leftElbowRotEnd = [frameDataNext[43]]
-    self._leftElbowRot = [leftElbowRotStart[0]+frameFraction*(leftElbowRotEnd[0]-leftElbowRotStart[0])]
-    self._leftElbowVel = [(leftElbowRotEnd[0]-leftElbowRotStart[0])/keyFrameDuration]
+    self._leftElbowRot = [
+        leftElbowRotStart[0] + frameFraction * (leftElbowRotEnd[0] - leftElbowRotStart[0])
+    ]
+    self._leftElbowVel = [(leftElbowRotEnd[0] - leftElbowRotStart[0]) / keyFrameDuration]
 
     pose = self.GetPose()
     return pose
-    
+
   def ConvertFromAction(self, pybullet_client, action):
     #turn action into pose
-    
-    self.Reset()#?? needed?
-    index=0
+
+    self.Reset()  #?? needed?
+    index = 0
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    self._chestRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle)
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    self._chestRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle)
     #print("pose._chestRot=",pose._chestRot)
 
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    self._neckRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle)
-    
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    self._neckRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle)
+
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    self._rightHipRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle)
-    
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    self._rightHipRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle)
+
     angle = action[index]
-    index+=1
+    index += 1
     self._rightKneeRot = [angle]
-        
+
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    self._rightAnkleRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle)
-    
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    self._rightAnkleRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle)
+
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    self._rightShoulderRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle)
-    
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    self._rightShoulderRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle)
+
     angle = action[index]
-    index+=1
+    index += 1
     self._rightElbowRot = [angle]
-    
+
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    self._leftHipRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle)
-    
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    self._leftHipRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle)
+
     angle = action[index]
-    index+=1
+    index += 1
     self._leftKneeRot = [angle]
-    
-    
+
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    self._leftAnkleRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle)
-    
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    self._leftAnkleRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle)
+
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    self._leftShoulderRot = pybullet_client.getQuaternionFromAxisAngle(axis,angle)
-        
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    self._leftShoulderRot = pybullet_client.getQuaternionFromAxisAngle(axis, angle)
+
     angle = action[index]
-    index+=1
+    index += 1
     self._leftElbowRot = [angle]
-    
+
     pose = self.GetPose()
     return pose
-  
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_stable_pd.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_stable_pd.py
index 9418dd4bd..1a39a91cb 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_stable_pd.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/humanoid_stable_pd.py
@@ -1,144 +1,222 @@
-
-from pybullet_utils import pd_controller_stable 
+from pybullet_utils import pd_controller_stable
 from pybullet_envs.deep_mimic.env import humanoid_pose_interpolator
 import math
 
-chest=1
-neck=2
-rightHip=3
-rightKnee=4
-rightAnkle=5
-rightShoulder=6
-rightElbow=7
-leftHip=9
-leftKnee=10
-leftAnkle=11
-leftShoulder=12
-leftElbow=13
+chest = 1
+neck = 2
+rightHip = 3
+rightKnee = 4
+rightAnkle = 5
+rightShoulder = 6
+rightElbow = 7
+leftHip = 9
+leftKnee = 10
+leftAnkle = 11
+leftShoulder = 12
+leftElbow = 13
 jointFrictionForce = 0
 
+
 class HumanoidStablePD(object):
+
   def __init__(self, pybullet_client, mocap_data, timeStep, useFixedBase=True):
     self._pybullet_client = pybullet_client
     self._mocap_data = mocap_data
     print("LOADING humanoid!")
-    
+
     self._sim_model = self._pybullet_client.loadURDF(
-      "humanoid/humanoid.urdf", [0,0.889540259,0],globalScaling=0.25, useFixedBase=useFixedBase, flags=self._pybullet_client.URDF_MAINTAIN_LINK_ORDER)
+        "humanoid/humanoid.urdf", [0, 0.889540259, 0],
+        globalScaling=0.25,
+        useFixedBase=useFixedBase,
+        flags=self._pybullet_client.URDF_MAINTAIN_LINK_ORDER)
 
     #self._pybullet_client.setCollisionFilterGroupMask(self._sim_model,-1,collisionFilterGroup=0,collisionFilterMask=0)
     #for j in range (self._pybullet_client.getNumJoints(self._sim_model)):
     #  self._pybullet_client.setCollisionFilterGroupMask(self._sim_model,j,collisionFilterGroup=0,collisionFilterMask=0)
-    
-    
-    self._end_effectors = [5,8,11,14]  #ankle and wrist, both left and right
-    
+
+    self._end_effectors = [5, 8, 11, 14]  #ankle and wrist, both left and right
+
     self._kin_model = self._pybullet_client.loadURDF(
-      "humanoid/humanoid.urdf", [0,0.85,0],globalScaling=0.25, useFixedBase=True, flags=self._pybullet_client.URDF_MAINTAIN_LINK_ORDER)
-    
+        "humanoid/humanoid.urdf", [0, 0.85, 0],
+        globalScaling=0.25,
+        useFixedBase=True,
+        flags=self._pybullet_client.URDF_MAINTAIN_LINK_ORDER)
+
     self._pybullet_client.changeDynamics(self._sim_model, -1, lateralFriction=0.9)
-    for j in range (self._pybullet_client.getNumJoints(self._sim_model)):
+    for j in range(self._pybullet_client.getNumJoints(self._sim_model)):
       self._pybullet_client.changeDynamics(self._sim_model, j, lateralFriction=0.9)
-      
+
     self._pybullet_client.changeDynamics(self._sim_model, -1, linearDamping=0, angularDamping=0)
     self._pybullet_client.changeDynamics(self._kin_model, -1, linearDamping=0, angularDamping=0)
-    
+
     #todo: add feature to disable simulation for a particular object. Until then, disable all collisions
-    self._pybullet_client.setCollisionFilterGroupMask(self._kin_model,-1,collisionFilterGroup=0,collisionFilterMask=0)
-    self._pybullet_client.changeDynamics(self._kin_model,-1,activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP+self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING+self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP)
+    self._pybullet_client.setCollisionFilterGroupMask(self._kin_model,
+                                                      -1,
+                                                      collisionFilterGroup=0,
+                                                      collisionFilterMask=0)
+    self._pybullet_client.changeDynamics(
+        self._kin_model,
+        -1,
+        activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP +
+        self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING +
+        self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP)
     alpha = 0.4
-    self._pybullet_client.changeVisualShape(self._kin_model,-1, rgbaColor=[1,1,1,alpha])
-    for j in range (self._pybullet_client.getNumJoints(self._kin_model)):
-      self._pybullet_client.setCollisionFilterGroupMask(self._kin_model,j,collisionFilterGroup=0,collisionFilterMask=0)
-      self._pybullet_client.changeDynamics(self._kin_model,j,activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP+self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING+self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP)
-      self._pybullet_client.changeVisualShape(self._kin_model,j, rgbaColor=[1,1,1,alpha])
-    
-    
+    self._pybullet_client.changeVisualShape(self._kin_model, -1, rgbaColor=[1, 1, 1, alpha])
+    for j in range(self._pybullet_client.getNumJoints(self._kin_model)):
+      self._pybullet_client.setCollisionFilterGroupMask(self._kin_model,
+                                                        j,
+                                                        collisionFilterGroup=0,
+                                                        collisionFilterMask=0)
+      self._pybullet_client.changeDynamics(
+          self._kin_model,
+          j,
+          activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP +
+          self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING +
+          self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP)
+      self._pybullet_client.changeVisualShape(self._kin_model, j, rgbaColor=[1, 1, 1, alpha])
+
     self._poseInterpolator = humanoid_pose_interpolator.HumanoidPoseInterpolator()
-    
-    for i in range (self._mocap_data.NumFrames()-1):
+
+    for i in range(self._mocap_data.NumFrames() - 1):
       frameData = self._mocap_data._motion_data['Frames'][i]
       self._poseInterpolator.PostProcessMotionData(frameData)
-      
+
     self._stablePD = pd_controller_stable.PDControllerStableMultiDof(self._pybullet_client)
     self._timeStep = timeStep
-    self._kpOrg = [0,0,0,0,0,0,0,1000,1000,1000,1000,100,100,100,100,500,500,500,500,500,400,400,400,400,400,400,400,400,300,500,500,500,500,500,400,400,400,400,400,400,400,400,300]
-    self._kdOrg = [0,0,0,0,0,0,0,100,100,100,100,10,10,10,10,50,50,50,50,50,40,40,40,40,40,40,40,40,30,50,50,50,50,50,40,40,40,40,40,40,40,40,30]
-   
-    self._jointIndicesAll = [chest,neck, rightHip,rightKnee,rightAnkle,rightShoulder,rightElbow,leftHip,leftKnee,leftAnkle,leftShoulder,leftElbow]
+    self._kpOrg = [
+        0, 0, 0, 0, 0, 0, 0, 1000, 1000, 1000, 1000, 100, 100, 100, 100, 500, 500, 500, 500, 500,
+        400, 400, 400, 400, 400, 400, 400, 400, 300, 500, 500, 500, 500, 500, 400, 400, 400, 400,
+        400, 400, 400, 400, 300
+    ]
+    self._kdOrg = [
+        0, 0, 0, 0, 0, 0, 0, 100, 100, 100, 100, 10, 10, 10, 10, 50, 50, 50, 50, 50, 40, 40, 40,
+        40, 40, 40, 40, 40, 30, 50, 50, 50, 50, 50, 40, 40, 40, 40, 40, 40, 40, 40, 30
+    ]
+
+    self._jointIndicesAll = [
+        chest, neck, rightHip, rightKnee, rightAnkle, rightShoulder, rightElbow, leftHip, leftKnee,
+        leftAnkle, leftShoulder, leftElbow
+    ]
     for j in self._jointIndicesAll:
       #self._pybullet_client.setJointMotorControlMultiDof(self._sim_model, j, self._pybullet_client.POSITION_CONTROL, force=[1,1,1])
-      self._pybullet_client.setJointMotorControl2(self._sim_model, j, self._pybullet_client.POSITION_CONTROL, targetPosition=0, positionGain=0, targetVelocity=0,force=jointFrictionForce)
-      self._pybullet_client.setJointMotorControlMultiDof(self._sim_model, j, self._pybullet_client.POSITION_CONTROL,targetPosition=[0,0,0,1], targetVelocity=[0,0,0], positionGain=0,velocityGain=1,force=[jointFrictionForce,jointFrictionForce,jointFrictionForce])
-      self._pybullet_client.setJointMotorControl2(self._kin_model, j, self._pybullet_client.POSITION_CONTROL, targetPosition=0, positionGain=0, targetVelocity=0,force=0)
-      self._pybullet_client.setJointMotorControlMultiDof(self._kin_model, j, self._pybullet_client.POSITION_CONTROL,targetPosition=[0,0,0,1], targetVelocity=[0,0,0], positionGain=0,velocityGain=1,force=[jointFrictionForce,jointFrictionForce,0])
-    
-    self._jointDofCounts=[4,4,4,1,4,4,1,4,1,4,4,1]
-    
+      self._pybullet_client.setJointMotorControl2(self._sim_model,
+                                                  j,
+                                                  self._pybullet_client.POSITION_CONTROL,
+                                                  targetPosition=0,
+                                                  positionGain=0,
+                                                  targetVelocity=0,
+                                                  force=jointFrictionForce)
+      self._pybullet_client.setJointMotorControlMultiDof(
+          self._sim_model,
+          j,
+          self._pybullet_client.POSITION_CONTROL,
+          targetPosition=[0, 0, 0, 1],
+          targetVelocity=[0, 0, 0],
+          positionGain=0,
+          velocityGain=1,
+          force=[jointFrictionForce, jointFrictionForce, jointFrictionForce])
+      self._pybullet_client.setJointMotorControl2(self._kin_model,
+                                                  j,
+                                                  self._pybullet_client.POSITION_CONTROL,
+                                                  targetPosition=0,
+                                                  positionGain=0,
+                                                  targetVelocity=0,
+                                                  force=0)
+      self._pybullet_client.setJointMotorControlMultiDof(
+          self._kin_model,
+          j,
+          self._pybullet_client.POSITION_CONTROL,
+          targetPosition=[0, 0, 0, 1],
+          targetVelocity=[0, 0, 0],
+          positionGain=0,
+          velocityGain=1,
+          force=[jointFrictionForce, jointFrictionForce, 0])
+
+    self._jointDofCounts = [4, 4, 4, 1, 4, 4, 1, 4, 1, 4, 4, 1]
+
     #only those body parts/links are allowed to touch the ground, otherwise the episode terminates
-    self._allowed_body_parts=[5,11]
-    
+    self._allowed_body_parts = [5, 11]
+
     #[x,y,z] base position and [x,y,z,w] base orientation!
-    self._totalDofs = 7 
+    self._totalDofs = 7
     for dof in self._jointDofCounts:
       self._totalDofs += dof
     self.setSimTime(0)
-    
+
     self.resetPose()
-    
+
   def resetPose(self):
     #print("resetPose with self._frame=", self._frame, " and self._frameFraction=",self._frameFraction)
     pose = self.computePose(self._frameFraction)
     self.initializePose(self._poseInterpolator, self._sim_model, initBase=True)
     self.initializePose(self._poseInterpolator, self._kin_model, initBase=False)
-    
-  def initializePose(self,  pose, phys_model,initBase, initializeVelocity = True):
-    
+
+  def initializePose(self, pose, phys_model, initBase, initializeVelocity=True):
+
     if initializeVelocity:
       if initBase:
-        self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos, pose._baseOrn)
+        self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos,
+                                                              pose._baseOrn)
         self._pybullet_client.resetBaseVelocity(phys_model, pose._baseLinVel, pose._baseAngVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,chest,pose._chestRot, pose._chestVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,neck,pose._neckRot, pose._neckVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightHip,pose._rightHipRot, pose._rightHipVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightKnee,pose._rightKneeRot, pose._rightKneeVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightAnkle,pose._rightAnkleRot, pose._rightAnkleVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightShoulder,pose._rightShoulderRot, pose._rightShoulderVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightElbow, pose._rightElbowRot, pose._rightElbowVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftHip, pose._leftHipRot, pose._leftHipVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftKnee, pose._leftKneeRot, pose._leftKneeVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftAnkle, pose._leftAnkleRot, pose._leftAnkleVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftShoulder, pose._leftShoulderRot, pose._leftShoulderVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftElbow, pose._leftElbowRot, pose._leftElbowVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, chest, pose._chestRot,
+                                                    pose._chestVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, neck, pose._neckRot, pose._neckVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightHip, pose._rightHipRot,
+                                                    pose._rightHipVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightKnee, pose._rightKneeRot,
+                                                    pose._rightKneeVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightAnkle, pose._rightAnkleRot,
+                                                    pose._rightAnkleVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightShoulder,
+                                                    pose._rightShoulderRot, pose._rightShoulderVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightElbow, pose._rightElbowRot,
+                                                    pose._rightElbowVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftHip, pose._leftHipRot,
+                                                    pose._leftHipVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftKnee, pose._leftKneeRot,
+                                                    pose._leftKneeVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftAnkle, pose._leftAnkleRot,
+                                                    pose._leftAnkleVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftShoulder,
+                                                    pose._leftShoulderRot, pose._leftShoulderVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftElbow, pose._leftElbowRot,
+                                                    pose._leftElbowVel)
     else:
       if initBase:
-        self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos, pose._baseOrn)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,chest,pose._chestRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,neck,pose._neckRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightHip,pose._rightHipRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightKnee,pose._rightKneeRot, [0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightAnkle,pose._rightAnkleRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightShoulder,pose._rightShoulderRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightElbow, pose._rightElbowRot, [0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftHip, pose._leftHipRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftKnee, pose._leftKneeRot, [0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftAnkle, pose._leftAnkleRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftShoulder, pose._leftShoulderRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftElbow, pose._leftElbowRot, [0])    
+        self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos,
+                                                              pose._baseOrn)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, chest, pose._chestRot, [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, neck, pose._neckRot, [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightHip, pose._rightHipRot,
+                                                    [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightKnee, pose._rightKneeRot, [0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightAnkle, pose._rightAnkleRot,
+                                                    [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightShoulder,
+                                                    pose._rightShoulderRot, [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightElbow, pose._rightElbowRot,
+                                                    [0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftHip, pose._leftHipRot,
+                                                    [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftKnee, pose._leftKneeRot, [0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftAnkle, pose._leftAnkleRot,
+                                                    [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftShoulder,
+                                                    pose._leftShoulderRot, [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftElbow, pose._leftElbowRot, [0])
 
   def calcCycleCount(self, simTime, cycleTime):
-    phases = simTime / cycleTime;
+    phases = simTime / cycleTime
     count = math.floor(phases)
     loop = True
     #count = (loop) ? count : cMathUtil::Clamp(count, 0, 1);
     return count
-    
 
   def getCycleTime(self):
     keyFrameDuration = self._mocap_data.KeyFrameDuraction()
-    cycleTime = keyFrameDuration*(self._mocap_data.NumFrames()-1)
+    cycleTime = keyFrameDuration * (self._mocap_data.NumFrames() - 1)
     return cycleTime
-    
+
   def setSimTime(self, t):
     self._simTime = t
     #print("SetTimeTime time =",t)
@@ -147,163 +225,266 @@ class HumanoidStablePD(object):
     #print("self._motion_data.NumFrames()=",self._mocap_data.NumFrames())
     self._cycleCount = self.calcCycleCount(t, cycleTime)
     #print("cycles=",cycles)
-    frameTime = t - self._cycleCount*cycleTime
-    if (frameTime<0):
+    frameTime = t - self._cycleCount * cycleTime
+    if (frameTime < 0):
       frameTime += cycleTime
-    
-    #print("keyFrameDuration=",keyFrameDuration)  
+
+    #print("keyFrameDuration=",keyFrameDuration)
     #print("frameTime=",frameTime)
-    self._frame = int(frameTime/keyFrameDuration)
+    self._frame = int(frameTime / keyFrameDuration)
     #print("self._frame=",self._frame)
-    
-    self._frameNext = self._frame+1
-    if (self._frameNext >=  self._mocap_data.NumFrames()):
+
+    self._frameNext = self._frame + 1
+    if (self._frameNext >= self._mocap_data.NumFrames()):
       self._frameNext = self._frame
 
-    self._frameFraction = (frameTime - self._frame*keyFrameDuration)/(keyFrameDuration)
-    
-    
+    self._frameFraction = (frameTime - self._frame * keyFrameDuration) / (keyFrameDuration)
+
   def computeCycleOffset(self):
-    firstFrame=0
-    lastFrame = self._mocap_data.NumFrames()-1
+    firstFrame = 0
+    lastFrame = self._mocap_data.NumFrames() - 1
     frameData = self._mocap_data._motion_data['Frames'][0]
     frameDataNext = self._mocap_data._motion_data['Frames'][lastFrame]
-    
-    basePosStart = [frameData[1],frameData[2],frameData[3]]
-    basePosEnd = [frameDataNext[1],frameDataNext[2],frameDataNext[3]]
-    self._cycleOffset = [basePosEnd[0]-basePosStart[0],basePosEnd[1]-basePosStart[1],basePosEnd[2]-basePosStart[2]]
+
+    basePosStart = [frameData[1], frameData[2], frameData[3]]
+    basePosEnd = [frameDataNext[1], frameDataNext[2], frameDataNext[3]]
+    self._cycleOffset = [
+        basePosEnd[0] - basePosStart[0], basePosEnd[1] - basePosStart[1],
+        basePosEnd[2] - basePosStart[2]
+    ]
     return self._cycleOffset
-    
+
   def computePose(self, frameFraction):
     frameData = self._mocap_data._motion_data['Frames'][self._frame]
     frameDataNext = self._mocap_data._motion_data['Frames'][self._frameNext]
-    
+
     self._poseInterpolator.Slerp(frameFraction, frameData, frameDataNext, self._pybullet_client)
     #print("self._poseInterpolator.Slerp(", frameFraction,")=", pose)
     self.computeCycleOffset()
     oldPos = self._poseInterpolator._basePos
-    self._poseInterpolator._basePos = [oldPos[0]+self._cycleCount*self._cycleOffset[0],oldPos[1]+self._cycleCount*self._cycleOffset[1],oldPos[2]+self._cycleCount*self._cycleOffset[2]]
+    self._poseInterpolator._basePos = [
+        oldPos[0] + self._cycleCount * self._cycleOffset[0],
+        oldPos[1] + self._cycleCount * self._cycleOffset[1],
+        oldPos[2] + self._cycleCount * self._cycleOffset[2]
+    ]
     pose = self._poseInterpolator.GetPose()
-    
-    return pose
 
+    return pose
 
   def convertActionToPose(self, action):
     pose = self._poseInterpolator.ConvertFromAction(self._pybullet_client, action)
-    return pose      
+    return pose
 
   def computePDForces(self, desiredPositions, desiredVelocities, maxForces):
-    if desiredVelocities==None:
-      desiredVelocities = [0]*self._totalDofs
-      
-      
-    taus = self._stablePD.computePD(bodyUniqueId=self._sim_model, 
-      jointIndices = self._jointIndicesAll,
-      desiredPositions = desiredPositions,
-      desiredVelocities = desiredVelocities,
-      kps = self._kpOrg,
-      kds = self._kdOrg,
-      maxForces = maxForces, 
-      timeStep=self._timeStep)
+    if desiredVelocities == None:
+      desiredVelocities = [0] * self._totalDofs
+
+    taus = self._stablePD.computePD(bodyUniqueId=self._sim_model,
+                                    jointIndices=self._jointIndicesAll,
+                                    desiredPositions=desiredPositions,
+                                    desiredVelocities=desiredVelocities,
+                                    kps=self._kpOrg,
+                                    kds=self._kdOrg,
+                                    maxForces=maxForces,
+                                    timeStep=self._timeStep)
     return taus
 
   def applyPDForces(self, taus):
-    dofIndex=7
+    dofIndex = 7
     scaling = 1
-    for index in range (len(self._jointIndicesAll)):
+    for index in range(len(self._jointIndicesAll)):
       jointIndex = self._jointIndicesAll[index]
-      if self._jointDofCounts[index]==4:
-         force=[scaling*taus[dofIndex+0],scaling*taus[dofIndex+1],scaling*taus[dofIndex+2]]
-         #print("force[", jointIndex,"]=",force)
-         self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,jointIndex,self._pybullet_client.TORQUE_CONTROL,force=force)
-      if self._jointDofCounts[index]==1:
-         force=[scaling*taus[dofIndex]]
-         #print("force[", jointIndex,"]=",force)
-         self._pybullet_client.setJointMotorControlMultiDof(self._sim_model, jointIndex, controlMode=self._pybullet_client.TORQUE_CONTROL, force=force)
-      dofIndex+=self._jointDofCounts[index]
-      
+      if self._jointDofCounts[index] == 4:
+        force = [
+            scaling * taus[dofIndex + 0], scaling * taus[dofIndex + 1],
+            scaling * taus[dofIndex + 2]
+        ]
+        #print("force[", jointIndex,"]=",force)
+        self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,
+                                                           jointIndex,
+                                                           self._pybullet_client.TORQUE_CONTROL,
+                                                           force=force)
+      if self._jointDofCounts[index] == 1:
+        force = [scaling * taus[dofIndex]]
+        #print("force[", jointIndex,"]=",force)
+        self._pybullet_client.setJointMotorControlMultiDof(
+            self._sim_model,
+            jointIndex,
+            controlMode=self._pybullet_client.TORQUE_CONTROL,
+            force=force)
+      dofIndex += self._jointDofCounts[index]
+
   def setJointMotors(self, desiredPositions, maxForces):
     controlMode = self._pybullet_client.POSITION_CONTROL
-    startIndex=7
-    chest=1
-    neck=2
-    rightHip=3
-    rightKnee=4
-    rightAnkle=5
-    rightShoulder=6
-    rightElbow=7
-    leftHip=9
-    leftKnee=10
-    leftAnkle=11
-    leftShoulder=12
-    leftElbow=13
+    startIndex = 7
+    chest = 1
+    neck = 2
+    rightHip = 3
+    rightKnee = 4
+    rightAnkle = 5
+    rightShoulder = 6
+    rightElbow = 7
+    leftHip = 9
+    leftKnee = 10
+    leftAnkle = 11
+    leftShoulder = 12
+    leftElbow = 13
     kp = 0.2
-    
-    forceScale=1
+
+    forceScale = 1
     #self._jointDofCounts=[4,4,4,1,4,4,1,4,1,4,4,1]
-    maxForce = [forceScale*maxForces[startIndex],forceScale*maxForces[startIndex+1],forceScale*maxForces[startIndex+2],forceScale*maxForces[startIndex+3]]
-    startIndex+=4
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,chest,controlMode, targetPosition=self._poseInterpolator._chestRot,positionGain=kp, force=maxForce)
-    maxForce =  [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]]
-    startIndex+=4
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,neck,controlMode,targetPosition=self._poseInterpolator._neckRot,positionGain=kp, force=maxForce)
-    maxForce =  [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]]
-    startIndex+=4
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightHip,controlMode,targetPosition=self._poseInterpolator._rightHipRot,positionGain=kp, force=maxForce)
-    maxForce = [forceScale*maxForces[startIndex]]
-    startIndex+=1
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightKnee,controlMode,targetPosition=self._poseInterpolator._rightKneeRot,positionGain=kp, force=maxForce)
-    maxForce =  [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]]
-    startIndex+=4
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightAnkle,controlMode,targetPosition=self._poseInterpolator._rightAnkleRot,positionGain=kp, force=maxForce)
-    maxForce = [forceScale*maxForces[startIndex],forceScale*maxForces[startIndex+1],forceScale*maxForces[startIndex+2],forceScale*maxForces[startIndex+3]]
-    startIndex+=4
-    maxForce = [forceScale*maxForces[startIndex]]
-    startIndex+=1
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightElbow, controlMode,targetPosition=self._poseInterpolator._rightElbowRot,positionGain=kp, force=maxForce)
-    maxForce =  [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]]
-    startIndex+=4
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftHip, controlMode,targetPosition=self._poseInterpolator._leftHipRot,positionGain=kp, force=maxForce)
-    maxForce = [forceScale*maxForces[startIndex]]
-    startIndex+=1
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftKnee, controlMode,targetPosition=self._poseInterpolator._leftKneeRot,positionGain=kp, force=maxForce)
-    maxForce =  [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]]
-    startIndex+=4
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftAnkle, controlMode,targetPosition=self._poseInterpolator._leftAnkleRot,positionGain=kp, force=maxForce)
-    maxForce =  [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]]
-    startIndex+=4
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftShoulder, controlMode,targetPosition=self._poseInterpolator._leftShoulderRot,positionGain=kp, force=maxForce)
-    maxForce = [forceScale*maxForces[startIndex]]
-    startIndex+=1
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftElbow, controlMode,targetPosition=self._poseInterpolator._leftElbowRot,positionGain=kp, force=maxForce)
+    maxForce = [
+        forceScale * maxForces[startIndex], forceScale * maxForces[startIndex + 1],
+        forceScale * maxForces[startIndex + 2], forceScale * maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        chest,
+        controlMode,
+        targetPosition=self._poseInterpolator._chestRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [
+        maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2],
+        maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        neck,
+        controlMode,
+        targetPosition=self._poseInterpolator._neckRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [
+        maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2],
+        maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        rightHip,
+        controlMode,
+        targetPosition=self._poseInterpolator._rightHipRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [forceScale * maxForces[startIndex]]
+    startIndex += 1
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        rightKnee,
+        controlMode,
+        targetPosition=self._poseInterpolator._rightKneeRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [
+        maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2],
+        maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        rightAnkle,
+        controlMode,
+        targetPosition=self._poseInterpolator._rightAnkleRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [
+        forceScale * maxForces[startIndex], forceScale * maxForces[startIndex + 1],
+        forceScale * maxForces[startIndex + 2], forceScale * maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    maxForce = [forceScale * maxForces[startIndex]]
+    startIndex += 1
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        rightElbow,
+        controlMode,
+        targetPosition=self._poseInterpolator._rightElbowRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [
+        maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2],
+        maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        leftHip,
+        controlMode,
+        targetPosition=self._poseInterpolator._leftHipRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [forceScale * maxForces[startIndex]]
+    startIndex += 1
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        leftKnee,
+        controlMode,
+        targetPosition=self._poseInterpolator._leftKneeRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [
+        maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2],
+        maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        leftAnkle,
+        controlMode,
+        targetPosition=self._poseInterpolator._leftAnkleRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [
+        maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2],
+        maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        leftShoulder,
+        controlMode,
+        targetPosition=self._poseInterpolator._leftShoulderRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [forceScale * maxForces[startIndex]]
+    startIndex += 1
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        leftElbow,
+        controlMode,
+        targetPosition=self._poseInterpolator._leftElbowRot,
+        positionGain=kp,
+        force=maxForce)
     #print("startIndex=",startIndex)
-      
+
   def getPhase(self):
     keyFrameDuration = self._mocap_data.KeyFrameDuraction()
-    cycleTime = keyFrameDuration*(self._mocap_data.NumFrames()-1)
+    cycleTime = keyFrameDuration * (self._mocap_data.NumFrames() - 1)
     phase = self._simTime / cycleTime
-    phase = math.fmod(phase,1.0)
-    if (phase<0):
+    phase = math.fmod(phase, 1.0)
+    if (phase < 0):
       phase += 1
     return phase
 
   def buildHeadingTrans(self, rootOrn):
     #align root transform 'forward' with world-space x axis
     eul = self._pybullet_client.getEulerFromQuaternion(rootOrn)
-    refDir = [1,0,0]
+    refDir = [1, 0, 0]
     rotVec = self._pybullet_client.rotateVector(rootOrn, refDir)
     heading = math.atan2(-rotVec[2], rotVec[0])
-    heading2=eul[1]
+    heading2 = eul[1]
     #print("heading=",heading)
-    headingOrn = self._pybullet_client.getQuaternionFromAxisAngle([0,1,0],-heading)
+    headingOrn = self._pybullet_client.getQuaternionFromAxisAngle([0, 1, 0], -heading)
     return headingOrn
 
-
   def buildOriginTrans(self):
-    rootPos,rootOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model)
-    
+    rootPos, rootOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model)
+
     #print("rootPos=",rootPos, " rootOrn=",rootOrn)
-    invRootPos=[-rootPos[0], 0, -rootPos[2]]
+    invRootPos = [-rootPos[0], 0, -rootPos[2]]
     #invOrigTransPos, invOrigTransOrn = self._pybullet_client.invertTransform(rootPos,rootOrn)
     headingOrn = self.buildHeadingTrans(rootOrn)
     #print("headingOrn=",headingOrn)
@@ -311,8 +492,11 @@ class HumanoidStablePD(object):
     #print("headingMat=",headingMat)
     #dummy, rootOrnWithoutHeading = self._pybullet_client.multiplyTransforms([0,0,0],headingOrn, [0,0,0], rootOrn)
     #dummy, invOrigTransOrn = self._pybullet_client.multiplyTransforms([0,0,0],rootOrnWithoutHeading, invOrigTransPos, invOrigTransOrn)
-    
-    invOrigTransPos, invOrigTransOrn = self._pybullet_client.multiplyTransforms( [0,0,0],headingOrn, invRootPos,[0,0,0,1])
+
+    invOrigTransPos, invOrigTransOrn = self._pybullet_client.multiplyTransforms([0, 0, 0],
+                                                                                headingOrn,
+                                                                                invRootPos,
+                                                                                [0, 0, 0, 1])
     #print("invOrigTransPos=",invOrigTransPos)
     #print("invOrigTransOrn=",invOrigTransOrn)
     invOrigTransMat = self._pybullet_client.getMatrixFromQuaternion(invOrigTransOrn)
@@ -325,106 +509,115 @@ class HumanoidStablePD(object):
     phase = self.getPhase()
     #print("phase=",phase)
     stateVector.append(phase)
-    
-    rootTransPos, rootTransOrn=self.buildOriginTrans()
-    basePos,baseOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model)
-    
-    rootPosRel, dummy = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, basePos,[0,0,0,1])
+
+    rootTransPos, rootTransOrn = self.buildOriginTrans()
+    basePos, baseOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model)
+
+    rootPosRel, dummy = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn,
+                                                                 basePos, [0, 0, 0, 1])
     #print("!!!rootPosRel =",rootPosRel )
     #print("rootTransPos=",rootTransPos)
     #print("basePos=",basePos)
-    localPos,localOrn = self._pybullet_client.multiplyTransforms( rootTransPos, rootTransOrn , basePos,baseOrn )
-    
-    localPos=[localPos[0]-rootPosRel[0],localPos[1]-rootPosRel[1],localPos[2]-rootPosRel[2]]
+    localPos, localOrn = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn,
+                                                                  basePos, baseOrn)
+
+    localPos = [
+        localPos[0] - rootPosRel[0], localPos[1] - rootPosRel[1], localPos[2] - rootPosRel[2]
+    ]
     #print("localPos=",localPos)
-        
+
     stateVector.append(rootPosRel[1])
-    
+
     #self.pb2dmJoints=[0,1,2,9,10,11,3,4,5,12,13,14,6,7,8]
-    self.pb2dmJoints=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14]
-    
-    for pbJoint in range (self._pybullet_client.getNumJoints(self._sim_model)):
+    self.pb2dmJoints = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
+
+    for pbJoint in range(self._pybullet_client.getNumJoints(self._sim_model)):
       j = self.pb2dmJoints[pbJoint]
       #print("joint order:",j)
       ls = self._pybullet_client.getLinkState(self._sim_model, j, computeForwardKinematics=True)
       linkPos = ls[0]
       linkOrn = ls[1]
-      linkPosLocal, linkOrnLocal = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, linkPos,linkOrn)
-      if (linkOrnLocal[3]<0):
-        linkOrnLocal=[-linkOrnLocal[0],-linkOrnLocal[1],-linkOrnLocal[2],-linkOrnLocal[3]]
-      linkPosLocal=[linkPosLocal[0]-rootPosRel[0],linkPosLocal[1]-rootPosRel[1],linkPosLocal[2]-rootPosRel[2]]
+      linkPosLocal, linkOrnLocal = self._pybullet_client.multiplyTransforms(
+          rootTransPos, rootTransOrn, linkPos, linkOrn)
+      if (linkOrnLocal[3] < 0):
+        linkOrnLocal = [-linkOrnLocal[0], -linkOrnLocal[1], -linkOrnLocal[2], -linkOrnLocal[3]]
+      linkPosLocal = [
+          linkPosLocal[0] - rootPosRel[0], linkPosLocal[1] - rootPosRel[1],
+          linkPosLocal[2] - rootPosRel[2]
+      ]
       for l in linkPosLocal:
         stateVector.append(l)
       #re-order the quaternion, DeepMimic uses w,x,y,z
-      
-      if (linkOrnLocal[3]<0):
-        linkOrnLocal[0]*=-1
-        linkOrnLocal[1]*=-1
-        linkOrnLocal[2]*=-1
-        linkOrnLocal[3]*=-1
-        
+
+      if (linkOrnLocal[3] < 0):
+        linkOrnLocal[0] *= -1
+        linkOrnLocal[1] *= -1
+        linkOrnLocal[2] *= -1
+        linkOrnLocal[3] *= -1
+
       stateVector.append(linkOrnLocal[3])
       stateVector.append(linkOrnLocal[0])
       stateVector.append(linkOrnLocal[1])
       stateVector.append(linkOrnLocal[2])
-    
-    
-    for pbJoint in range (self._pybullet_client.getNumJoints(self._sim_model)):
+
+    for pbJoint in range(self._pybullet_client.getNumJoints(self._sim_model)):
       j = self.pb2dmJoints[pbJoint]
       ls = self._pybullet_client.getLinkState(self._sim_model, j, computeLinkVelocity=True)
       linkLinVel = ls[6]
       linkAngVel = ls[7]
-      linkLinVelLocal  , unused = self._pybullet_client.multiplyTransforms([0,0,0], rootTransOrn, linkLinVel,[0,0,0,1])
+      linkLinVelLocal, unused = self._pybullet_client.multiplyTransforms([0, 0, 0], rootTransOrn,
+                                                                         linkLinVel, [0, 0, 0, 1])
       #linkLinVelLocal=[linkLinVelLocal[0]-rootPosRel[0],linkLinVelLocal[1]-rootPosRel[1],linkLinVelLocal[2]-rootPosRel[2]]
-      linkAngVelLocal ,unused = self._pybullet_client.multiplyTransforms([0,0,0], rootTransOrn, linkAngVel,[0,0,0,1])
-      
+      linkAngVelLocal, unused = self._pybullet_client.multiplyTransforms([0, 0, 0], rootTransOrn,
+                                                                         linkAngVel, [0, 0, 0, 1])
+
       for l in linkLinVelLocal:
         stateVector.append(l)
       for l in linkAngVelLocal:
         stateVector.append(l)
-    
-    #print("stateVector len=",len(stateVector))  
+
+    #print("stateVector len=",len(stateVector))
     #for st in range (len(stateVector)):
     #  print("state[",st,"]=",stateVector[st])
     return stateVector
-  
+
   def terminates(self):
     #check if any non-allowed body part hits the ground
-    terminates=False
+    terminates = False
     pts = self._pybullet_client.getContactPoints()
     for p in pts:
       part = -1
       #ignore self-collision
-      if (p[1]==p[2]):
+      if (p[1] == p[2]):
         continue
-      if (p[1]==self._sim_model):
-        part=p[3]
-      if (p[2]==self._sim_model):
-        part=p[4]
-      if (part >=0 and part not in self._allowed_body_parts):
+      if (p[1] == self._sim_model):
+        part = p[3]
+      if (p[2] == self._sim_model):
+        part = p[4]
+      if (part >= 0 and part not in self._allowed_body_parts):
         #print("terminating part:", part)
-        terminates=True
-      
+        terminates = True
+
     return terminates
-    
+
   def quatMul(self, q1, q2):
-      return [ q1[3] * q2[0] + q1[0] * q2[3] + q1[1] * q2[2] - q1[2] * q2[1],
-               q1[3] * q2[1] + q1[1] * q2[3] + q1[2] * q2[0] - q1[0] * q2[2],
-              q1[3] * q2[2] + q1[2] * q2[3] + q1[0] * q2[1] - q1[1] * q2[0],
-              q1[3] * q2[3] - q1[0] * q2[0] - q1[1] * q2[1] - q1[2] * q2[2]]
-              
+    return [
+        q1[3] * q2[0] + q1[0] * q2[3] + q1[1] * q2[2] - q1[2] * q2[1],
+        q1[3] * q2[1] + q1[1] * q2[3] + q1[2] * q2[0] - q1[0] * q2[2],
+        q1[3] * q2[2] + q1[2] * q2[3] + q1[0] * q2[1] - q1[1] * q2[0],
+        q1[3] * q2[3] - q1[0] * q2[0] - q1[1] * q2[1] - q1[2] * q2[2]
+    ]
+
   def calcRootAngVelErr(self, vel0, vel1):
-    diff = [vel0[0]-vel1[0],vel0[1]-vel1[1], vel0[2]-vel1[2]]
-    return diff[0]*diff[0]+diff[1]*diff[1]+diff[2]*diff[2]
+    diff = [vel0[0] - vel1[0], vel0[1] - vel1[1], vel0[2] - vel1[2]]
+    return diff[0] * diff[0] + diff[1] * diff[1] + diff[2] * diff[2]
 
-  
-    
-  def calcRootRotDiff(self,orn0, orn1):
-    orn0Conj = [-orn0[0],-orn0[1],-orn0[2],orn0[3]]
+  def calcRootRotDiff(self, orn0, orn1):
+    orn0Conj = [-orn0[0], -orn0[1], -orn0[2], orn0[3]]
     q_diff = self.quatMul(orn1, orn0Conj)
-    axis,angle = self._pybullet_client.getAxisAngleFromQuaternion(q_diff)
-    return angle*angle
-  
+    axis, angle = self._pybullet_client.getAxisAngleFromQuaternion(q_diff)
+    return angle * angle
+
   def getReward(self, pose):
     #from DeepMimic double cSceneImitate::CalcRewardImitate
     #todo: compensate for ground height in some parts, once we move to non-flat terrain
@@ -432,7 +625,7 @@ class HumanoidStablePD(object):
     vel_w = 0.05
     end_eff_w = 0.15
     root_w = 0.2
-    com_w = 0 #0.1
+    com_w = 0  #0.1
 
     total_w = pose_w + vel_w + end_eff_w + root_w + com_w
     pose_w /= total_w
@@ -456,9 +649,9 @@ class HumanoidStablePD(object):
     root_err = 0
     com_err = 0
     heading_err = 0
-    
+
     #create a mimic reward, comparing the dynamics humanoid with a kinematic one
-    
+
     #pose = self.InitializePoseFromMotionData()
     #print("self._kin_model=",self._kin_model)
     #print("kinematicHumanoid #joints=",self._pybullet_client.getNumJoints(self._kin_model))
@@ -487,91 +680,102 @@ class HumanoidStablePD(object):
     #tVector root_ang_vel0 = cKinTree::GetRootAngVel(joint_mat, vel0);
     #tVector root_ang_vel1 = cKinTree::GetRootAngVel(joint_mat, vel1);
 
-    mJointWeights = [0.20833,0.10416, 0.0625, 0.10416,
-      0.0625, 0.041666666666666671, 0.0625, 0.0416,
-      0.00, 0.10416,  0.0625, 0.0416, 0.0625, 0.0416, 0.0000]
-      
+    mJointWeights = [
+        0.20833, 0.10416, 0.0625, 0.10416, 0.0625, 0.041666666666666671, 0.0625, 0.0416, 0.00,
+        0.10416, 0.0625, 0.0416, 0.0625, 0.0416, 0.0000
+    ]
+
     num_end_effs = 0
     num_joints = 15
-    
+
     root_rot_w = mJointWeights[root_id]
-    rootPosSim,rootOrnSim = self._pybullet_client.getBasePositionAndOrientation(self._sim_model)
-    rootPosKin ,rootOrnKin = self._pybullet_client.getBasePositionAndOrientation(self._kin_model)
+    rootPosSim, rootOrnSim = self._pybullet_client.getBasePositionAndOrientation(self._sim_model)
+    rootPosKin, rootOrnKin = self._pybullet_client.getBasePositionAndOrientation(self._kin_model)
     linVelSim, angVelSim = self._pybullet_client.getBaseVelocity(self._sim_model)
     linVelKin, angVelKin = self._pybullet_client.getBaseVelocity(self._kin_model)
-    
-    
-    root_rot_err = self.calcRootRotDiff(rootOrnSim,rootOrnKin)
+
+    root_rot_err = self.calcRootRotDiff(rootOrnSim, rootOrnKin)
     pose_err += root_rot_w * root_rot_err
-    
-    
-    root_vel_diff  = [linVelSim[0]-linVelKin[0],linVelSim[1]-linVelKin[1],linVelSim[2]-linVelKin[2]]
-    root_vel_err = root_vel_diff[0]*root_vel_diff[0]+root_vel_diff[1]*root_vel_diff[1]+root_vel_diff[2]*root_vel_diff[2]
-    
-    root_ang_vel_err = self.calcRootAngVelErr( angVelSim, angVelKin)
+
+    root_vel_diff = [
+        linVelSim[0] - linVelKin[0], linVelSim[1] - linVelKin[1], linVelSim[2] - linVelKin[2]
+    ]
+    root_vel_err = root_vel_diff[0] * root_vel_diff[0] + root_vel_diff[1] * root_vel_diff[
+        1] + root_vel_diff[2] * root_vel_diff[2]
+
+    root_ang_vel_err = self.calcRootAngVelErr(angVelSim, angVelKin)
     vel_err += root_rot_w * root_ang_vel_err
 
-    for j in range (num_joints):
+    for j in range(num_joints):
       curr_pose_err = 0
       curr_vel_err = 0
-      w = mJointWeights[j];
-      
+      w = mJointWeights[j]
+
       simJointInfo = self._pybullet_client.getJointStateMultiDof(self._sim_model, j)
-      
+
       #print("simJointInfo.pos=",simJointInfo[0])
       #print("simJointInfo.vel=",simJointInfo[1])
-      kinJointInfo = self._pybullet_client.getJointStateMultiDof(self._kin_model,j)
+      kinJointInfo = self._pybullet_client.getJointStateMultiDof(self._kin_model, j)
       #print("kinJointInfo.pos=",kinJointInfo[0])
       #print("kinJointInfo.vel=",kinJointInfo[1])
-      if (len(simJointInfo[0])==1):
-        angle = simJointInfo[0][0]-kinJointInfo[0][0]
-        curr_pose_err = angle*angle
-        velDiff = simJointInfo[1][0]-kinJointInfo[1][0]
-        curr_vel_err = velDiff*velDiff
-      if (len(simJointInfo[0])==4):
+      if (len(simJointInfo[0]) == 1):
+        angle = simJointInfo[0][0] - kinJointInfo[0][0]
+        curr_pose_err = angle * angle
+        velDiff = simJointInfo[1][0] - kinJointInfo[1][0]
+        curr_vel_err = velDiff * velDiff
+      if (len(simJointInfo[0]) == 4):
         #print("quaternion diff")
-        diffQuat = self._pybullet_client.getDifferenceQuaternion(simJointInfo[0],kinJointInfo[0])
-        axis,angle = self._pybullet_client.getAxisAngleFromQuaternion(diffQuat)
-        curr_pose_err = angle*angle
-        diffVel = [simJointInfo[1][0]-kinJointInfo[1][0],simJointInfo[1][1]-kinJointInfo[1][1],simJointInfo[1][2]-kinJointInfo[1][2]]
-        curr_vel_err = diffVel[0]*diffVel[0]+diffVel[1]*diffVel[1]+diffVel[2]*diffVel[2]
-      
-      
+        diffQuat = self._pybullet_client.getDifferenceQuaternion(simJointInfo[0], kinJointInfo[0])
+        axis, angle = self._pybullet_client.getAxisAngleFromQuaternion(diffQuat)
+        curr_pose_err = angle * angle
+        diffVel = [
+            simJointInfo[1][0] - kinJointInfo[1][0], simJointInfo[1][1] - kinJointInfo[1][1],
+            simJointInfo[1][2] - kinJointInfo[1][2]
+        ]
+        curr_vel_err = diffVel[0] * diffVel[0] + diffVel[1] * diffVel[1] + diffVel[2] * diffVel[2]
+
       pose_err += w * curr_pose_err
       vel_err += w * curr_vel_err
-    
+
       is_end_eff = j in self._end_effectors
       if is_end_eff:
-        
+
         linkStateSim = self._pybullet_client.getLinkState(self._sim_model, j)
         linkStateKin = self._pybullet_client.getLinkState(self._kin_model, j)
         linkPosSim = linkStateSim[0]
         linkPosKin = linkStateKin[0]
-        linkPosDiff = [linkPosSim[0]-linkPosKin[0],linkPosSim[1]-linkPosKin[1],linkPosSim[2]-linkPosKin[2]]
-        curr_end_err = linkPosDiff[0]*linkPosDiff[0]+linkPosDiff[1]*linkPosDiff[1]+linkPosDiff[2]*linkPosDiff[2]
+        linkPosDiff = [
+            linkPosSim[0] - linkPosKin[0], linkPosSim[1] - linkPosKin[1],
+            linkPosSim[2] - linkPosKin[2]
+        ]
+        curr_end_err = linkPosDiff[0] * linkPosDiff[0] + linkPosDiff[1] * linkPosDiff[
+            1] + linkPosDiff[2] * linkPosDiff[2]
         end_eff_err += curr_end_err
-        num_end_effs+=1
-    
+        num_end_effs += 1
+
     if (num_end_effs > 0):
       end_eff_err /= num_end_effs
-    
+
     #double root_ground_h0 = mGround->SampleHeight(sim_char.GetRootPos())
     #double root_ground_h1 = kin_char.GetOriginPos()[1]
     #root_pos0[1] -= root_ground_h0
     #root_pos1[1] -= root_ground_h1
-    root_pos_diff = [rootPosSim[0]-rootPosKin[0],rootPosSim[1]-rootPosKin[1],rootPosSim[2]-rootPosKin[2]]
-    root_pos_err = root_pos_diff[0]*root_pos_diff[0]+root_pos_diff[1]*root_pos_diff[1]+root_pos_diff[2]*root_pos_diff[2]
-    #  
+    root_pos_diff = [
+        rootPosSim[0] - rootPosKin[0], rootPosSim[1] - rootPosKin[1], rootPosSim[2] - rootPosKin[2]
+    ]
+    root_pos_err = root_pos_diff[0] * root_pos_diff[0] + root_pos_diff[1] * root_pos_diff[
+        1] + root_pos_diff[2] * root_pos_diff[2]
+    #
     #root_rot_err = cMathUtil::QuatDiffTheta(root_rot0, root_rot1)
     #root_rot_err *= root_rot_err
 
     #root_vel_err = (root_vel1 - root_vel0).squaredNorm()
     #root_ang_vel_err = (root_ang_vel1 - root_ang_vel0).squaredNorm()
 
-    root_err = root_pos_err + 0.1 * root_rot_err+ 0.01 * root_vel_err+ 0.001 * root_ang_vel_err
-    
+    root_err = root_pos_err + 0.1 * root_rot_err + 0.01 * root_vel_err + 0.001 * root_ang_vel_err
+
     #com_err = 0.1 * (com_vel1_world - com_vel0_world).squaredNorm()
-    
+
     #print("pose_err=",pose_err)
     #print("vel_err=",vel_err)
     pose_reward = math.exp(-err_scale * pose_scale * pose_err)
@@ -582,7 +786,6 @@ class HumanoidStablePD(object):
 
     reward = pose_w * pose_reward + vel_w * vel_reward + end_eff_w * end_eff_reward + root_w * root_reward + com_w * com_reward
 
-    
     # pose_reward,vel_reward,end_eff_reward, root_reward, com_reward);
     #print("reward=",reward)
     #print("pose_reward=",pose_reward)
@@ -590,5 +793,5 @@ class HumanoidStablePD(object):
     #print("end_eff_reward=",end_eff_reward)
     #print("root_reward=",root_reward)
     #print("com_reward=",com_reward)
-    
+
     return reward
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/motion_capture_data.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/motion_capture_data.py
index ea9399899..f14ef9e78 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/motion_capture_data.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/motion_capture_data.py
@@ -1,7 +1,9 @@
 import json
 import math
 
+
 class MotionCaptureData(object):
+
   def __init__(self):
     self.Reset()
 
@@ -13,30 +15,33 @@ class MotionCaptureData(object):
       self._motion_data = json.load(f)
 
   def NumFrames(self):
-    return  len(self._motion_data['Frames'])
+    return len(self._motion_data['Frames'])
 
   def KeyFrameDuraction(self):
-  	return self._motion_data['Frames'][0][0]
- 
+    return self._motion_data['Frames'][0][0]
+
   def getCycleTime(self):
     keyFrameDuration = self.KeyFrameDuraction()
-    cycleTime = keyFrameDuration*(self.NumFrames()-1)
+    cycleTime = keyFrameDuration * (self.NumFrames() - 1)
     return cycleTime
-    
+
   def calcCycleCount(self, simTime, cycleTime):
-    phases = simTime / cycleTime;
+    phases = simTime / cycleTime
     count = math.floor(phases)
     loop = True
     #count = (loop) ? count : cMathUtil::Clamp(count, 0, 1);
     return count
-    
+
   def computeCycleOffset(self):
-    firstFrame=0
-    lastFrame = self.NumFrames()-1
+    firstFrame = 0
+    lastFrame = self.NumFrames() - 1
     frameData = self._motion_data['Frames'][0]
     frameDataNext = self._motion_data['Frames'][lastFrame]
-    
-    basePosStart = [frameData[1],frameData[2],frameData[3]]
-    basePosEnd = [frameDataNext[1],frameDataNext[2],frameDataNext[3]]
-    self._cycleOffset = [basePosEnd[0]-basePosStart[0],basePosEnd[1]-basePosStart[1],basePosEnd[2]-basePosStart[2]]
-    return self._cycleOffset
\ No newline at end of file
+
+    basePosStart = [frameData[1], frameData[2], frameData[3]]
+    basePosEnd = [frameDataNext[1], frameDataNext[2], frameDataNext[3]]
+    self._cycleOffset = [
+        basePosEnd[0] - basePosStart[0], basePosEnd[1] - basePosStart[1],
+        basePosEnd[2] - basePosStart[2]
+    ]
+    return self._cycleOffset
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/pybullet_deep_mimic_env.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/pybullet_deep_mimic_env.py
index 68e4d613e..764d70c3d 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/pybullet_deep_mimic_env.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/pybullet_deep_mimic_env.py
@@ -9,301 +9,313 @@ from pybullet_envs.deep_mimic.env import humanoid_stable_pd
 import pybullet_data
 import pybullet as p1
 import random
-      
-      
+
+
 class PyBulletDeepMimicEnv(Env):
-    def __init__(self, arg_parser=None, enable_draw=False, pybullet_client=None):
-      super().__init__(arg_parser, enable_draw)
-      self._num_agents = 1
-      self._pybullet_client = pybullet_client
-      self._isInitialized = False
-      self._useStablePD = True
-      self._arg_parser = arg_parser
-      self.reset()
-    
-    def reset(self):
-      
-      
-      if not self._isInitialized:
-        if self.enable_draw:
-          self._pybullet_client =  bullet_client.BulletClient(connection_mode=p1.GUI)
-          #disable 'GUI' since it slows down a lot on Mac OSX and some other platforms
-          self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_GUI,0)
-        else:
-          self._pybullet_client  =  bullet_client.BulletClient()
-        
-        self._pybullet_client.setAdditionalSearchPath(pybullet_data.getDataPath())
-        z2y = self._pybullet_client.getQuaternionFromEuler([-math.pi*0.5,0,0])
-        self._planeId = self._pybullet_client.loadURDF("plane_implicit.urdf",[0,0,0],z2y, useMaximalCoordinates=True)
-        #print("planeId=",self._planeId)
-        self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_Y_AXIS_UP,1)
-        self._pybullet_client.setGravity(0,-9.8,0)
-        
-        self._pybullet_client.setPhysicsEngineParameter(numSolverIterations=10)
-        self._pybullet_client.changeDynamics(self._planeId, linkIndex=-1, lateralFriction=0.9)
-        
-        self._mocapData = motion_capture_data.MotionCaptureData()
-        
-        motion_file = self._arg_parser.parse_strings('motion_file')
-        print("motion_file=",motion_file[0])
-                
-        motionPath = pybullet_data.getDataPath()+"/"+motion_file[0]
-        #motionPath = pybullet_data.getDataPath()+"/motions/humanoid3d_backflip.txt"
-        self._mocapData.Load(motionPath)
-        timeStep = 1./600.
-        useFixedBase=False
-        self._humanoid = humanoid_stable_pd.HumanoidStablePD(self._pybullet_client, self._mocapData, timeStep, useFixedBase)
-        self._isInitialized = True
-        
-        self._pybullet_client.setTimeStep(timeStep)
-        self._pybullet_client.setPhysicsEngineParameter(numSubSteps=1)
-       
-        
-        selfCheck = False
-        if (selfCheck):
-          curTime = 0
-          while self._pybullet_client.isConnected():
-            self._humanoid.setSimTime(curTime)
-            state = self._humanoid.getState()
-            #print("state=",state)
-            pose = self._humanoid.computePose(self._humanoid._frameFraction)
-            for i in range (10):
-              curTime+=timeStep
-              #taus = self._humanoid.computePDForces(pose)
-              #self._humanoid.applyPDForces(taus)
-              #self._pybullet_client.stepSimulation()
-            time.sleep(timeStep)
-      #print("numframes = ", self._humanoid._mocap_data.NumFrames())
-      #startTime = random.randint(0,self._humanoid._mocap_data.NumFrames()-2)
-      rnrange = 1000
-      rn = random.randint(0,rnrange)
-      startTime = float(rn)/rnrange * self._humanoid.getCycleTime()
-      self.t = startTime
-      
-      self._humanoid.setSimTime(startTime)
-      
-      self._humanoid.resetPose()
-      #this clears the contact points. Todo: add API to explicitly clear all contact points?
-      #self._pybullet_client.stepSimulation()
-      self._humanoid.resetPose()
-      self.needs_update_time = self.t-1#force update
-       	
-    def get_num_agents(self):
-    	return self._num_agents
-    	
-    def get_action_space(self, agent_id):
-    	return ActionSpace(ActionSpace.Continuous)
-    	
-    def get_reward_min(self, agent_id):
-    	return 0
-    
-    def get_reward_max(self, agent_id):
-    	return 1
-    	
-    def get_reward_fail(self, agent_id):
-    	return self.get_reward_min(agent_id)
-    
-    def get_reward_succ(self, agent_id):
-    	return self.get_reward_max(agent_id)
-    	
-    #scene_name == "imitate" -> cDrawSceneImitate
-    def get_state_size(self, agent_id):
-        #cCtController::GetStateSize()
-        #int state_size = cDeepMimicCharController::GetStateSize();
-        #                     state_size += GetStatePoseSize();#106
-	      #                     state_size += GetStateVelSize(); #(3+3)*numBodyParts=90
-        #state_size += GetStatePhaseSize();#1
-        #197
-        return 197
-
-    def build_state_norm_groups(self, agent_id):
-        #if (mEnablePhaseInput)
-	      #{
-		    #int phase_group = gNormGroupNone;
-		    #int phase_offset = GetStatePhaseOffset();
-		    #int phase_size = GetStatePhaseSize();
-		    #out_groups.segment(phase_offset, phase_size) = phase_group * Eigen::VectorXi::Ones(phase_size);
-        groups = [0]*self.get_state_size(agent_id)
-        groups[0] = -1
-        return groups	
-        
-    def build_state_offset(self, agent_id):
-        out_offset = [0]*self.get_state_size(agent_id)
-        phase_offset = -0.5
-        out_offset[0] = phase_offset
-        return np.array(out_offset)
-    
-    def build_state_scale(self, agent_id):
-        out_scale = [1]*self.get_state_size(agent_id)
-        phase_scale = 2
-        out_scale[0] = phase_scale
-        return np.array(out_scale)
-
-    def get_goal_size(self, agent_id):
-        return 0
-
-    def get_action_size(self, agent_id):
-     	  ctrl_size = 43 #numDof
-     	  root_size = 7
-     	  return ctrl_size - root_size
-     	  
-    def build_goal_norm_groups(self, agent_id):
-        return np.array([])
-        
-    def build_goal_offset(self, agent_id):
-        return np.array([])
-    
-    def build_goal_scale(self, agent_id):
-        return np.array([])
-        
-    def build_action_offset(self, agent_id):
-    	  out_offset = [0] * self.get_action_size(agent_id)
-    	  out_offset = [0.0000000000,0.0000000000,0.0000000000,-0.200000000,0.0000000000,0.0000000000,0.0000000000,
-    	    -0.200000000,0.0000000000,0.0000000000,	0.00000000,	-0.2000000,	1.57000000,	0.00000000,	0.00000000,
-    	    0.00000000,	-0.2000000,	0.00000000,	0.00000000,	0.00000000,	-0.2000000,	-1.5700000,	0.00000000,	0.00000000,
-    	    0.00000000,	-0.2000000,	1.57000000,	0.00000000,	0.00000000,	0.00000000,	-0.2000000,	0.00000000,	0.00000000,
-    	    0.00000000,	-0.2000000,	-1.5700000]
-    	  #see cCtCtrlUtil::BuildOffsetScalePDPrismatic and
-    	  #see cCtCtrlUtil::BuildOffsetScalePDSpherical
-    	  return np.array(out_offset)
-    	  
-    def build_action_scale(self, agent_id):
-    	  out_scale = [1] * self.get_action_size(agent_id)
-    	  #see cCtCtrlUtil::BuildOffsetScalePDPrismatic and
-    	  #see cCtCtrlUtil::BuildOffsetScalePDSpherical
-    	  out_scale=[ 0.20833333333333,1.00000000000000,1.00000000000000,1.00000000000000,0.25000000000000,
-    	    1.00000000000000,1.00000000000000,1.00000000000000,0.12077294685990,1.00000000000000,
-    	    1.000000000000,	1.000000000000,	0.159235668789,	0.159235668789,	1.000000000000,
-    	    1.000000000000,	1.000000000000,	0.079617834394,	1.000000000000,	1.000000000000,
-    	    1.000000000000,	0.159235668789,	0.120772946859,	1.000000000000,	1.000000000000,
-    	    1.000000000000,	0.159235668789,	0.159235668789,	1.000000000000,	1.000000000000,
-    	    1.000000000000,	0.107758620689,	1.000000000000,	1.000000000000,	1.000000000000,
-    	    0.159235668789]
-    	  return np.array(out_scale)
-    
-    def build_action_bound_min(self, agent_id):
-    	  #see cCtCtrlUtil::BuildBoundsPDSpherical
-    	  out_scale = [-1] * self.get_action_size(agent_id)
-    	  out_scale = [-4.79999999999,-1.00000000000,-1.00000000000,-1.00000000000,-4.00000000000,
-    	    -1.00000000000,-1.00000000000,-1.00000000000,-7.77999999999,-1.00000000000,	-1.000000000,
-	    	  -1.000000000,	-7.850000000,	-6.280000000,	-1.000000000,	-1.000000000,	-1.000000000,
-	    	  -12.56000000,	-1.000000000,	-1.000000000,	-1.000000000,	-4.710000000,
-	    	  -7.779999999,	-1.000000000,	-1.000000000,	-1.000000000,	-7.850000000,
-	    	  -6.280000000,	-1.000000000,	-1.000000000,	-1.000000000,	-8.460000000,
-	    	  -1.000000000,	-1.000000000,	-1.000000000,	-4.710000000]
-
-    	  return out_scale
-    
-    def build_action_bound_max(self, agent_id):
-    	  out_scale = [1] * self.get_action_size(agent_id)
-    	  out_scale=[
-    	    4.799999999,1.000000000,1.000000000,1.000000000,4.000000000,1.000000000,
-    	    1.000000000,1.000000000,8.779999999,1.000000000,	1.0000000,	1.0000000,
-    	  	4.7100000,	6.2800000,	1.0000000,	1.0000000,	1.0000000,
-	    	  12.560000,	1.0000000,	1.0000000,	1.0000000,	7.8500000,
-	    	  8.7799999,	1.0000000,	1.0000000,	1.0000000,	4.7100000,
-	    	  6.2800000,	1.0000000,	1.0000000,	1.0000000,	10.100000,
-	    	  1.0000000,	1.0000000,	1.0000000,	7.8500000]
-    	  return out_scale
-    	  
-    def set_mode(self, mode):
-    	  self._mode = mode
-    	  
-    def need_new_action(self, agent_id):
-        if self.t>=self.needs_update_time:
-          self.needs_update_time = self.t + 1./30.
-          return True
-        return False
-        
-    def record_state(self, agent_id):
-        state = self._humanoid.getState()
-        
-        return np.array(state)
-        
-        
-    def record_goal(self, agent_id):
-        return np.array([])
-        
-    def calc_reward(self, agent_id):
-        kinPose = self._humanoid.computePose(self._humanoid._frameFraction)
-        reward = self._humanoid.getReward(kinPose)
-        return reward
-        
-    def set_action(self, agent_id, action):
-        #print("action=",)
-        #for a in action:
-        #  print(a)
-        np.savetxt("pb_action.csv", action, delimiter=",")
-        self.desiredPose = self._humanoid.convertActionToPose(action)
-        #we need the target root positon and orientation to be zero, to be compatible with deep mimic
-        self.desiredPose[0] = 0
-        self.desiredPose[1] = 0
-        self.desiredPose[2] = 0
-        self.desiredPose[3] = 0
-        self.desiredPose[4] = 0
-        self.desiredPose[5] = 0
-        self.desiredPose[6] = 0
-        target_pose = np.array(self.desiredPose)
-        
-        
-        np.savetxt("pb_target_pose.csv", target_pose, delimiter=",")
-        
-        #print("set_action: desiredPose=", self.desiredPose)
-        
-    def log_val(self, agent_id, val):
-        pass
-        
-        
-    def update(self, timeStep):
-      #print("pybullet_deep_mimic_env:update timeStep=",timeStep," t=",self.t)
+
+  def __init__(self, arg_parser=None, enable_draw=False, pybullet_client=None):
+    super().__init__(arg_parser, enable_draw)
+    self._num_agents = 1
+    self._pybullet_client = pybullet_client
+    self._isInitialized = False
+    self._useStablePD = True
+    self._arg_parser = arg_parser
+    self.reset()
+
+  def reset(self):
+
+    if not self._isInitialized:
+      if self.enable_draw:
+        self._pybullet_client = bullet_client.BulletClient(connection_mode=p1.GUI)
+        #disable 'GUI' since it slows down a lot on Mac OSX and some other platforms
+        self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_GUI, 0)
+      else:
+        self._pybullet_client = bullet_client.BulletClient()
+
+      self._pybullet_client.setAdditionalSearchPath(pybullet_data.getDataPath())
+      z2y = self._pybullet_client.getQuaternionFromEuler([-math.pi * 0.5, 0, 0])
+      self._planeId = self._pybullet_client.loadURDF("plane_implicit.urdf", [0, 0, 0],
+                                                     z2y,
+                                                     useMaximalCoordinates=True)
+      #print("planeId=",self._planeId)
+      self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_Y_AXIS_UP, 1)
+      self._pybullet_client.setGravity(0, -9.8, 0)
+
+      self._pybullet_client.setPhysicsEngineParameter(numSolverIterations=10)
+      self._pybullet_client.changeDynamics(self._planeId, linkIndex=-1, lateralFriction=0.9)
+
+      self._mocapData = motion_capture_data.MotionCaptureData()
+
+      motion_file = self._arg_parser.parse_strings('motion_file')
+      print("motion_file=", motion_file[0])
+
+      motionPath = pybullet_data.getDataPath() + "/" + motion_file[0]
+      #motionPath = pybullet_data.getDataPath()+"/motions/humanoid3d_backflip.txt"
+      self._mocapData.Load(motionPath)
+      timeStep = 1. / 600.
+      useFixedBase = False
+      self._humanoid = humanoid_stable_pd.HumanoidStablePD(self._pybullet_client, self._mocapData,
+                                                           timeStep, useFixedBase)
+      self._isInitialized = True
+
       self._pybullet_client.setTimeStep(timeStep)
-      self._humanoid._timeStep = timeStep
-        
-      for i in range(1):
-          self.t += timeStep
-          self._humanoid.setSimTime(self.t)
-          
-          if self.desiredPose:
-            kinPose = self._humanoid.computePose(self._humanoid._frameFraction)
-            self._humanoid.initializePose(self._humanoid._poseInterpolator, self._humanoid._kin_model, initBase=True)
-            #pos,orn=self._pybullet_client.getBasePositionAndOrientation(self._humanoid._sim_model)
-            #self._pybullet_client.resetBasePositionAndOrientation(self._humanoid._kin_model, [pos[0]+3,pos[1],pos[2]],orn)
-            #print("desiredPositions=",self.desiredPose)
-            maxForces = [0,0,0,0,0,0,0,200,200,200,200, 50,50,50,50, 200,200,200,200, 150, 90,90,90,90, 100,100,100,100, 60, 200,200,200,200,  150, 90, 90, 90, 90, 100,100,100,100, 60]
-            
-            if self._useStablePD:
-              taus = self._humanoid.computePDForces(self.desiredPose, desiredVelocities=None, maxForces=maxForces)
-              self._humanoid.applyPDForces(taus)
-            else:
-              self._humanoid.setJointMotors(self.desiredPose, maxForces=maxForces)
-            
-            self._pybullet_client.stepSimulation()
-            
-
-    def set_sample_count(self, count):
-        return
-        
-    def check_terminate(self, agent_id):
-      return Env.Terminate(self.is_episode_end())
-      
-    def is_episode_end(self):
-      isEnded = self._humanoid.terminates()
-      #also check maximum time, 20 seconds (todo get from file)
-      #print("self.t=",self.t)
-      if (self.t>20):
-        isEnded = True
-      return isEnded
-      
-    def check_valid_episode(self):
-      #could check if limbs exceed velocity threshold
-      return true
-    
-    def getKeyboardEvents(self):
-      return self._pybullet_client.getKeyboardEvents()
-    
-    def isKeyTriggered(self, keys, key):
-      o = ord(key)
-      #print("ord=",o)
-      if o in keys:
-        return keys[ord(key)] & self._pybullet_client.KEY_WAS_TRIGGERED
-      return False
+      self._pybullet_client.setPhysicsEngineParameter(numSubSteps=1)
+
+      selfCheck = False
+      if (selfCheck):
+        curTime = 0
+        while self._pybullet_client.isConnected():
+          self._humanoid.setSimTime(curTime)
+          state = self._humanoid.getState()
+          #print("state=",state)
+          pose = self._humanoid.computePose(self._humanoid._frameFraction)
+          for i in range(10):
+            curTime += timeStep
+            #taus = self._humanoid.computePDForces(pose)
+            #self._humanoid.applyPDForces(taus)
+            #self._pybullet_client.stepSimulation()
+          time.sleep(timeStep)
+    #print("numframes = ", self._humanoid._mocap_data.NumFrames())
+    #startTime = random.randint(0,self._humanoid._mocap_data.NumFrames()-2)
+    rnrange = 1000
+    rn = random.randint(0, rnrange)
+    startTime = float(rn) / rnrange * self._humanoid.getCycleTime()
+    self.t = startTime
+
+    self._humanoid.setSimTime(startTime)
+
+    self._humanoid.resetPose()
+    #this clears the contact points. Todo: add API to explicitly clear all contact points?
+    #self._pybullet_client.stepSimulation()
+    self._humanoid.resetPose()
+    self.needs_update_time = self.t - 1  #force update
+
+  def get_num_agents(self):
+    return self._num_agents
+
+  def get_action_space(self, agent_id):
+    return ActionSpace(ActionSpace.Continuous)
+
+  def get_reward_min(self, agent_id):
+    return 0
+
+  def get_reward_max(self, agent_id):
+    return 1
+
+  def get_reward_fail(self, agent_id):
+    return self.get_reward_min(agent_id)
+
+  def get_reward_succ(self, agent_id):
+    return self.get_reward_max(agent_id)
+
+  #scene_name == "imitate" -> cDrawSceneImitate
+  def get_state_size(self, agent_id):
+    #cCtController::GetStateSize()
+    #int state_size = cDeepMimicCharController::GetStateSize();
+    #                     state_size += GetStatePoseSize();#106
+    #                     state_size += GetStateVelSize(); #(3+3)*numBodyParts=90
+    #state_size += GetStatePhaseSize();#1
+    #197
+    return 197
+
+  def build_state_norm_groups(self, agent_id):
+    #if (mEnablePhaseInput)
+    #{
+    #int phase_group = gNormGroupNone;
+    #int phase_offset = GetStatePhaseOffset();
+    #int phase_size = GetStatePhaseSize();
+    #out_groups.segment(phase_offset, phase_size) = phase_group * Eigen::VectorXi::Ones(phase_size);
+    groups = [0] * self.get_state_size(agent_id)
+    groups[0] = -1
+    return groups
+
+  def build_state_offset(self, agent_id):
+    out_offset = [0] * self.get_state_size(agent_id)
+    phase_offset = -0.5
+    out_offset[0] = phase_offset
+    return np.array(out_offset)
+
+  def build_state_scale(self, agent_id):
+    out_scale = [1] * self.get_state_size(agent_id)
+    phase_scale = 2
+    out_scale[0] = phase_scale
+    return np.array(out_scale)
+
+  def get_goal_size(self, agent_id):
+    return 0
+
+  def get_action_size(self, agent_id):
+    ctrl_size = 43  #numDof
+    root_size = 7
+    return ctrl_size - root_size
+
+  def build_goal_norm_groups(self, agent_id):
+    return np.array([])
+
+  def build_goal_offset(self, agent_id):
+    return np.array([])
+
+  def build_goal_scale(self, agent_id):
+    return np.array([])
+
+  def build_action_offset(self, agent_id):
+    out_offset = [0] * self.get_action_size(agent_id)
+    out_offset = [
+        0.0000000000, 0.0000000000, 0.0000000000, -0.200000000, 0.0000000000, 0.0000000000,
+        0.0000000000, -0.200000000, 0.0000000000, 0.0000000000, 0.00000000, -0.2000000, 1.57000000,
+        0.00000000, 0.00000000, 0.00000000, -0.2000000, 0.00000000, 0.00000000, 0.00000000,
+        -0.2000000, -1.5700000, 0.00000000, 0.00000000, 0.00000000, -0.2000000, 1.57000000,
+        0.00000000, 0.00000000, 0.00000000, -0.2000000, 0.00000000, 0.00000000, 0.00000000,
+        -0.2000000, -1.5700000
+    ]
+    #see cCtCtrlUtil::BuildOffsetScalePDPrismatic and
+    #see cCtCtrlUtil::BuildOffsetScalePDSpherical
+    return np.array(out_offset)
+
+  def build_action_scale(self, agent_id):
+    out_scale = [1] * self.get_action_size(agent_id)
+    #see cCtCtrlUtil::BuildOffsetScalePDPrismatic and
+    #see cCtCtrlUtil::BuildOffsetScalePDSpherical
+    out_scale = [
+        0.20833333333333, 1.00000000000000, 1.00000000000000, 1.00000000000000, 0.25000000000000,
+        1.00000000000000, 1.00000000000000, 1.00000000000000, 0.12077294685990, 1.00000000000000,
+        1.000000000000, 1.000000000000, 0.159235668789, 0.159235668789, 1.000000000000,
+        1.000000000000, 1.000000000000, 0.079617834394, 1.000000000000, 1.000000000000,
+        1.000000000000, 0.159235668789, 0.120772946859, 1.000000000000, 1.000000000000,
+        1.000000000000, 0.159235668789, 0.159235668789, 1.000000000000, 1.000000000000,
+        1.000000000000, 0.107758620689, 1.000000000000, 1.000000000000, 1.000000000000,
+        0.159235668789
+    ]
+    return np.array(out_scale)
+
+  def build_action_bound_min(self, agent_id):
+    #see cCtCtrlUtil::BuildBoundsPDSpherical
+    out_scale = [-1] * self.get_action_size(agent_id)
+    out_scale = [
+        -4.79999999999, -1.00000000000, -1.00000000000, -1.00000000000, -4.00000000000,
+        -1.00000000000, -1.00000000000, -1.00000000000, -7.77999999999, -1.00000000000,
+        -1.000000000, -1.000000000, -7.850000000, -6.280000000, -1.000000000, -1.000000000,
+        -1.000000000, -12.56000000, -1.000000000, -1.000000000, -1.000000000, -4.710000000,
+        -7.779999999, -1.000000000, -1.000000000, -1.000000000, -7.850000000, -6.280000000,
+        -1.000000000, -1.000000000, -1.000000000, -8.460000000, -1.000000000, -1.000000000,
+        -1.000000000, -4.710000000
+    ]
+
+    return out_scale
+
+  def build_action_bound_max(self, agent_id):
+    out_scale = [1] * self.get_action_size(agent_id)
+    out_scale = [
+        4.799999999, 1.000000000, 1.000000000, 1.000000000, 4.000000000, 1.000000000, 1.000000000,
+        1.000000000, 8.779999999, 1.000000000, 1.0000000, 1.0000000, 4.7100000, 6.2800000,
+        1.0000000, 1.0000000, 1.0000000, 12.560000, 1.0000000, 1.0000000, 1.0000000, 7.8500000,
+        8.7799999, 1.0000000, 1.0000000, 1.0000000, 4.7100000, 6.2800000, 1.0000000, 1.0000000,
+        1.0000000, 10.100000, 1.0000000, 1.0000000, 1.0000000, 7.8500000
+    ]
+    return out_scale
+
+  def set_mode(self, mode):
+    self._mode = mode
+
+  def need_new_action(self, agent_id):
+    if self.t >= self.needs_update_time:
+      self.needs_update_time = self.t + 1. / 30.
+      return True
+    return False
+
+  def record_state(self, agent_id):
+    state = self._humanoid.getState()
+
+    return np.array(state)
+
+  def record_goal(self, agent_id):
+    return np.array([])
+
+  def calc_reward(self, agent_id):
+    kinPose = self._humanoid.computePose(self._humanoid._frameFraction)
+    reward = self._humanoid.getReward(kinPose)
+    return reward
+
+  def set_action(self, agent_id, action):
+    #print("action=",)
+    #for a in action:
+    #  print(a)
+    np.savetxt("pb_action.csv", action, delimiter=",")
+    self.desiredPose = self._humanoid.convertActionToPose(action)
+    #we need the target root positon and orientation to be zero, to be compatible with deep mimic
+    self.desiredPose[0] = 0
+    self.desiredPose[1] = 0
+    self.desiredPose[2] = 0
+    self.desiredPose[3] = 0
+    self.desiredPose[4] = 0
+    self.desiredPose[5] = 0
+    self.desiredPose[6] = 0
+    target_pose = np.array(self.desiredPose)
+
+    np.savetxt("pb_target_pose.csv", target_pose, delimiter=",")
+
+    #print("set_action: desiredPose=", self.desiredPose)
+
+  def log_val(self, agent_id, val):
+    pass
+
+  def update(self, timeStep):
+    #print("pybullet_deep_mimic_env:update timeStep=",timeStep," t=",self.t)
+    self._pybullet_client.setTimeStep(timeStep)
+    self._humanoid._timeStep = timeStep
+
+    for i in range(1):
+      self.t += timeStep
+      self._humanoid.setSimTime(self.t)
+
+      if self.desiredPose:
+        kinPose = self._humanoid.computePose(self._humanoid._frameFraction)
+        self._humanoid.initializePose(self._humanoid._poseInterpolator,
+                                      self._humanoid._kin_model,
+                                      initBase=True)
+        #pos,orn=self._pybullet_client.getBasePositionAndOrientation(self._humanoid._sim_model)
+        #self._pybullet_client.resetBasePositionAndOrientation(self._humanoid._kin_model, [pos[0]+3,pos[1],pos[2]],orn)
+        #print("desiredPositions=",self.desiredPose)
+        maxForces = [
+            0, 0, 0, 0, 0, 0, 0, 200, 200, 200, 200, 50, 50, 50, 50, 200, 200, 200, 200, 150, 90,
+            90, 90, 90, 100, 100, 100, 100, 60, 200, 200, 200, 200, 150, 90, 90, 90, 90, 100, 100,
+            100, 100, 60
+        ]
+
+        if self._useStablePD:
+          taus = self._humanoid.computePDForces(self.desiredPose,
+                                                desiredVelocities=None,
+                                                maxForces=maxForces)
+          self._humanoid.applyPDForces(taus)
+        else:
+          self._humanoid.setJointMotors(self.desiredPose, maxForces=maxForces)
+
+        self._pybullet_client.stepSimulation()
+
+  def set_sample_count(self, count):
+    return
+
+  def check_terminate(self, agent_id):
+    return Env.Terminate(self.is_episode_end())
+
+  def is_episode_end(self):
+    isEnded = self._humanoid.terminates()
+    #also check maximum time, 20 seconds (todo get from file)
+    #print("self.t=",self.t)
+    if (self.t > 20):
+      isEnded = True
+    return isEnded
+
+  def check_valid_episode(self):
+    #could check if limbs exceed velocity threshold
+    return true
+
+  def getKeyboardEvents(self):
+    return self._pybullet_client.getKeyboardEvents()
+
+  def isKeyTriggered(self, keys, key):
+    o = ord(key)
+    #print("ord=",o)
+    if o in keys:
+      return keys[ord(key)] & self._pybullet_client.KEY_WAS_TRIGGERED
+    return False
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadrupedPoseInterpolator.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadrupedPoseInterpolator.py
index 9aa6d2652..49a05b003 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadrupedPoseInterpolator.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadrupedPoseInterpolator.py
@@ -1,54 +1,66 @@
 from pybullet_utils import bullet_client
 import math
-	
+
+
 class QuadrupedPoseInterpolator(object):
+
   def __init__(self):
     pass
-    
-    
-  def ComputeLinVel(self,posStart, posEnd, deltaTime):
-    vel = [(posEnd[0]-posStart[0])/deltaTime,(posEnd[1]-posStart[1])/deltaTime,(posEnd[2]-posStart[2])/deltaTime]
+
+  def ComputeLinVel(self, posStart, posEnd, deltaTime):
+    vel = [(posEnd[0] - posStart[0]) / deltaTime, (posEnd[1] - posStart[1]) / deltaTime,
+           (posEnd[2] - posStart[2]) / deltaTime]
     return vel
-  
-  def ComputeAngVel(self,ornStart, ornEnd, deltaTime, bullet_client):
-    dorn = bullet_client.getDifferenceQuaternion(ornStart,ornEnd)
-    axis,angle = bullet_client.getAxisAngleFromQuaternion(dorn)
-    angVel = [(axis[0]*angle)/deltaTime,(axis[1]*angle)/deltaTime,(axis[2]*angle)/deltaTime]
+
+  def ComputeAngVel(self, ornStart, ornEnd, deltaTime, bullet_client):
+    dorn = bullet_client.getDifferenceQuaternion(ornStart, ornEnd)
+    axis, angle = bullet_client.getAxisAngleFromQuaternion(dorn)
+    angVel = [(axis[0] * angle) / deltaTime, (axis[1] * angle) / deltaTime,
+              (axis[2] * angle) / deltaTime]
     return angVel
-  
-  def ComputeAngVelRel(self,ornStart, ornEnd, deltaTime, bullet_client):
-    ornStartConjugate = [-ornStart[0],-ornStart[1],-ornStart[2],ornStart[3]]
-    pos_diff, q_diff =bullet_client.multiplyTransforms([0,0,0], ornStartConjugate, [0,0,0], ornEnd)
-    axis,angle = bullet_client.getAxisAngleFromQuaternion(q_diff)
-    angVel = [(axis[0]*angle)/deltaTime,(axis[1]*angle)/deltaTime,(axis[2]*angle)/deltaTime]
+
+  def ComputeAngVelRel(self, ornStart, ornEnd, deltaTime, bullet_client):
+    ornStartConjugate = [-ornStart[0], -ornStart[1], -ornStart[2], ornStart[3]]
+    pos_diff, q_diff = bullet_client.multiplyTransforms([0, 0, 0], ornStartConjugate, [0, 0, 0],
+                                                        ornEnd)
+    axis, angle = bullet_client.getAxisAngleFromQuaternion(q_diff)
+    angVel = [(axis[0] * angle) / deltaTime, (axis[1] * angle) / deltaTime,
+              (axis[2] * angle) / deltaTime]
     return angVel
-  
-  def Slerp(self, frameFraction, frameData, frameDataNext,bullet_client ):
+
+  def Slerp(self, frameFraction, frameData, frameDataNext, bullet_client):
     keyFrameDuration = frameData[0]
-    basePos1Start = [frameData[1],frameData[2],frameData[3]]
-    basePos1End = [frameDataNext[1],frameDataNext[2],frameDataNext[3]]
-    self._basePos = [basePos1Start[0]+frameFraction*(basePos1End[0]-basePos1Start[0]), 
-      basePos1Start[1]+frameFraction*(basePos1End[1]-basePos1Start[1]), 
-      basePos1Start[2]+frameFraction*(basePos1End[2]-basePos1Start[2])]
-    self._baseLinVel = self.ComputeLinVel(basePos1Start,basePos1End, keyFrameDuration)
-    baseOrn1Start = [frameData[5],frameData[6], frameData[7],frameData[4]]
-    baseOrn1Next = [frameDataNext[5],frameDataNext[6], frameDataNext[7],frameDataNext[4]]
-    self._baseOrn = bullet_client.getQuaternionSlerp(baseOrn1Start,baseOrn1Next,frameFraction)
-    self._baseAngVel = self.ComputeAngVel(baseOrn1Start,baseOrn1Next, keyFrameDuration, bullet_client)
-    
-    jointPositions=[self._basePos[0],self._basePos[1],self._basePos[2],
-       self._baseOrn[0],self._baseOrn[1],self._baseOrn[2],self._baseOrn[3]]
-    jointVelocities=[self._baseLinVel[0],self._baseLinVel[1],self._baseLinVel[2],
-      self._baseAngVel[0],self._baseAngVel[1],self._baseAngVel[2]]
-    
-    for j in range (12):
-      index=j+8
-      jointPosStart=frameData[index]
-      jointPosEnd=frameDataNext[index]
-      jointPos=jointPosStart+frameFraction*(jointPosEnd-jointPosStart)
-      jointVel=(jointPosEnd-jointPosStart)/keyFrameDuration
+    basePos1Start = [frameData[1], frameData[2], frameData[3]]
+    basePos1End = [frameDataNext[1], frameDataNext[2], frameDataNext[3]]
+    self._basePos = [
+        basePos1Start[0] + frameFraction * (basePos1End[0] - basePos1Start[0]),
+        basePos1Start[1] + frameFraction * (basePos1End[1] - basePos1Start[1]),
+        basePos1Start[2] + frameFraction * (basePos1End[2] - basePos1Start[2])
+    ]
+    self._baseLinVel = self.ComputeLinVel(basePos1Start, basePos1End, keyFrameDuration)
+    baseOrn1Start = [frameData[5], frameData[6], frameData[7], frameData[4]]
+    baseOrn1Next = [frameDataNext[5], frameDataNext[6], frameDataNext[7], frameDataNext[4]]
+    self._baseOrn = bullet_client.getQuaternionSlerp(baseOrn1Start, baseOrn1Next, frameFraction)
+    self._baseAngVel = self.ComputeAngVel(baseOrn1Start, baseOrn1Next, keyFrameDuration,
+                                          bullet_client)
+
+    jointPositions = [
+        self._basePos[0], self._basePos[1], self._basePos[2], self._baseOrn[0], self._baseOrn[1],
+        self._baseOrn[2], self._baseOrn[3]
+    ]
+    jointVelocities = [
+        self._baseLinVel[0], self._baseLinVel[1], self._baseLinVel[2], self._baseAngVel[0],
+        self._baseAngVel[1], self._baseAngVel[2]
+    ]
+
+    for j in range(12):
+      index = j + 8
+      jointPosStart = frameData[index]
+      jointPosEnd = frameDataNext[index]
+      jointPos = jointPosStart + frameFraction * (jointPosEnd - jointPosStart)
+      jointVel = (jointPosEnd - jointPosStart) / keyFrameDuration
       jointPositions.append(jointPos)
       jointVelocities.append(jointVel)
     self._jointPositions = jointPositions
     self._jointVelocities = jointVelocities
-    return jointPositions,jointVelocities
+    return jointPositions, jointVelocities
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadruped_stable_pd.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadruped_stable_pd.py
index ce66fb462..66f2f70b2 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadruped_stable_pd.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/quadruped_stable_pd.py
@@ -1,127 +1,209 @@
-
-from pybullet_utils import pd_controller_stable 
+from pybullet_utils import pd_controller_stable
 from pybullet_envs.deep_mimic.env import quadruped_pose_interpolator
 import math
 
 
 class QuadrupedStablePD(object):
+
   def __init__(self, pybullet_client, mocap_data, timeStep, useFixedBase=True):
     self._pybullet_client = pybullet_client
     self._mocap_data = mocap_data
     print("LOADING quadruped!")
- 
-    startPos=[0.007058990464444105, 0.03149299192130908, 0.4918981912395484]
-    startOrn=[0.005934649695708604, 0.7065453990917289, 0.7076373820553712, -0.0027774940359030264]
-    self._sim_model = self._pybullet_client.loadURDF("laikago/laikago.urdf",startPos,startOrn, flags = urdfFlags,useFixedBase=False)
-    self._pybullet_client.resetBasePositionAndOrientation(_sim_model,startPos,startOrn)
-   
+
+    startPos = [0.007058990464444105, 0.03149299192130908, 0.4918981912395484]
+    startOrn = [
+        0.005934649695708604, 0.7065453990917289, 0.7076373820553712, -0.0027774940359030264
+    ]
+    self._sim_model = self._pybullet_client.loadURDF("laikago/laikago.urdf",
+                                                     startPos,
+                                                     startOrn,
+                                                     flags=urdfFlags,
+                                                     useFixedBase=False)
+    self._pybullet_client.resetBasePositionAndOrientation(_sim_model, startPos, startOrn)
+
     self._end_effectors = []  #ankle and wrist, both left and right
 
-    self._kin_model = self._pybullet_client.loadURDF("laikago/laikago.urdf",startPos,startOrn,useFixedBase=True)
-    
+    self._kin_model = self._pybullet_client.loadURDF("laikago/laikago.urdf",
+                                                     startPos,
+                                                     startOrn,
+                                                     useFixedBase=True)
+
     self._pybullet_client.changeDynamics(self._sim_model, -1, lateralFriction=0.9)
-    for j in range (self._pybullet_client.getNumJoints(self._sim_model)):
+    for j in range(self._pybullet_client.getNumJoints(self._sim_model)):
       self._pybullet_client.changeDynamics(self._sim_model, j, lateralFriction=0.9)
-      
+
     self._pybullet_client.changeDynamics(self._sim_model, -1, linearDamping=0, angularDamping=0)
     self._pybullet_client.changeDynamics(self._kin_model, -1, linearDamping=0, angularDamping=0)
-    
+
     #todo: add feature to disable simulation for a particular object. Until then, disable all collisions
-    self._pybullet_client.setCollisionFilterGroupMask(self._kin_model,-1,collisionFilterGroup=0,collisionFilterMask=0)
-    self._pybullet_client.changeDynamics(self._kin_model,-1,activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP+self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING+self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP)
+    self._pybullet_client.setCollisionFilterGroupMask(self._kin_model,
+                                                      -1,
+                                                      collisionFilterGroup=0,
+                                                      collisionFilterMask=0)
+    self._pybullet_client.changeDynamics(
+        self._kin_model,
+        -1,
+        activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP +
+        self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING +
+        self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP)
     alpha = 0.4
-    self._pybullet_client.changeVisualShape(self._kin_model,-1, rgbaColor=[1,1,1,alpha])
-    for j in range (self._pybullet_client.getNumJoints(self._kin_model)):
-      self._pybullet_client.setCollisionFilterGroupMask(self._kin_model,j,collisionFilterGroup=0,collisionFilterMask=0)
-      self._pybullet_client.changeDynamics(self._kin_model,j,activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP+self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING+self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP)
-      self._pybullet_client.changeVisualShape(self._kin_model,j, rgbaColor=[1,1,1,alpha])
-        
+    self._pybullet_client.changeVisualShape(self._kin_model, -1, rgbaColor=[1, 1, 1, alpha])
+    for j in range(self._pybullet_client.getNumJoints(self._kin_model)):
+      self._pybullet_client.setCollisionFilterGroupMask(self._kin_model,
+                                                        j,
+                                                        collisionFilterGroup=0,
+                                                        collisionFilterMask=0)
+      self._pybullet_client.changeDynamics(
+          self._kin_model,
+          j,
+          activationState=self._pybullet_client.ACTIVATION_STATE_SLEEP +
+          self._pybullet_client.ACTIVATION_STATE_ENABLE_SLEEPING +
+          self._pybullet_client.ACTIVATION_STATE_DISABLE_WAKEUP)
+      self._pybullet_client.changeVisualShape(self._kin_model, j, rgbaColor=[1, 1, 1, alpha])
+
     self._poseInterpolator = humanoid_pose_interpolator.HumanoidPoseInterpolator()
-    
-    for i in range (self._mocap_data.NumFrames()-1):
+
+    for i in range(self._mocap_data.NumFrames() - 1):
       frameData = self._mocap_data._motion_data['Frames'][i]
       self._poseInterpolator.PostProcessMotionData(frameData)
-      
+
     self._stablePD = pd_controller_stable.PDControllerStableMultiDof(self._pybullet_client)
     self._timeStep = timeStep
     #todo: kp/pd
-    self._kpOrg = [0,0,0,0,0,0,0,1000,1000,1000,1000,100,100,100,100,500,500,500,500,500,400,400,400,400,400,400,400,400,300,500,500,500,500,500,400,400,400,400,400,400,400,400,300]
-    self._kdOrg = [0,0,0,0,0,0,0,100,100,100,100,10,10,10,10,50,50,50,50,50,40,40,40,40,40,40,40,40,30,50,50,50,50,50,40,40,40,40,40,40,40,40,30]
-   
-    self._jointIndicesAll = [chest,neck, rightHip,rightKnee,rightAnkle,rightShoulder,rightElbow,leftHip,leftKnee,leftAnkle,leftShoulder,leftElbow]
+    self._kpOrg = [
+        0, 0, 0, 0, 0, 0, 0, 1000, 1000, 1000, 1000, 100, 100, 100, 100, 500, 500, 500, 500, 500,
+        400, 400, 400, 400, 400, 400, 400, 400, 300, 500, 500, 500, 500, 500, 400, 400, 400, 400,
+        400, 400, 400, 400, 300
+    ]
+    self._kdOrg = [
+        0, 0, 0, 0, 0, 0, 0, 100, 100, 100, 100, 10, 10, 10, 10, 50, 50, 50, 50, 50, 40, 40, 40,
+        40, 40, 40, 40, 40, 30, 50, 50, 50, 50, 50, 40, 40, 40, 40, 40, 40, 40, 40, 30
+    ]
+
+    self._jointIndicesAll = [
+        chest, neck, rightHip, rightKnee, rightAnkle, rightShoulder, rightElbow, leftHip, leftKnee,
+        leftAnkle, leftShoulder, leftElbow
+    ]
     for j in self._jointIndicesAll:
       #self._pybullet_client.setJointMotorControlMultiDof(self._sim_model, j, self._pybullet_client.POSITION_CONTROL, force=[1,1,1])
-      self._pybullet_client.setJointMotorControl2(self._sim_model, j, self._pybullet_client.POSITION_CONTROL, targetPosition=0, positionGain=0, targetVelocity=0,force=jointFrictionForce)
-      self._pybullet_client.setJointMotorControlMultiDof(self._sim_model, j, self._pybullet_client.POSITION_CONTROL,targetPosition=[0,0,0,1], targetVelocity=[0,0,0], positionGain=0,velocityGain=1,force=[jointFrictionForce,jointFrictionForce,jointFrictionForce])
-      self._pybullet_client.setJointMotorControl2(self._kin_model, j, self._pybullet_client.POSITION_CONTROL, targetPosition=0, positionGain=0, targetVelocity=0,force=0)
-      self._pybullet_client.setJointMotorControlMultiDof(self._kin_model, j, self._pybullet_client.POSITION_CONTROL,targetPosition=[0,0,0,1], targetVelocity=[0,0,0], positionGain=0,velocityGain=1,force=[jointFrictionForce,jointFrictionForce,0])
-    
-    self._jointDofCounts=[4,4,4,1,4,4,1,4,1,4,4,1]
-    
+      self._pybullet_client.setJointMotorControl2(self._sim_model,
+                                                  j,
+                                                  self._pybullet_client.POSITION_CONTROL,
+                                                  targetPosition=0,
+                                                  positionGain=0,
+                                                  targetVelocity=0,
+                                                  force=jointFrictionForce)
+      self._pybullet_client.setJointMotorControlMultiDof(
+          self._sim_model,
+          j,
+          self._pybullet_client.POSITION_CONTROL,
+          targetPosition=[0, 0, 0, 1],
+          targetVelocity=[0, 0, 0],
+          positionGain=0,
+          velocityGain=1,
+          force=[jointFrictionForce, jointFrictionForce, jointFrictionForce])
+      self._pybullet_client.setJointMotorControl2(self._kin_model,
+                                                  j,
+                                                  self._pybullet_client.POSITION_CONTROL,
+                                                  targetPosition=0,
+                                                  positionGain=0,
+                                                  targetVelocity=0,
+                                                  force=0)
+      self._pybullet_client.setJointMotorControlMultiDof(
+          self._kin_model,
+          j,
+          self._pybullet_client.POSITION_CONTROL,
+          targetPosition=[0, 0, 0, 1],
+          targetVelocity=[0, 0, 0],
+          positionGain=0,
+          velocityGain=1,
+          force=[jointFrictionForce, jointFrictionForce, 0])
+
+    self._jointDofCounts = [4, 4, 4, 1, 4, 4, 1, 4, 1, 4, 4, 1]
+
     #only those body parts/links are allowed to touch the ground, otherwise the episode terminates
-    self._allowed_body_parts=[5,11]
-    
+    self._allowed_body_parts = [5, 11]
+
     #[x,y,z] base position and [x,y,z,w] base orientation!
-    self._totalDofs = 7 
+    self._totalDofs = 7
     for dof in self._jointDofCounts:
       self._totalDofs += dof
     self.setSimTime(0)
-    
+
     self.resetPose()
-    
+
   def resetPose(self):
     #print("resetPose with self._frame=", self._frame, " and self._frameFraction=",self._frameFraction)
     pose = self.computePose(self._frameFraction)
     self.initializePose(self._poseInterpolator, self._sim_model, initBase=True)
     self.initializePose(self._poseInterpolator, self._kin_model, initBase=False)
-    
-  def initializePose(self,  pose, phys_model,initBase, initializeVelocity = True):
-    
+
+  def initializePose(self, pose, phys_model, initBase, initializeVelocity=True):
+
     if initializeVelocity:
       if initBase:
-        self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos, pose._baseOrn)
+        self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos,
+                                                              pose._baseOrn)
         self._pybullet_client.resetBaseVelocity(phys_model, pose._baseLinVel, pose._baseAngVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,chest,pose._chestRot, pose._chestVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,neck,pose._neckRot, pose._neckVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightHip,pose._rightHipRot, pose._rightHipVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightKnee,pose._rightKneeRot, pose._rightKneeVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightAnkle,pose._rightAnkleRot, pose._rightAnkleVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightShoulder,pose._rightShoulderRot, pose._rightShoulderVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightElbow, pose._rightElbowRot, pose._rightElbowVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftHip, pose._leftHipRot, pose._leftHipVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftKnee, pose._leftKneeRot, pose._leftKneeVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftAnkle, pose._leftAnkleRot, pose._leftAnkleVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftShoulder, pose._leftShoulderRot, pose._leftShoulderVel)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftElbow, pose._leftElbowRot, pose._leftElbowVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, chest, pose._chestRot,
+                                                    pose._chestVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, neck, pose._neckRot, pose._neckVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightHip, pose._rightHipRot,
+                                                    pose._rightHipVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightKnee, pose._rightKneeRot,
+                                                    pose._rightKneeVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightAnkle, pose._rightAnkleRot,
+                                                    pose._rightAnkleVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightShoulder,
+                                                    pose._rightShoulderRot, pose._rightShoulderVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightElbow, pose._rightElbowRot,
+                                                    pose._rightElbowVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftHip, pose._leftHipRot,
+                                                    pose._leftHipVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftKnee, pose._leftKneeRot,
+                                                    pose._leftKneeVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftAnkle, pose._leftAnkleRot,
+                                                    pose._leftAnkleVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftShoulder,
+                                                    pose._leftShoulderRot, pose._leftShoulderVel)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftElbow, pose._leftElbowRot,
+                                                    pose._leftElbowVel)
     else:
       if initBase:
-        self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos, pose._baseOrn)
-      self._pybullet_client.resetJointStateMultiDof(phys_model,chest,pose._chestRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,neck,pose._neckRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightHip,pose._rightHipRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightKnee,pose._rightKneeRot, [0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightAnkle,pose._rightAnkleRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightShoulder,pose._rightShoulderRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,rightElbow, pose._rightElbowRot, [0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftHip, pose._leftHipRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftKnee, pose._leftKneeRot, [0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftAnkle, pose._leftAnkleRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftShoulder, pose._leftShoulderRot, [0,0,0])
-      self._pybullet_client.resetJointStateMultiDof(phys_model,leftElbow, pose._leftElbowRot, [0])    
+        self._pybullet_client.resetBasePositionAndOrientation(phys_model, pose._basePos,
+                                                              pose._baseOrn)
+      self._pybullet_client.resetJointStateMultiDof(phys_model, chest, pose._chestRot, [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, neck, pose._neckRot, [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightHip, pose._rightHipRot,
+                                                    [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightKnee, pose._rightKneeRot, [0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightAnkle, pose._rightAnkleRot,
+                                                    [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightShoulder,
+                                                    pose._rightShoulderRot, [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, rightElbow, pose._rightElbowRot,
+                                                    [0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftHip, pose._leftHipRot,
+                                                    [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftKnee, pose._leftKneeRot, [0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftAnkle, pose._leftAnkleRot,
+                                                    [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftShoulder,
+                                                    pose._leftShoulderRot, [0, 0, 0])
+      self._pybullet_client.resetJointStateMultiDof(phys_model, leftElbow, pose._leftElbowRot, [0])
 
   def calcCycleCount(self, simTime, cycleTime):
-    phases = simTime / cycleTime;
+    phases = simTime / cycleTime
     count = math.floor(phases)
     loop = True
     #count = (loop) ? count : cMathUtil::Clamp(count, 0, 1);
     return count
-    
 
   def getCycleTime(self):
     keyFrameDuration = self._mocap_data.KeyFrameDuraction()
-    cycleTime = keyFrameDuration*(self._mocap_data.NumFrames()-1)
+    cycleTime = keyFrameDuration * (self._mocap_data.NumFrames() - 1)
     return cycleTime
-    
+
   def setSimTime(self, t):
     self._simTime = t
     #print("SetTimeTime time =",t)
@@ -130,163 +212,266 @@ class QuadrupedStablePD(object):
     #print("self._motion_data.NumFrames()=",self._mocap_data.NumFrames())
     self._cycleCount = self.calcCycleCount(t, cycleTime)
     #print("cycles=",cycles)
-    frameTime = t - self._cycleCount*cycleTime
-    if (frameTime<0):
+    frameTime = t - self._cycleCount * cycleTime
+    if (frameTime < 0):
       frameTime += cycleTime
-    
-    #print("keyFrameDuration=",keyFrameDuration)  
+
+    #print("keyFrameDuration=",keyFrameDuration)
     #print("frameTime=",frameTime)
-    self._frame = int(frameTime/keyFrameDuration)
+    self._frame = int(frameTime / keyFrameDuration)
     #print("self._frame=",self._frame)
-    
-    self._frameNext = self._frame+1
-    if (self._frameNext >=  self._mocap_data.NumFrames()):
+
+    self._frameNext = self._frame + 1
+    if (self._frameNext >= self._mocap_data.NumFrames()):
       self._frameNext = self._frame
 
-    self._frameFraction = (frameTime - self._frame*keyFrameDuration)/(keyFrameDuration)
-    
-    
+    self._frameFraction = (frameTime - self._frame * keyFrameDuration) / (keyFrameDuration)
+
   def computeCycleOffset(self):
-    firstFrame=0
-    lastFrame = self._mocap_data.NumFrames()-1
+    firstFrame = 0
+    lastFrame = self._mocap_data.NumFrames() - 1
     frameData = self._mocap_data._motion_data['Frames'][0]
     frameDataNext = self._mocap_data._motion_data['Frames'][lastFrame]
-    
-    basePosStart = [frameData[1],frameData[2],frameData[3]]
-    basePosEnd = [frameDataNext[1],frameDataNext[2],frameDataNext[3]]
-    self._cycleOffset = [basePosEnd[0]-basePosStart[0],basePosEnd[1]-basePosStart[1],basePosEnd[2]-basePosStart[2]]
+
+    basePosStart = [frameData[1], frameData[2], frameData[3]]
+    basePosEnd = [frameDataNext[1], frameDataNext[2], frameDataNext[3]]
+    self._cycleOffset = [
+        basePosEnd[0] - basePosStart[0], basePosEnd[1] - basePosStart[1],
+        basePosEnd[2] - basePosStart[2]
+    ]
     return self._cycleOffset
-    
+
   def computePose(self, frameFraction):
     frameData = self._mocap_data._motion_data['Frames'][self._frame]
     frameDataNext = self._mocap_data._motion_data['Frames'][self._frameNext]
-    
+
     self._poseInterpolator.Slerp(frameFraction, frameData, frameDataNext, self._pybullet_client)
     #print("self._poseInterpolator.Slerp(", frameFraction,")=", pose)
     self.computeCycleOffset()
     oldPos = self._poseInterpolator._basePos
-    self._poseInterpolator._basePos = [oldPos[0]+self._cycleCount*self._cycleOffset[0],oldPos[1]+self._cycleCount*self._cycleOffset[1],oldPos[2]+self._cycleCount*self._cycleOffset[2]]
+    self._poseInterpolator._basePos = [
+        oldPos[0] + self._cycleCount * self._cycleOffset[0],
+        oldPos[1] + self._cycleCount * self._cycleOffset[1],
+        oldPos[2] + self._cycleCount * self._cycleOffset[2]
+    ]
     pose = self._poseInterpolator.GetPose()
-    
-    return pose
 
+    return pose
 
   def convertActionToPose(self, action):
     pose = self._poseInterpolator.ConvertFromAction(self._pybullet_client, action)
-    return pose      
+    return pose
 
   def computePDForces(self, desiredPositions, desiredVelocities, maxForces):
-    if desiredVelocities==None:
-      desiredVelocities = [0]*self._totalDofs
-      
-      
-    taus = self._stablePD.computePD(bodyUniqueId=self._sim_model, 
-      jointIndices = self._jointIndicesAll,
-      desiredPositions = desiredPositions,
-      desiredVelocities = desiredVelocities,
-      kps = self._kpOrg,
-      kds = self._kdOrg,
-      maxForces = maxForces, 
-      timeStep=self._timeStep)
+    if desiredVelocities == None:
+      desiredVelocities = [0] * self._totalDofs
+
+    taus = self._stablePD.computePD(bodyUniqueId=self._sim_model,
+                                    jointIndices=self._jointIndicesAll,
+                                    desiredPositions=desiredPositions,
+                                    desiredVelocities=desiredVelocities,
+                                    kps=self._kpOrg,
+                                    kds=self._kdOrg,
+                                    maxForces=maxForces,
+                                    timeStep=self._timeStep)
     return taus
 
   def applyPDForces(self, taus):
-    dofIndex=7
+    dofIndex = 7
     scaling = 1
-    for index in range (len(self._jointIndicesAll)):
+    for index in range(len(self._jointIndicesAll)):
       jointIndex = self._jointIndicesAll[index]
-      if self._jointDofCounts[index]==4:
-         force=[scaling*taus[dofIndex+0],scaling*taus[dofIndex+1],scaling*taus[dofIndex+2]]
-         #print("force[", jointIndex,"]=",force)
-         self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,jointIndex,self._pybullet_client.TORQUE_CONTROL,force=force)
-      if self._jointDofCounts[index]==1:
-         force=[scaling*taus[dofIndex]]
-         #print("force[", jointIndex,"]=",force)
-         self._pybullet_client.setJointMotorControlMultiDof(self._sim_model, jointIndex, controlMode=self._pybullet_client.TORQUE_CONTROL, force=force)
-      dofIndex+=self._jointDofCounts[index]
-      
+      if self._jointDofCounts[index] == 4:
+        force = [
+            scaling * taus[dofIndex + 0], scaling * taus[dofIndex + 1],
+            scaling * taus[dofIndex + 2]
+        ]
+        #print("force[", jointIndex,"]=",force)
+        self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,
+                                                           jointIndex,
+                                                           self._pybullet_client.TORQUE_CONTROL,
+                                                           force=force)
+      if self._jointDofCounts[index] == 1:
+        force = [scaling * taus[dofIndex]]
+        #print("force[", jointIndex,"]=",force)
+        self._pybullet_client.setJointMotorControlMultiDof(
+            self._sim_model,
+            jointIndex,
+            controlMode=self._pybullet_client.TORQUE_CONTROL,
+            force=force)
+      dofIndex += self._jointDofCounts[index]
+
   def setJointMotors(self, desiredPositions, maxForces):
     controlMode = self._pybullet_client.POSITION_CONTROL
-    startIndex=7
-    chest=1
-    neck=2
-    rightHip=3
-    rightKnee=4
-    rightAnkle=5
-    rightShoulder=6
-    rightElbow=7
-    leftHip=9
-    leftKnee=10
-    leftAnkle=11
-    leftShoulder=12
-    leftElbow=13
+    startIndex = 7
+    chest = 1
+    neck = 2
+    rightHip = 3
+    rightKnee = 4
+    rightAnkle = 5
+    rightShoulder = 6
+    rightElbow = 7
+    leftHip = 9
+    leftKnee = 10
+    leftAnkle = 11
+    leftShoulder = 12
+    leftElbow = 13
     kp = 0.2
-    
-    forceScale=1
+
+    forceScale = 1
     #self._jointDofCounts=[4,4,4,1,4,4,1,4,1,4,4,1]
-    maxForce = [forceScale*maxForces[startIndex],forceScale*maxForces[startIndex+1],forceScale*maxForces[startIndex+2],forceScale*maxForces[startIndex+3]]
-    startIndex+=4
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,chest,controlMode, targetPosition=self._poseInterpolator._chestRot,positionGain=kp, force=maxForce)
-    maxForce =  [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]]
-    startIndex+=4
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,neck,controlMode,targetPosition=self._poseInterpolator._neckRot,positionGain=kp, force=maxForce)
-    maxForce =  [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]]
-    startIndex+=4
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightHip,controlMode,targetPosition=self._poseInterpolator._rightHipRot,positionGain=kp, force=maxForce)
-    maxForce = [forceScale*maxForces[startIndex]]
-    startIndex+=1
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightKnee,controlMode,targetPosition=self._poseInterpolator._rightKneeRot,positionGain=kp, force=maxForce)
-    maxForce =  [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]]
-    startIndex+=4
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightAnkle,controlMode,targetPosition=self._poseInterpolator._rightAnkleRot,positionGain=kp, force=maxForce)
-    maxForce = [forceScale*maxForces[startIndex],forceScale*maxForces[startIndex+1],forceScale*maxForces[startIndex+2],forceScale*maxForces[startIndex+3]]
-    startIndex+=4
-    maxForce = [forceScale*maxForces[startIndex]]
-    startIndex+=1
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,rightElbow, controlMode,targetPosition=self._poseInterpolator._rightElbowRot,positionGain=kp, force=maxForce)
-    maxForce =  [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]]
-    startIndex+=4
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftHip, controlMode,targetPosition=self._poseInterpolator._leftHipRot,positionGain=kp, force=maxForce)
-    maxForce = [forceScale*maxForces[startIndex]]
-    startIndex+=1
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftKnee, controlMode,targetPosition=self._poseInterpolator._leftKneeRot,positionGain=kp, force=maxForce)
-    maxForce =  [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]]
-    startIndex+=4
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftAnkle, controlMode,targetPosition=self._poseInterpolator._leftAnkleRot,positionGain=kp, force=maxForce)
-    maxForce =  [maxForces[startIndex],maxForces[startIndex+1],maxForces[startIndex+2],maxForces[startIndex+3]]
-    startIndex+=4
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftShoulder, controlMode,targetPosition=self._poseInterpolator._leftShoulderRot,positionGain=kp, force=maxForce)
-    maxForce = [forceScale*maxForces[startIndex]]
-    startIndex+=1
-    self._pybullet_client.setJointMotorControlMultiDof(self._sim_model,leftElbow, controlMode,targetPosition=self._poseInterpolator._leftElbowRot,positionGain=kp, force=maxForce)
+    maxForce = [
+        forceScale * maxForces[startIndex], forceScale * maxForces[startIndex + 1],
+        forceScale * maxForces[startIndex + 2], forceScale * maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        chest,
+        controlMode,
+        targetPosition=self._poseInterpolator._chestRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [
+        maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2],
+        maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        neck,
+        controlMode,
+        targetPosition=self._poseInterpolator._neckRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [
+        maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2],
+        maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        rightHip,
+        controlMode,
+        targetPosition=self._poseInterpolator._rightHipRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [forceScale * maxForces[startIndex]]
+    startIndex += 1
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        rightKnee,
+        controlMode,
+        targetPosition=self._poseInterpolator._rightKneeRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [
+        maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2],
+        maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        rightAnkle,
+        controlMode,
+        targetPosition=self._poseInterpolator._rightAnkleRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [
+        forceScale * maxForces[startIndex], forceScale * maxForces[startIndex + 1],
+        forceScale * maxForces[startIndex + 2], forceScale * maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    maxForce = [forceScale * maxForces[startIndex]]
+    startIndex += 1
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        rightElbow,
+        controlMode,
+        targetPosition=self._poseInterpolator._rightElbowRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [
+        maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2],
+        maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        leftHip,
+        controlMode,
+        targetPosition=self._poseInterpolator._leftHipRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [forceScale * maxForces[startIndex]]
+    startIndex += 1
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        leftKnee,
+        controlMode,
+        targetPosition=self._poseInterpolator._leftKneeRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [
+        maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2],
+        maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        leftAnkle,
+        controlMode,
+        targetPosition=self._poseInterpolator._leftAnkleRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [
+        maxForces[startIndex], maxForces[startIndex + 1], maxForces[startIndex + 2],
+        maxForces[startIndex + 3]
+    ]
+    startIndex += 4
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        leftShoulder,
+        controlMode,
+        targetPosition=self._poseInterpolator._leftShoulderRot,
+        positionGain=kp,
+        force=maxForce)
+    maxForce = [forceScale * maxForces[startIndex]]
+    startIndex += 1
+    self._pybullet_client.setJointMotorControlMultiDof(
+        self._sim_model,
+        leftElbow,
+        controlMode,
+        targetPosition=self._poseInterpolator._leftElbowRot,
+        positionGain=kp,
+        force=maxForce)
     #print("startIndex=",startIndex)
-      
+
   def getPhase(self):
     keyFrameDuration = self._mocap_data.KeyFrameDuraction()
-    cycleTime = keyFrameDuration*(self._mocap_data.NumFrames()-1)
+    cycleTime = keyFrameDuration * (self._mocap_data.NumFrames() - 1)
     phase = self._simTime / cycleTime
-    phase = math.fmod(phase,1.0)
-    if (phase<0):
+    phase = math.fmod(phase, 1.0)
+    if (phase < 0):
       phase += 1
     return phase
 
   def buildHeadingTrans(self, rootOrn):
     #align root transform 'forward' with world-space x axis
     eul = self._pybullet_client.getEulerFromQuaternion(rootOrn)
-    refDir = [1,0,0]
+    refDir = [1, 0, 0]
     rotVec = self._pybullet_client.rotateVector(rootOrn, refDir)
     heading = math.atan2(-rotVec[2], rotVec[0])
-    heading2=eul[1]
+    heading2 = eul[1]
     #print("heading=",heading)
-    headingOrn = self._pybullet_client.getQuaternionFromAxisAngle([0,1,0],-heading)
+    headingOrn = self._pybullet_client.getQuaternionFromAxisAngle([0, 1, 0], -heading)
     return headingOrn
 
-
   def buildOriginTrans(self):
-    rootPos,rootOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model)
-    
+    rootPos, rootOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model)
+
     #print("rootPos=",rootPos, " rootOrn=",rootOrn)
-    invRootPos=[-rootPos[0], 0, -rootPos[2]]
+    invRootPos = [-rootPos[0], 0, -rootPos[2]]
     #invOrigTransPos, invOrigTransOrn = self._pybullet_client.invertTransform(rootPos,rootOrn)
     headingOrn = self.buildHeadingTrans(rootOrn)
     #print("headingOrn=",headingOrn)
@@ -294,8 +479,11 @@ class QuadrupedStablePD(object):
     #print("headingMat=",headingMat)
     #dummy, rootOrnWithoutHeading = self._pybullet_client.multiplyTransforms([0,0,0],headingOrn, [0,0,0], rootOrn)
     #dummy, invOrigTransOrn = self._pybullet_client.multiplyTransforms([0,0,0],rootOrnWithoutHeading, invOrigTransPos, invOrigTransOrn)
-    
-    invOrigTransPos, invOrigTransOrn = self._pybullet_client.multiplyTransforms( [0,0,0],headingOrn, invRootPos,[0,0,0,1])
+
+    invOrigTransPos, invOrigTransOrn = self._pybullet_client.multiplyTransforms([0, 0, 0],
+                                                                                headingOrn,
+                                                                                invRootPos,
+                                                                                [0, 0, 0, 1])
     #print("invOrigTransPos=",invOrigTransPos)
     #print("invOrigTransOrn=",invOrigTransOrn)
     invOrigTransMat = self._pybullet_client.getMatrixFromQuaternion(invOrigTransOrn)
@@ -308,106 +496,115 @@ class QuadrupedStablePD(object):
     phase = self.getPhase()
     #print("phase=",phase)
     stateVector.append(phase)
-    
-    rootTransPos, rootTransOrn=self.buildOriginTrans()
-    basePos,baseOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model)
-    
-    rootPosRel, dummy = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, basePos,[0,0,0,1])
+
+    rootTransPos, rootTransOrn = self.buildOriginTrans()
+    basePos, baseOrn = self._pybullet_client.getBasePositionAndOrientation(self._sim_model)
+
+    rootPosRel, dummy = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn,
+                                                                 basePos, [0, 0, 0, 1])
     #print("!!!rootPosRel =",rootPosRel )
     #print("rootTransPos=",rootTransPos)
     #print("basePos=",basePos)
-    localPos,localOrn = self._pybullet_client.multiplyTransforms( rootTransPos, rootTransOrn , basePos,baseOrn )
-    
-    localPos=[localPos[0]-rootPosRel[0],localPos[1]-rootPosRel[1],localPos[2]-rootPosRel[2]]
+    localPos, localOrn = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn,
+                                                                  basePos, baseOrn)
+
+    localPos = [
+        localPos[0] - rootPosRel[0], localPos[1] - rootPosRel[1], localPos[2] - rootPosRel[2]
+    ]
     #print("localPos=",localPos)
-        
+
     stateVector.append(rootPosRel[1])
-    
+
     #self.pb2dmJoints=[0,1,2,9,10,11,3,4,5,12,13,14,6,7,8]
-    self.pb2dmJoints=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14]
-    
-    for pbJoint in range (self._pybullet_client.getNumJoints(self._sim_model)):
+    self.pb2dmJoints = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
+
+    for pbJoint in range(self._pybullet_client.getNumJoints(self._sim_model)):
       j = self.pb2dmJoints[pbJoint]
       #print("joint order:",j)
       ls = self._pybullet_client.getLinkState(self._sim_model, j, computeForwardKinematics=True)
       linkPos = ls[0]
       linkOrn = ls[1]
-      linkPosLocal, linkOrnLocal = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, linkPos,linkOrn)
-      if (linkOrnLocal[3]<0):
-        linkOrnLocal=[-linkOrnLocal[0],-linkOrnLocal[1],-linkOrnLocal[2],-linkOrnLocal[3]]
-      linkPosLocal=[linkPosLocal[0]-rootPosRel[0],linkPosLocal[1]-rootPosRel[1],linkPosLocal[2]-rootPosRel[2]]
+      linkPosLocal, linkOrnLocal = self._pybullet_client.multiplyTransforms(
+          rootTransPos, rootTransOrn, linkPos, linkOrn)
+      if (linkOrnLocal[3] < 0):
+        linkOrnLocal = [-linkOrnLocal[0], -linkOrnLocal[1], -linkOrnLocal[2], -linkOrnLocal[3]]
+      linkPosLocal = [
+          linkPosLocal[0] - rootPosRel[0], linkPosLocal[1] - rootPosRel[1],
+          linkPosLocal[2] - rootPosRel[2]
+      ]
       for l in linkPosLocal:
         stateVector.append(l)
       #re-order the quaternion, DeepMimic uses w,x,y,z
-      
-      if (linkOrnLocal[3]<0):
-        linkOrnLocal[0]*=-1
-        linkOrnLocal[1]*=-1
-        linkOrnLocal[2]*=-1
-        linkOrnLocal[3]*=-1
-        
+
+      if (linkOrnLocal[3] < 0):
+        linkOrnLocal[0] *= -1
+        linkOrnLocal[1] *= -1
+        linkOrnLocal[2] *= -1
+        linkOrnLocal[3] *= -1
+
       stateVector.append(linkOrnLocal[3])
       stateVector.append(linkOrnLocal[0])
       stateVector.append(linkOrnLocal[1])
       stateVector.append(linkOrnLocal[2])
-    
-    
-    for pbJoint in range (self._pybullet_client.getNumJoints(self._sim_model)):
+
+    for pbJoint in range(self._pybullet_client.getNumJoints(self._sim_model)):
       j = self.pb2dmJoints[pbJoint]
       ls = self._pybullet_client.getLinkState(self._sim_model, j, computeLinkVelocity=True)
       linkLinVel = ls[6]
       linkAngVel = ls[7]
-      linkLinVelLocal  , unused = self._pybullet_client.multiplyTransforms([0,0,0], rootTransOrn, linkLinVel,[0,0,0,1])
+      linkLinVelLocal, unused = self._pybullet_client.multiplyTransforms([0, 0, 0], rootTransOrn,
+                                                                         linkLinVel, [0, 0, 0, 1])
       #linkLinVelLocal=[linkLinVelLocal[0]-rootPosRel[0],linkLinVelLocal[1]-rootPosRel[1],linkLinVelLocal[2]-rootPosRel[2]]
-      linkAngVelLocal ,unused = self._pybullet_client.multiplyTransforms([0,0,0], rootTransOrn, linkAngVel,[0,0,0,1])
-      
+      linkAngVelLocal, unused = self._pybullet_client.multiplyTransforms([0, 0, 0], rootTransOrn,
+                                                                         linkAngVel, [0, 0, 0, 1])
+
       for l in linkLinVelLocal:
         stateVector.append(l)
       for l in linkAngVelLocal:
         stateVector.append(l)
-    
-    #print("stateVector len=",len(stateVector))  
+
+    #print("stateVector len=",len(stateVector))
     #for st in range (len(stateVector)):
     #  print("state[",st,"]=",stateVector[st])
     return stateVector
-  
+
   def terminates(self):
     #check if any non-allowed body part hits the ground
-    terminates=False
+    terminates = False
     pts = self._pybullet_client.getContactPoints()
     for p in pts:
       part = -1
       #ignore self-collision
-      if (p[1]==p[2]):
+      if (p[1] == p[2]):
         continue
-      if (p[1]==self._sim_model):
-        part=p[3]
-      if (p[2]==self._sim_model):
-        part=p[4]
-      if (part >=0 and part not in self._allowed_body_parts):
+      if (p[1] == self._sim_model):
+        part = p[3]
+      if (p[2] == self._sim_model):
+        part = p[4]
+      if (part >= 0 and part not in self._allowed_body_parts):
         #print("terminating part:", part)
-        terminates=True
-      
+        terminates = True
+
     return terminates
-    
+
   def quatMul(self, q1, q2):
-      return [ q1[3] * q2[0] + q1[0] * q2[3] + q1[1] * q2[2] - q1[2] * q2[1],
-               q1[3] * q2[1] + q1[1] * q2[3] + q1[2] * q2[0] - q1[0] * q2[2],
-              q1[3] * q2[2] + q1[2] * q2[3] + q1[0] * q2[1] - q1[1] * q2[0],
-              q1[3] * q2[3] - q1[0] * q2[0] - q1[1] * q2[1] - q1[2] * q2[2]]
-              
+    return [
+        q1[3] * q2[0] + q1[0] * q2[3] + q1[1] * q2[2] - q1[2] * q2[1],
+        q1[3] * q2[1] + q1[1] * q2[3] + q1[2] * q2[0] - q1[0] * q2[2],
+        q1[3] * q2[2] + q1[2] * q2[3] + q1[0] * q2[1] - q1[1] * q2[0],
+        q1[3] * q2[3] - q1[0] * q2[0] - q1[1] * q2[1] - q1[2] * q2[2]
+    ]
+
   def calcRootAngVelErr(self, vel0, vel1):
-    diff = [vel0[0]-vel1[0],vel0[1]-vel1[1], vel0[2]-vel1[2]]
-    return diff[0]*diff[0]+diff[1]*diff[1]+diff[2]*diff[2]
+    diff = [vel0[0] - vel1[0], vel0[1] - vel1[1], vel0[2] - vel1[2]]
+    return diff[0] * diff[0] + diff[1] * diff[1] + diff[2] * diff[2]
 
-  
-    
-  def calcRootRotDiff(self,orn0, orn1):
-    orn0Conj = [-orn0[0],-orn0[1],-orn0[2],orn0[3]]
+  def calcRootRotDiff(self, orn0, orn1):
+    orn0Conj = [-orn0[0], -orn0[1], -orn0[2], orn0[3]]
     q_diff = self.quatMul(orn1, orn0Conj)
-    axis,angle = self._pybullet_client.getAxisAngleFromQuaternion(q_diff)
-    return angle*angle
-  
+    axis, angle = self._pybullet_client.getAxisAngleFromQuaternion(q_diff)
+    return angle * angle
+
   def getReward(self, pose):
     #from DeepMimic double cSceneImitate::CalcRewardImitate
     #todo: compensate for ground height in some parts, once we move to non-flat terrain
@@ -415,7 +612,7 @@ class QuadrupedStablePD(object):
     vel_w = 0.05
     end_eff_w = 0.15
     root_w = 0.2
-    com_w = 0 #0.1
+    com_w = 0  #0.1
 
     total_w = pose_w + vel_w + end_eff_w + root_w + com_w
     pose_w /= total_w
@@ -439,9 +636,9 @@ class QuadrupedStablePD(object):
     root_err = 0
     com_err = 0
     heading_err = 0
-    
+
     #create a mimic reward, comparing the dynamics humanoid with a kinematic one
-    
+
     #pose = self.InitializePoseFromMotionData()
     #print("self._kin_model=",self._kin_model)
     #print("kinematicHumanoid #joints=",self._pybullet_client.getNumJoints(self._kin_model))
@@ -470,91 +667,102 @@ class QuadrupedStablePD(object):
     #tVector root_ang_vel0 = cKinTree::GetRootAngVel(joint_mat, vel0);
     #tVector root_ang_vel1 = cKinTree::GetRootAngVel(joint_mat, vel1);
 
-    mJointWeights = [0.20833,0.10416, 0.0625, 0.10416,
-      0.0625, 0.041666666666666671, 0.0625, 0.0416,
-      0.00, 0.10416,  0.0625, 0.0416, 0.0625, 0.0416, 0.0000]
-      
+    mJointWeights = [
+        0.20833, 0.10416, 0.0625, 0.10416, 0.0625, 0.041666666666666671, 0.0625, 0.0416, 0.00,
+        0.10416, 0.0625, 0.0416, 0.0625, 0.0416, 0.0000
+    ]
+
     num_end_effs = 0
     num_joints = 15
-    
+
     root_rot_w = mJointWeights[root_id]
-    rootPosSim,rootOrnSim = self._pybullet_client.getBasePositionAndOrientation(self._sim_model)
-    rootPosKin ,rootOrnKin = self._pybullet_client.getBasePositionAndOrientation(self._kin_model)
+    rootPosSim, rootOrnSim = self._pybullet_client.getBasePositionAndOrientation(self._sim_model)
+    rootPosKin, rootOrnKin = self._pybullet_client.getBasePositionAndOrientation(self._kin_model)
     linVelSim, angVelSim = self._pybullet_client.getBaseVelocity(self._sim_model)
     linVelKin, angVelKin = self._pybullet_client.getBaseVelocity(self._kin_model)
-    
-    
-    root_rot_err = self.calcRootRotDiff(rootOrnSim,rootOrnKin)
+
+    root_rot_err = self.calcRootRotDiff(rootOrnSim, rootOrnKin)
     pose_err += root_rot_w * root_rot_err
-    
-    
-    root_vel_diff  = [linVelSim[0]-linVelKin[0],linVelSim[1]-linVelKin[1],linVelSim[2]-linVelKin[2]]
-    root_vel_err = root_vel_diff[0]*root_vel_diff[0]+root_vel_diff[1]*root_vel_diff[1]+root_vel_diff[2]*root_vel_diff[2]
-    
-    root_ang_vel_err = self.calcRootAngVelErr( angVelSim, angVelKin)
+
+    root_vel_diff = [
+        linVelSim[0] - linVelKin[0], linVelSim[1] - linVelKin[1], linVelSim[2] - linVelKin[2]
+    ]
+    root_vel_err = root_vel_diff[0] * root_vel_diff[0] + root_vel_diff[1] * root_vel_diff[
+        1] + root_vel_diff[2] * root_vel_diff[2]
+
+    root_ang_vel_err = self.calcRootAngVelErr(angVelSim, angVelKin)
     vel_err += root_rot_w * root_ang_vel_err
 
-    for j in range (num_joints):
+    for j in range(num_joints):
       curr_pose_err = 0
       curr_vel_err = 0
-      w = mJointWeights[j];
-      
+      w = mJointWeights[j]
+
       simJointInfo = self._pybullet_client.getJointStateMultiDof(self._sim_model, j)
-      
+
       #print("simJointInfo.pos=",simJointInfo[0])
       #print("simJointInfo.vel=",simJointInfo[1])
-      kinJointInfo = self._pybullet_client.getJointStateMultiDof(self._kin_model,j)
+      kinJointInfo = self._pybullet_client.getJointStateMultiDof(self._kin_model, j)
       #print("kinJointInfo.pos=",kinJointInfo[0])
       #print("kinJointInfo.vel=",kinJointInfo[1])
-      if (len(simJointInfo[0])==1):
-        angle = simJointInfo[0][0]-kinJointInfo[0][0]
-        curr_pose_err = angle*angle
-        velDiff = simJointInfo[1][0]-kinJointInfo[1][0]
-        curr_vel_err = velDiff*velDiff
-      if (len(simJointInfo[0])==4):
+      if (len(simJointInfo[0]) == 1):
+        angle = simJointInfo[0][0] - kinJointInfo[0][0]
+        curr_pose_err = angle * angle
+        velDiff = simJointInfo[1][0] - kinJointInfo[1][0]
+        curr_vel_err = velDiff * velDiff
+      if (len(simJointInfo[0]) == 4):
         #print("quaternion diff")
-        diffQuat = self._pybullet_client.getDifferenceQuaternion(simJointInfo[0],kinJointInfo[0])
-        axis,angle = self._pybullet_client.getAxisAngleFromQuaternion(diffQuat)
-        curr_pose_err = angle*angle
-        diffVel = [simJointInfo[1][0]-kinJointInfo[1][0],simJointInfo[1][1]-kinJointInfo[1][1],simJointInfo[1][2]-kinJointInfo[1][2]]
-        curr_vel_err = diffVel[0]*diffVel[0]+diffVel[1]*diffVel[1]+diffVel[2]*diffVel[2]
-      
-      
+        diffQuat = self._pybullet_client.getDifferenceQuaternion(simJointInfo[0], kinJointInfo[0])
+        axis, angle = self._pybullet_client.getAxisAngleFromQuaternion(diffQuat)
+        curr_pose_err = angle * angle
+        diffVel = [
+            simJointInfo[1][0] - kinJointInfo[1][0], simJointInfo[1][1] - kinJointInfo[1][1],
+            simJointInfo[1][2] - kinJointInfo[1][2]
+        ]
+        curr_vel_err = diffVel[0] * diffVel[0] + diffVel[1] * diffVel[1] + diffVel[2] * diffVel[2]
+
       pose_err += w * curr_pose_err
       vel_err += w * curr_vel_err
-    
+
       is_end_eff = j in self._end_effectors
       if is_end_eff:
-        
+
         linkStateSim = self._pybullet_client.getLinkState(self._sim_model, j)
         linkStateKin = self._pybullet_client.getLinkState(self._kin_model, j)
         linkPosSim = linkStateSim[0]
         linkPosKin = linkStateKin[0]
-        linkPosDiff = [linkPosSim[0]-linkPosKin[0],linkPosSim[1]-linkPosKin[1],linkPosSim[2]-linkPosKin[2]]
-        curr_end_err = linkPosDiff[0]*linkPosDiff[0]+linkPosDiff[1]*linkPosDiff[1]+linkPosDiff[2]*linkPosDiff[2]
+        linkPosDiff = [
+            linkPosSim[0] - linkPosKin[0], linkPosSim[1] - linkPosKin[1],
+            linkPosSim[2] - linkPosKin[2]
+        ]
+        curr_end_err = linkPosDiff[0] * linkPosDiff[0] + linkPosDiff[1] * linkPosDiff[
+            1] + linkPosDiff[2] * linkPosDiff[2]
         end_eff_err += curr_end_err
-        num_end_effs+=1
-    
+        num_end_effs += 1
+
     if (num_end_effs > 0):
       end_eff_err /= num_end_effs
-    
+
     #double root_ground_h0 = mGround->SampleHeight(sim_char.GetRootPos())
     #double root_ground_h1 = kin_char.GetOriginPos()[1]
     #root_pos0[1] -= root_ground_h0
     #root_pos1[1] -= root_ground_h1
-    root_pos_diff = [rootPosSim[0]-rootPosKin[0],rootPosSim[1]-rootPosKin[1],rootPosSim[2]-rootPosKin[2]]
-    root_pos_err = root_pos_diff[0]*root_pos_diff[0]+root_pos_diff[1]*root_pos_diff[1]+root_pos_diff[2]*root_pos_diff[2]
-    #  
+    root_pos_diff = [
+        rootPosSim[0] - rootPosKin[0], rootPosSim[1] - rootPosKin[1], rootPosSim[2] - rootPosKin[2]
+    ]
+    root_pos_err = root_pos_diff[0] * root_pos_diff[0] + root_pos_diff[1] * root_pos_diff[
+        1] + root_pos_diff[2] * root_pos_diff[2]
+    #
     #root_rot_err = cMathUtil::QuatDiffTheta(root_rot0, root_rot1)
     #root_rot_err *= root_rot_err
 
     #root_vel_err = (root_vel1 - root_vel0).squaredNorm()
     #root_ang_vel_err = (root_ang_vel1 - root_ang_vel0).squaredNorm()
 
-    root_err = root_pos_err + 0.1 * root_rot_err+ 0.01 * root_vel_err+ 0.001 * root_ang_vel_err
-    
+    root_err = root_pos_err + 0.1 * root_rot_err + 0.01 * root_vel_err + 0.001 * root_ang_vel_err
+
     #com_err = 0.1 * (com_vel1_world - com_vel0_world).squaredNorm()
-    
+
     #print("pose_err=",pose_err)
     #print("vel_err=",vel_err)
     pose_reward = math.exp(-err_scale * pose_scale * pose_err)
@@ -565,7 +773,6 @@ class QuadrupedStablePD(object):
 
     reward = pose_w * pose_reward + vel_w * vel_reward + end_eff_w * end_eff_reward + root_w * root_reward + com_w * com_reward
 
-    
     # pose_reward,vel_reward,end_eff_reward, root_reward, com_reward);
     #print("reward=",reward)
     #print("pose_reward=",pose_reward)
@@ -573,5 +780,5 @@ class QuadrupedStablePD(object):
     #print("end_eff_reward=",end_eff_reward)
     #print("root_reward=",root_reward)
     #print("com_reward=",com_reward)
-    
+
     return reward
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testHumanoid.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testHumanoid.py
index d34ffe794..c689a062a 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testHumanoid.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testHumanoid.py
@@ -8,87 +8,89 @@ import pybullet as p1
 import humanoid_pose_interpolator
 import numpy as np
 
-pybullet_client =  bullet_client.BulletClient(connection_mode=p1.GUI)
+pybullet_client = bullet_client.BulletClient(connection_mode=p1.GUI)
 
 pybullet_client.setAdditionalSearchPath(pybullet_data.getDataPath())
-z2y = pybullet_client.getQuaternionFromEuler([-math.pi*0.5,0,0])
+z2y = pybullet_client.getQuaternionFromEuler([-math.pi * 0.5, 0, 0])
 #planeId = pybullet_client.loadURDF("plane.urdf",[0,0,0],z2y)
-planeId= pybullet_client.loadURDF("plane_implicit.urdf",[0,0,0],z2y, useMaximalCoordinates=True)
-pybullet_client.changeDynamics(planeId, linkIndex=-1, lateralFriction=0.9)        
+planeId = pybullet_client.loadURDF("plane_implicit.urdf", [0, 0, 0],
+                                   z2y,
+                                   useMaximalCoordinates=True)
+pybullet_client.changeDynamics(planeId, linkIndex=-1, lateralFriction=0.9)
 #print("planeId=",planeId)
 
-pybullet_client.configureDebugVisualizer(pybullet_client.COV_ENABLE_Y_AXIS_UP,1)
-pybullet_client.setGravity(0,-9.8,0)
+pybullet_client.configureDebugVisualizer(pybullet_client.COV_ENABLE_Y_AXIS_UP, 1)
+pybullet_client.setGravity(0, -9.8, 0)
 
 pybullet_client.setPhysicsEngineParameter(numSolverIterations=10)
 
-
 mocapData = motion_capture_data.MotionCaptureData()
 #motionPath = pybullet_data.getDataPath()+"/data/motions/humanoid3d_walk.txt"
-motionPath = pybullet_data.getDataPath()+"/data/motions/humanoid3d_backflip.txt"
+motionPath = pybullet_data.getDataPath() + "/data/motions/humanoid3d_backflip.txt"
 mocapData.Load(motionPath)
-timeStep = 1./600
-useFixedBase=False
+timeStep = 1. / 600
+useFixedBase = False
 humanoid = humanoid_stable_pd.HumanoidStablePD(pybullet_client, mocapData, timeStep, useFixedBase)
 isInitialized = True
 
 pybullet_client.setTimeStep(timeStep)
 pybullet_client.setPhysicsEngineParameter(numSubSteps=2)
-timeId = pybullet_client.addUserDebugParameter("time",0,10,0)
+timeId = pybullet_client.addUserDebugParameter("time", 0, 10, 0)
+
 
-  
 def isKeyTriggered(keys, key):
   o = ord(key)
   if o in keys:
     return keys[ord(key)] & pybullet_client.KEY_WAS_TRIGGERED
   return False
-  
+
+
 animating = False
 singleStep = False
 
-
-t=0
+t = 0
 while (1):
 
   keys = pybullet_client.getKeyboardEvents()
   #print(keys)
   if isKeyTriggered(keys, ' '):
-      animating = not animating
+    animating = not animating
 
   if isKeyTriggered(keys, 'b'):
-      singleStep = True
-  
+    singleStep = True
+
   if animating or singleStep:
-    
-    
+
     singleStep = False
     #t = pybullet_client.readUserDebugParameter(timeId)
     #print("t=",t)
-    for i in range (1):
+    for i in range(1):
 
-      print("t=",t)
+      print("t=", t)
       humanoid.setSimTime(t)
-        
+
       humanoid.computePose(humanoid._frameFraction)
       pose = humanoid._poseInterpolator
       #humanoid.initializePose(pose=pose, phys_model = humanoid._sim_model, initBase=True, initializeVelocity=True)
       #humanoid.resetPose()
-      
-      
+
       desiredPose = humanoid.computePose(humanoid._frameFraction)
-      #desiredPose = desiredPose.GetPose() 
+      #desiredPose = desiredPose.GetPose()
       #curPose = HumanoidPoseInterpolator()
       #curPose.reset()
       s = humanoid.getState()
       #np.savetxt("pb_record_state_s.csv", s, delimiter=",")
-      maxForces = [0,0,0,0,0,0,0,200,200,200,200, 50,50,50,50, 200,200,200,200, 150, 90,90,90,90, 100,100,100,100, 60, 200,200,200,200,  150, 90, 90, 90, 90, 100,100,100,100, 60]
+      maxForces = [
+          0, 0, 0, 0, 0, 0, 0, 200, 200, 200, 200, 50, 50, 50, 50, 200, 200, 200, 200, 150, 90, 90,
+          90, 90, 100, 100, 100, 100, 60, 200, 200, 200, 200, 150, 90, 90, 90, 90, 100, 100, 100,
+          100, 60
+      ]
       taus = humanoid.computePDForces(desiredPose, desiredVelocities=None, maxForces=maxForces)
-      
+
       #print("taus=",taus)
       humanoid.applyPDForces(taus)
-      
+
       pybullet_client.stepSimulation()
-      t+=1./600.
-      
-    
-  time.sleep(1./600.)
\ No newline at end of file
+      t += 1. / 600.
+
+  time.sleep(1. / 600.)
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testLaikago.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testLaikago.py
index 1ad8972ed..ef2b21c33 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testLaikago.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/env/testLaikago.py
@@ -8,240 +8,258 @@ import motion_capture_data
 import quadrupedPoseInterpolator
 
 useConstraints = False
-  
-p =  bullet_client.BulletClient(connection_mode=p1.GUI)
+
+p = bullet_client.BulletClient(connection_mode=p1.GUI)
 p.setAdditionalSearchPath(pybullet_data.getDataPath())
 
 plane = p.loadURDF("plane.urdf")
-p.setGravity(0,0,-10)
-timeStep=1./500
+p.setGravity(0, 0, -10)
+timeStep = 1. / 500
 p.setTimeStep(timeStep)
 #p.setDefaultContactERP(0)
-#urdfFlags = p.URDF_USE_SELF_COLLISION+p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS 
+#urdfFlags = p.URDF_USE_SELF_COLLISION+p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS
 urdfFlags = p.URDF_USE_SELF_COLLISION
 
-
-startPos=[0.007058990464444105, 0.03149299192130908, 0.4918981912395484]
-startOrn=[0.005934649695708604, 0.7065453990917289, 0.7076373820553712, -0.0027774940359030264]
-quadruped = p.loadURDF("laikago/laikago.urdf",startPos,startOrn, flags = urdfFlags,useFixedBase=False)
-p.resetBasePositionAndOrientation(quadruped,startPos,startOrn)
+startPos = [0.007058990464444105, 0.03149299192130908, 0.4918981912395484]
+startOrn = [0.005934649695708604, 0.7065453990917289, 0.7076373820553712, -0.0027774940359030264]
+quadruped = p.loadURDF("laikago/laikago.urdf",
+                       startPos,
+                       startOrn,
+                       flags=urdfFlags,
+                       useFixedBase=False)
+p.resetBasePositionAndOrientation(quadruped, startPos, startOrn)
 if not useConstraints:
   for j in range(p.getNumJoints(quadruped)):
-    p.setJointMotorControl2(quadruped,j,p.POSITION_CONTROL,force=0)
-  
-#This cube is added as a soft constraint to keep the laikago from falling 
+    p.setJointMotorControl2(quadruped, j, p.POSITION_CONTROL, force=0)
+
+#This cube is added as a soft constraint to keep the laikago from falling
 #since we didn't train it yet, it doesn't balance
-cube = p.loadURDF("cube_no_rotation.urdf",[0,0,-0.5],[0,0.5,0.5,0])
-p.setCollisionFilterGroupMask(cube,-1,0,0)
+cube = p.loadURDF("cube_no_rotation.urdf", [0, 0, -0.5], [0, 0.5, 0.5, 0])
+p.setCollisionFilterGroupMask(cube, -1, 0, 0)
 for j in range(p.getNumJoints(cube)):
-  p.setJointMotorControl2(cube,j,p.POSITION_CONTROL,force=0)
-  p.setCollisionFilterGroupMask(cube,j,0,0)
-  p.changeVisualShape(cube,j,rgbaColor=[1,0,0,0])
-cid = p.createConstraint(cube,p.getNumJoints(cube)-1,quadruped,-1,p.JOINT_FIXED,[0,0,0],[0,1,0],[0,0,0])
+  p.setJointMotorControl2(cube, j, p.POSITION_CONTROL, force=0)
+  p.setCollisionFilterGroupMask(cube, j, 0, 0)
+  p.changeVisualShape(cube, j, rgbaColor=[1, 0, 0, 0])
+cid = p.createConstraint(cube,
+                         p.getNumJoints(cube) - 1, quadruped, -1, p.JOINT_FIXED, [0, 0, 0],
+                         [0, 1, 0], [0, 0, 0])
 p.changeConstraint(cid, maxForce=10)
 
+jointIds = []
+paramIds = []
+jointOffsets = []
+jointDirections = [-1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1]
+jointAngles = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
 
-jointIds=[]
-paramIds=[]
-jointOffsets=[]
-jointDirections=[-1,1,1,1,1,1,-1,1,1,1,1,1]
-jointAngles=[0,0,0,0,0,0,0,0,0,0,0,0]
-
-for i in range (4):
+for i in range(4):
   jointOffsets.append(0)
   jointOffsets.append(-0.7)
   jointOffsets.append(0.7)
 
-maxForceId = p.addUserDebugParameter("maxForce",0,100,20)
-
-for j in range (p.getNumJoints(quadruped)):
-        p.changeDynamics(quadruped,j,linearDamping=0, angularDamping=0)
-        info = p.getJointInfo(quadruped,j)
-        #print(info)
-        jointName = info[1]
-        jointType = info[2]
-        if (jointType==p.JOINT_PRISMATIC or jointType==p.JOINT_REVOLUTE):
-                jointIds.append(j)
+maxForceId = p.addUserDebugParameter("maxForce", 0, 100, 20)
 
-    
-startQ=[0.08389, 0.8482, -1.547832, -0.068933, 0.625726, -1.272086, 0.074398, 0.61135, -1.255892, -0.068262, 0.836745, -1.534517]
 for j in range(p.getNumJoints(quadruped)):
-  p.resetJointState(quadruped,jointIds[j],jointDirections[j]*startQ[j]+jointOffsets[j])
-
+  p.changeDynamics(quadruped, j, linearDamping=0, angularDamping=0)
+  info = p.getJointInfo(quadruped, j)
+  #print(info)
+  jointName = info[1]
+  jointType = info[2]
+  if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE):
+    jointIds.append(j)
+
+startQ = [
+    0.08389, 0.8482, -1.547832, -0.068933, 0.625726, -1.272086, 0.074398, 0.61135, -1.255892,
+    -0.068262, 0.836745, -1.534517
+]
+for j in range(p.getNumJoints(quadruped)):
+  p.resetJointState(quadruped, jointIds[j], jointDirections[j] * startQ[j] + jointOffsets[j])
 
 qpi = quadrupedPoseInterpolator.QuadrupedPoseInterpolator()
 
 #enable collision between lower legs
 
-for j in range (p.getNumJoints(quadruped)):
-		print(p.getJointInfo(quadruped,j))
+for j in range(p.getNumJoints(quadruped)):
+  print(p.getJointInfo(quadruped, j))
 
 #2,5,8 and 11 are the lower legs
-lower_legs = [2,5,8,11]
+lower_legs = [2, 5, 8, 11]
 for l0 in lower_legs:
-	for l1 in lower_legs:
-		if (l1>l0):
-			enableCollision = 1
-			print("collision for pair",l0,l1, p.getJointInfo(quadruped,l0)[12],p.getJointInfo(quadruped,l1)[12], "enabled=",enableCollision)
-			p.setCollisionFilterPair(quadruped, quadruped, 2,5,enableCollision)
-
-jointIds=[]
-paramIds=[]
-jointOffsets=[]
-jointDirections=[-1,1,1,1,1,1,-1,1,1,1,1,1]
-jointAngles=[0,0,0,0,0,0,0,0,0,0,0,0]
-
-for i in range (4):
-	jointOffsets.append(0)
-	jointOffsets.append(-0.7)
-	jointOffsets.append(0.7)
-
-maxForceId = p.addUserDebugParameter("maxForce",0,100,20)
-
-for j in range (p.getNumJoints(quadruped)):
-        p.changeDynamics(quadruped,j,linearDamping=0, angularDamping=0)
-        info = p.getJointInfo(quadruped,j)
-        #print(info)
-        jointName = info[1]
-        jointType = info[2]
-        if (jointType==p.JOINT_PRISMATIC or jointType==p.JOINT_REVOLUTE):
-                jointIds.append(j)
-
-		
-p.getCameraImage(480,320)
-p.setRealTimeSimulation(0)
+  for l1 in lower_legs:
+    if (l1 > l0):
+      enableCollision = 1
+      print("collision for pair", l0, l1,
+            p.getJointInfo(quadruped, l0)[12],
+            p.getJointInfo(quadruped, l1)[12], "enabled=", enableCollision)
+      p.setCollisionFilterPair(quadruped, quadruped, 2, 5, enableCollision)
+
+jointIds = []
+paramIds = []
+jointOffsets = []
+jointDirections = [-1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1]
+jointAngles = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+
+for i in range(4):
+  jointOffsets.append(0)
+  jointOffsets.append(-0.7)
+  jointOffsets.append(0.7)
+
+maxForceId = p.addUserDebugParameter("maxForce", 0, 100, 20)
 
-joints=[]
+for j in range(p.getNumJoints(quadruped)):
+  p.changeDynamics(quadruped, j, linearDamping=0, angularDamping=0)
+  info = p.getJointInfo(quadruped, j)
+  #print(info)
+  jointName = info[1]
+  jointType = info[2]
+  if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE):
+    jointIds.append(j)
+
+p.getCameraImage(480, 320)
+p.setRealTimeSimulation(0)
 
+joints = []
 
 mocapData = motion_capture_data.MotionCaptureData()
 
-motionPath = pybullet_data.getDataPath()+"/data/motions/laikago_walk.json"
+motionPath = pybullet_data.getDataPath() + "/data/motions/laikago_walk.txt"
 
 mocapData.Load(motionPath)
-print("mocapData.NumFrames=",mocapData.NumFrames())
-print("mocapData.KeyFrameDuraction=",mocapData.KeyFrameDuraction())
-print("mocapData.getCycleTime=",mocapData.getCycleTime())
-print("mocapData.computeCycleOffset=",mocapData.computeCycleOffset())
+print("mocapData.NumFrames=", mocapData.NumFrames())
+print("mocapData.KeyFrameDuraction=", mocapData.KeyFrameDuraction())
+print("mocapData.getCycleTime=", mocapData.getCycleTime())
+print("mocapData.computeCycleOffset=", mocapData.computeCycleOffset())
 
 stablePD = pd_controller_stable.PDControllerStable(p)
 
 cycleTime = mocapData.getCycleTime()
-t=0
+t = 0
 
-while t<10.*cycleTime:
+while t < 10. * cycleTime:
   #get interpolated joint
   keyFrameDuration = mocapData.KeyFrameDuraction()
   cycleTime = mocapData.getCycleTime()
   cycleCount = mocapData.calcCycleCount(t, cycleTime)
-  
+
   #print("cycleTime=",cycleTime)
   #print("cycleCount=",cycleCount)
-  
+
   #print("cycles=",cycles)
-  frameTime = t - cycleCount*cycleTime
+  frameTime = t - cycleCount * cycleTime
   #print("frameTime=",frameTime)
-  if (frameTime<0):
+  if (frameTime < 0):
     frameTime += cycleTime
 
-  frame = int(frameTime/keyFrameDuration)
-  frameNext = frame+1
-  if (frameNext >=  mocapData.NumFrames()):
+  frame = int(frameTime / keyFrameDuration)
+  frameNext = frame + 1
+  if (frameNext >= mocapData.NumFrames()):
     frameNext = frame
-  frameFraction = (frameTime - frame*keyFrameDuration)/(keyFrameDuration)
+  frameFraction = (frameTime - frame * keyFrameDuration) / (keyFrameDuration)
   #print("frame=",frame)
   #print("frameFraction=",frameFraction)
   frameData = mocapData._motion_data['Frames'][frame]
   frameDataNext = mocapData._motion_data['Frames'][frameNext]
-    
-  jointsStr,qdot=qpi.Slerp(frameFraction, frameData, frameDataNext, p)
-  
+
+  jointsStr, qdot = qpi.Slerp(frameFraction, frameData, frameDataNext, p)
+
   maxForce = p.readUserDebugParameter(maxForceId)
-  print("jointIds=",jointIds)
+  print("jointIds=", jointIds)
 
   if useConstraints:
-    for j in range (12):
+    for j in range(12):
       #skip the base positional dofs
-      targetPos = float(jointsStr[j+7])
-      p.setJointMotorControl2(quadruped,jointIds[j],p.POSITION_CONTROL,jointDirections[j]*targetPos+jointOffsets[j], force=maxForce)
-    
+      targetPos = float(jointsStr[j + 7])
+      p.setJointMotorControl2(quadruped,
+                              jointIds[j],
+                              p.POSITION_CONTROL,
+                              jointDirections[j] * targetPos + jointOffsets[j],
+                              force=maxForce)
+
   else:
-    desiredPositions=[]
-    for j in range (7):
+    desiredPositions = []
+    for j in range(7):
       targetPosUnmodified = float(jointsStr[j])
       desiredPositions.append(targetPosUnmodified)
-    for j in range (12):
-      targetPosUnmodified = float(jointsStr[j+7])
-      targetPos=jointDirections[j]*targetPosUnmodified+jointOffsets[j]
+    for j in range(12):
+      targetPosUnmodified = float(jointsStr[j + 7])
+      targetPos = jointDirections[j] * targetPosUnmodified + jointOffsets[j]
       desiredPositions.append(targetPos)
-    numBaseDofs=6
-    totalDofs=12+numBaseDofs
-    desiredVelocities=None
-    if desiredVelocities==None:
-      desiredVelocities = [0]*totalDofs 
-    taus = stablePD.computePD(bodyUniqueId=quadruped, 
-      jointIndices = jointIds,
-      desiredPositions = desiredPositions,
-      desiredVelocities = desiredVelocities,
-      kps = [4000]*totalDofs,
-      kds = [40]*totalDofs,
-      maxForces = [500]*totalDofs, 
-      timeStep=timeStep)
-    
-    dofIndex=6
+    numBaseDofs = 6
+    totalDofs = 12 + numBaseDofs
+    desiredVelocities = None
+    if desiredVelocities == None:
+      desiredVelocities = [0] * totalDofs
+    taus = stablePD.computePD(bodyUniqueId=quadruped,
+                              jointIndices=jointIds,
+                              desiredPositions=desiredPositions,
+                              desiredVelocities=desiredVelocities,
+                              kps=[4000] * totalDofs,
+                              kds=[40] * totalDofs,
+                              maxForces=[500] * totalDofs,
+                              timeStep=timeStep)
+
+    dofIndex = 6
     scaling = 1
-    for index in range (len(jointIds)):
+    for index in range(len(jointIds)):
       jointIndex = jointIds[index]
-      force=[scaling*taus[dofIndex]]
-      print("force[", jointIndex,"]=",force)
-      p.setJointMotorControlMultiDof(quadruped, jointIndex, controlMode=p.TORQUE_CONTROL, force=force)
-      dofIndex+=1
-   
+      force = [scaling * taus[dofIndex]]
+      print("force[", jointIndex, "]=", force)
+      p.setJointMotorControlMultiDof(quadruped,
+                                     jointIndex,
+                                     controlMode=p.TORQUE_CONTROL,
+                                     force=force)
+      dofIndex += 1
 
   p.stepSimulation()
-  t+=timeStep
+  t += timeStep
   time.sleep(timeStep)
 
-useOrgData=False
+useOrgData = False
 if useOrgData:
-  with open("data1.txt","r") as filestream:
-  	for line in filestream:
-  		maxForce = p.readUserDebugParameter(maxForceId)
-  		currentline = line.split(",")
-  		frame = currentline[0]
-  		t = currentline[1]
-  		joints=currentline[2:14]
-  		for j in range (12):
-  			targetPos = float(joints[j])
-  			p.setJointMotorControl2(quadruped,jointIds[j],p.POSITION_CONTROL,jointDirections[j]*targetPos+jointOffsets[j], force=maxForce)
-  		p.stepSimulation()
-  		for lower_leg in lower_legs:
-  			#print("points for ", quadruped, " link: ", lower_leg)
-  			pts = p.getContactPoints(quadruped,-1, lower_leg)
-  			#print("num points=",len(pts))
-  			#for pt in pts:
-  			#	print(pt[9])
-  		time.sleep(1./500.)
-
-
-for j in range (p.getNumJoints(quadruped)):
-        p.changeDynamics(quadruped,j,linearDamping=0, angularDamping=0)
-        info = p.getJointInfo(quadruped,j)
-        js = p.getJointState(quadruped,j)
-        #print(info)
-        jointName = info[1]
-        jointType = info[2]
-        if (jointType==p.JOINT_PRISMATIC or jointType==p.JOINT_REVOLUTE):
-                paramIds.append(p.addUserDebugParameter(jointName.decode("utf-8"),-4,4,(js[0]-jointOffsets[j])/jointDirections[j]))
+  with open("data1.txt", "r") as filestream:
+    for line in filestream:
+      maxForce = p.readUserDebugParameter(maxForceId)
+      currentline = line.split(",")
+      frame = currentline[0]
+      t = currentline[1]
+      joints = currentline[2:14]
+      for j in range(12):
+        targetPos = float(joints[j])
+        p.setJointMotorControl2(quadruped,
+                                jointIds[j],
+                                p.POSITION_CONTROL,
+                                jointDirections[j] * targetPos + jointOffsets[j],
+                                force=maxForce)
+      p.stepSimulation()
+      for lower_leg in lower_legs:
+        #print("points for ", quadruped, " link: ", lower_leg)
+        pts = p.getContactPoints(quadruped, -1, lower_leg)
+        #print("num points=",len(pts))
+        #for pt in pts:
+        #	print(pt[9])
+      time.sleep(1. / 500.)
 
+for j in range(p.getNumJoints(quadruped)):
+  p.changeDynamics(quadruped, j, linearDamping=0, angularDamping=0)
+  info = p.getJointInfo(quadruped, j)
+  js = p.getJointState(quadruped, j)
+  #print(info)
+  jointName = info[1]
+  jointType = info[2]
+  if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE):
+    paramIds.append(
+        p.addUserDebugParameter(jointName.decode("utf-8"), -4, 4,
+                                (js[0] - jointOffsets[j]) / jointDirections[j]))
 
 p.setRealTimeSimulation(1)
 
 while (1):
-	
-	for i in range(len(paramIds)):
-		c = paramIds[i]
-		targetPos = p.readUserDebugParameter(c)
-		maxForce = p.readUserDebugParameter(maxForceId)
-		p.setJointMotorControl2(quadruped,jointIds[i],p.POSITION_CONTROL,jointDirections[i]*targetPos+jointOffsets[i], force=maxForce)
-	
+
+  for i in range(len(paramIds)):
+    c = paramIds[i]
+    targetPos = p.readUserDebugParameter(c)
+    maxForce = p.readUserDebugParameter(maxForceId)
+    p.setJointMotorControl2(quadruped,
+                            jointIds[i],
+                            p.POSITION_CONTROL,
+                            jointDirections[i] * targetPos + jointOffsets[i],
+                            force=maxForce)
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/agent_builder.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/agent_builder.py
index e2047da98..cf67210da 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/agent_builder.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/agent_builder.py
@@ -5,17 +5,18 @@ import pybullet_data
 
 AGENT_TYPE_KEY = "AgentType"
 
+
 def build_agent(world, id, file):
-    agent = None
-    with open(pybullet_data.getDataPath()+"/"+file) as data_file:    
-        json_data = json.load(data_file)
-        
-        assert AGENT_TYPE_KEY in json_data
-        agent_type = json_data[AGENT_TYPE_KEY]
-        
-        if (agent_type == PPOAgent.NAME):
-            agent = PPOAgent(world, id, json_data)
-        else:
-            assert False, 'Unsupported agent type: ' + agent_type
+  agent = None
+  with open(pybullet_data.getDataPath() + "/" + file) as data_file:
+    json_data = json.load(data_file)
+
+    assert AGENT_TYPE_KEY in json_data
+    agent_type = json_data[AGENT_TYPE_KEY]
+
+    if (agent_type == PPOAgent.NAME):
+      agent = PPOAgent(world, id, json_data)
+    else:
+      assert False, 'Unsupported agent type: ' + agent_type
 
-    return agent
+  return agent
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/exp_params.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/exp_params.py
index 07c83e0f2..4a01dfec9 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/exp_params.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/exp_params.py
@@ -2,53 +2,54 @@ import json
 import numpy as np
 import pybullet_utils.math_util as MathUtil
 
+
 class ExpParams(object):
-    RATE_KEY = 'Rate'
-    INIT_ACTION_RATE_KEY = 'InitActionRate'
-    NOISE_KEY = 'Noise'
-    NOISE_INTERNAL_KEY = 'NoiseInternal'
-    TEMP_KEY = 'Temp'
-
-    def __init__(self):
-        self.rate = 0.2
-        self.init_action_rate = 0
-        self.noise = 0.1
-        self.noise_internal = 0
-        self.temp = 0.1
-        return
-
-    def __str__(self):
-        str = ''
-        str += '{}: {:.2f}\n'.format(self.RATE_KEY, self.rate)
-        str += '{}: {:.2f}\n'.format(self.INIT_ACTION_RATE_KEY, self.init_action_rate)
-        str += '{}: {:.2f}\n'.format(self.NOISE_KEY, self.noise)
-        str += '{}: {:.2f}\n'.format(self.NOISE_INTERNAL_KEY, self.noise_internal)
-        str += '{}: {:.2f}\n'.format(self.TEMP_KEY, self.temp)
-        return str
-
-    def load(self, json_data):
-        if (self.RATE_KEY in json_data):
-            self.rate = json_data[self.RATE_KEY]
-
-        if (self.INIT_ACTION_RATE_KEY in json_data):
-            self.init_action_rate = json_data[self.INIT_ACTION_RATE_KEY]
-
-        if (self.NOISE_KEY in json_data):
-            self.noise = json_data[self.NOISE_KEY]
-
-        if (self.NOISE_INTERNAL_KEY in json_data):
-            self.noise_internal = json_data[self.NOISE_INTERNAL_KEY]
-
-        if (self.TEMP_KEY in json_data):
-            self.temp = json_data[self.TEMP_KEY]
-
-        return
-
-    def lerp(self, other, t):
-        lerp_params = ExpParams()
-        lerp_params.rate = MathUtil.lerp(self.rate, other.rate, t)
-        lerp_params.init_action_rate = MathUtil.lerp(self.init_action_rate, other.init_action_rate, t)
-        lerp_params.noise = MathUtil.lerp(self.noise, other.noise, t)
-        lerp_params.noise_internal = MathUtil.lerp(self.noise_internal, other.noise_internal, t)
-        lerp_params.temp = MathUtil.log_lerp(self.temp, other.temp, t)
-        return lerp_params
\ No newline at end of file
+  RATE_KEY = 'Rate'
+  INIT_ACTION_RATE_KEY = 'InitActionRate'
+  NOISE_KEY = 'Noise'
+  NOISE_INTERNAL_KEY = 'NoiseInternal'
+  TEMP_KEY = 'Temp'
+
+  def __init__(self):
+    self.rate = 0.2
+    self.init_action_rate = 0
+    self.noise = 0.1
+    self.noise_internal = 0
+    self.temp = 0.1
+    return
+
+  def __str__(self):
+    str = ''
+    str += '{}: {:.2f}\n'.format(self.RATE_KEY, self.rate)
+    str += '{}: {:.2f}\n'.format(self.INIT_ACTION_RATE_KEY, self.init_action_rate)
+    str += '{}: {:.2f}\n'.format(self.NOISE_KEY, self.noise)
+    str += '{}: {:.2f}\n'.format(self.NOISE_INTERNAL_KEY, self.noise_internal)
+    str += '{}: {:.2f}\n'.format(self.TEMP_KEY, self.temp)
+    return str
+
+  def load(self, json_data):
+    if (self.RATE_KEY in json_data):
+      self.rate = json_data[self.RATE_KEY]
+
+    if (self.INIT_ACTION_RATE_KEY in json_data):
+      self.init_action_rate = json_data[self.INIT_ACTION_RATE_KEY]
+
+    if (self.NOISE_KEY in json_data):
+      self.noise = json_data[self.NOISE_KEY]
+
+    if (self.NOISE_INTERNAL_KEY in json_data):
+      self.noise_internal = json_data[self.NOISE_INTERNAL_KEY]
+
+    if (self.TEMP_KEY in json_data):
+      self.temp = json_data[self.TEMP_KEY]
+
+    return
+
+  def lerp(self, other, t):
+    lerp_params = ExpParams()
+    lerp_params.rate = MathUtil.lerp(self.rate, other.rate, t)
+    lerp_params.init_action_rate = MathUtil.lerp(self.init_action_rate, other.init_action_rate, t)
+    lerp_params.noise = MathUtil.lerp(self.noise, other.noise, t)
+    lerp_params.noise_internal = MathUtil.lerp(self.noise_internal, other.noise_internal, t)
+    lerp_params.temp = MathUtil.log_lerp(self.temp, other.temp, t)
+    return lerp_params
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/__init__.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/__init__.py
index b9742821a..1e136c6c4 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/__init__.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/__init__.py
@@ -1 +1 @@
-from . import *
\ No newline at end of file
+from . import *

diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/fc_2layers_1024units.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/fc_2layers_1024units.py
index b4d466124..a0f627639 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/fc_2layers_1024units.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/fc_2layers_1024units.py
@@ -3,11 +3,12 @@ import pybullet_envs.deep_mimic.learning.tf_util as TFUtil
 
 NAME = "fc_2layers_1024units"
 
+
 def build_net(input_tfs, reuse=False):
-    layers = [1024, 512]
-    activation = tf.nn.relu
+  layers = [1024, 512]
+  activation = tf.nn.relu
 
-    input_tf = tf.concat(axis=-1, values=input_tfs)          
-    h = TFUtil.fc_net(input_tf, layers, activation=activation, reuse=reuse)
-    h = activation(h)
-    return h
\ No newline at end of file
+  input_tf = tf.concat(axis=-1, values=input_tfs)
+  h = TFUtil.fc_net(input_tf, layers, activation=activation, reuse=reuse)
+  h = activation(h)
+  return h
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/net_builder.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/net_builder.py
index 32328493b..1441bffd9 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/net_builder.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/nets/net_builder.py
@@ -1,11 +1,12 @@
 import pybullet_envs.deep_mimic.learning.nets.fc_2layers_1024units as fc_2layers_1024units
 
+
 def build_net(net_name, input_tfs, reuse=False):
-    net = None
-
-    if (net_name == fc_2layers_1024units.NAME):
-        net = fc_2layers_1024units.build_net(input_tfs, reuse)
-    else:
-        assert False, 'Unsupported net: ' + net_name
-    
-    return net
\ No newline at end of file
+  net = None
+
+  if (net_name == fc_2layers_1024units.NAME):
+    net = fc_2layers_1024units.build_net(input_tfs, reuse)
+  else:
+    assert False, 'Unsupported net: ' + net_name
+
+  return net
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/normalizer.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/normalizer.py
index 74bf90569..8feabc7f9 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/normalizer.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/normalizer.py
@@ -3,147 +3,149 @@ import copy
 import pybullet_utils.mpi_util as MPIUtil
 from pybullet_utils.logger import Logger
 
+
 class Normalizer(object):
-    CHECK_SYNC_COUNT = 50000 # check synchronization after a certain number of entries
-
-    # these group IDs must be the same as those in CharController.h
-    NORM_GROUP_SINGLE = 0
-    NORM_GROUP_NONE = -1
-
-    class Group(object):
-        def __init__(self, id, indices):
-            self.id = id
-            self.indices = indices
-            return
-
-    def __init__(self, size, groups_ids=None, eps=0.02, clip=np.inf):
-        self.eps = eps
-        self.clip = clip
-        self.mean = np.zeros(size)
-        self.mean_sq = np.zeros(size)
-        self.std = np.ones(size)
-        self.count = 0
-        self.groups = self._build_groups(groups_ids)
-
-        self.new_count = 0
-        self.new_sum = np.zeros_like(self.mean)
-        self.new_sum_sq = np.zeros_like(self.mean_sq)
-        return
-
-    def record(self, x):
-        size = self.get_size()
-        is_array = isinstance(x, np.ndarray)
-        if not is_array:
-            assert(size == 1)
-            x = np.array([[x]])
-
-        assert x.shape[-1] == size, \
-            Logger.print2('Normalizer shape mismatch, expecting size {:d}, but got {:d}'.format(size, x.shape[-1]))
-        x = np.reshape(x, [-1, size])
-
-        self.new_count += x.shape[0]
-        self.new_sum += np.sum(x, axis=0)
-        self.new_sum_sq += np.sum(np.square(x), axis=0)
-        return
-
-    def update(self):
-        new_count = MPIUtil.reduce_sum(self.new_count)
-        new_sum = MPIUtil.reduce_sum(self.new_sum)
-        new_sum_sq = MPIUtil.reduce_sum(self.new_sum_sq)
-
-        new_total = self.count + new_count
-        if (self.count // self.CHECK_SYNC_COUNT != new_total // self.CHECK_SYNC_COUNT):
-            assert self.check_synced(), Logger.print2('Normalizer parameters desynchronized')
-
-        if new_count > 0:
-            new_mean = self._process_group_data(new_sum / new_count, self.mean)
-            new_mean_sq = self._process_group_data(new_sum_sq / new_count, self.mean_sq)
-            w_old = float(self.count) / new_total
-            w_new = float(new_count) / new_total
-
-            self.mean = w_old * self.mean + w_new * new_mean
-            self.mean_sq = w_old * self.mean_sq + w_new * new_mean_sq
-            self.count = new_total
-            self.std = self.calc_std(self.mean, self.mean_sq)
-
-            self.new_count = 0
-            self.new_sum.fill(0)
-            self.new_sum_sq.fill(0)
-
-        return
-
-    def get_size(self):
-        return self.mean.size
-
-    def set_mean_std(self, mean, std):
-        size = self.get_size()
-        is_array = isinstance(mean, np.ndarray) and isinstance(std, np.ndarray)
-        
-        if not is_array:
-            assert(size == 1)
-            mean = np.array([mean])
-            std = np.array([std])
-
-        assert len(mean) == size and len(std) == size, \
-            Logger.print2('Normalizer shape mismatch, expecting size {:d}, but got {:d} and {:d}'.format(size, len(mean), len(std)))
-        
-        self.mean = mean
-        self.std = std
-        self.mean_sq = self.calc_mean_sq(self.mean, self.std)
-        return
-
-    def normalize(self, x):
-        norm_x = (x - self.mean) / self.std
-        norm_x = np.clip(norm_x, -self.clip, self.clip)
-        return norm_x
-
-    def unnormalize(self, norm_x):
-        x = norm_x * self.std + self.mean
-        return x
-
-    def calc_std(self, mean, mean_sq):
-        var = mean_sq - np.square(mean)
-        # some time floating point errors can lead to small negative numbers
-        var = np.maximum(var, 0)
-        std = np.sqrt(var)
-        std = np.maximum(std, self.eps)
-        return std
-
-    def calc_mean_sq(self, mean, std):
-        return np.square(std) + np.square(self.mean)
-
-    def check_synced(self):
-        synced = True
-        if MPIUtil.is_root_proc():
-            vars = np.concatenate([self.mean, self.mean_sq])
-            MPIUtil.bcast(vars)
-        else:
-            vars_local = np.concatenate([self.mean, self.mean_sq])
-            vars_root = np.empty_like(vars_local)
-            MPIUtil.bcast(vars_root)
-            synced = (vars_local == vars_root).all()
-        return synced
-
-    def _build_groups(self, groups_ids):
-        groups = []
-        if groups_ids is None:
-            curr_id = self.NORM_GROUP_SINGLE
-            curr_list = np.arange(self.get_size()).astype(np.int32)
-            groups.append(self.Group(curr_id, curr_list))
-        else:
-            ids = np.unique(groups_ids)
-            for id in ids:
-                curr_list = np.nonzero(groups_ids == id)[0].astype(np.int32)
-                groups.append(self.Group(id, curr_list))
-
-        return groups
-
-    def _process_group_data(self, new_data, old_data):
-        proc_data = new_data.copy()
-        for group in self.groups:
-            if group.id == self.NORM_GROUP_NONE:
-                proc_data[group.indices] = old_data[group.indices]
-            elif group.id != self.NORM_GROUP_SINGLE:
-                avg = np.mean(new_data[group.indices])
-                proc_data[group.indices] = avg
-        return proc_data
\ No newline at end of file
+  CHECK_SYNC_COUNT = 50000  # check synchronization after a certain number of entries
+
+  # these group IDs must be the same as those in CharController.h
+  NORM_GROUP_SINGLE = 0
+  NORM_GROUP_NONE = -1
+
+  class Group(object):
+
+    def __init__(self, id, indices):
+      self.id = id
+      self.indices = indices
+      return
+
+  def __init__(self, size, groups_ids=None, eps=0.02, clip=np.inf):
+    self.eps = eps
+    self.clip = clip
+    self.mean = np.zeros(size)
+    self.mean_sq = np.zeros(size)
+    self.std = np.ones(size)
+    self.count = 0
+    self.groups = self._build_groups(groups_ids)
+
+    self.new_count = 0
+    self.new_sum = np.zeros_like(self.mean)
+    self.new_sum_sq = np.zeros_like(self.mean_sq)
+    return
+
+  def record(self, x):
+    size = self.get_size()
+    is_array = isinstance(x, np.ndarray)
+    if not is_array:
+      assert (size == 1)
+      x = np.array([[x]])
+
+    assert x.shape[-1] == size, \
+        Logger.print2('Normalizer shape mismatch, expecting size {:d}, but got {:d}'.format(size, x.shape[-1]))
+    x = np.reshape(x, [-1, size])
+
+    self.new_count += x.shape[0]
+    self.new_sum += np.sum(x, axis=0)
+    self.new_sum_sq += np.sum(np.square(x), axis=0)
+    return
+
+  def update(self):
+    new_count = MPIUtil.reduce_sum(self.new_count)
+    new_sum = MPIUtil.reduce_sum(self.new_sum)
+    new_sum_sq = MPIUtil.reduce_sum(self.new_sum_sq)
+
+    new_total = self.count + new_count
+    if (self.count // self.CHECK_SYNC_COUNT != new_total // self.CHECK_SYNC_COUNT):
+      assert self.check_synced(), Logger.print2('Normalizer parameters desynchronized')
+
+    if new_count > 0:
+      new_mean = self._process_group_data(new_sum / new_count, self.mean)
+      new_mean_sq = self._process_group_data(new_sum_sq / new_count, self.mean_sq)
+      w_old = float(self.count) / new_total
+      w_new = float(new_count) / new_total
+
+      self.mean = w_old * self.mean + w_new * new_mean
+      self.mean_sq = w_old * self.mean_sq + w_new * new_mean_sq
+      self.count = new_total
+      self.std = self.calc_std(self.mean, self.mean_sq)
+
+      self.new_count = 0
+      self.new_sum.fill(0)
+      self.new_sum_sq.fill(0)
+
+    return
+
+  def get_size(self):
+    return self.mean.size
+
+  def set_mean_std(self, mean, std):
+    size = self.get_size()
+    is_array = isinstance(mean, np.ndarray) and isinstance(std, np.ndarray)
+
+    if not is_array:
+      assert (size == 1)
+      mean = np.array([mean])
+      std = np.array([std])
+
+    assert len(mean) == size and len(std) == size, \
+        Logger.print2('Normalizer shape mismatch, expecting size {:d}, but got {:d} and {:d}'.format(size, len(mean), len(std)))
+
+    self.mean = mean
+    self.std = std
+    self.mean_sq = self.calc_mean_sq(self.mean, self.std)
+    return
+
+  def normalize(self, x):
+    norm_x = (x - self.mean) / self.std
+    norm_x = np.clip(norm_x, -self.clip, self.clip)
+    return norm_x
+
+  def unnormalize(self, norm_x):
+    x = norm_x * self.std + self.mean
+    return x
+
+  def calc_std(self, mean, mean_sq):
+    var = mean_sq - np.square(mean)
+    # some time floating point errors can lead to small negative numbers
+    var = np.maximum(var, 0)
+    std = np.sqrt(var)
+    std = np.maximum(std, self.eps)
+    return std
+
+  def calc_mean_sq(self, mean, std):
+    return np.square(std) + np.square(self.mean)
+
+  def check_synced(self):
+    synced = True
+    if MPIUtil.is_root_proc():
+      vars = np.concatenate([self.mean, self.mean_sq])
+      MPIUtil.bcast(vars)
+    else:
+      vars_local = np.concatenate([self.mean, self.mean_sq])
+      vars_root = np.empty_like(vars_local)
+      MPIUtil.bcast(vars_root)
+      synced = (vars_local == vars_root).all()
+    return synced
+
+  def _build_groups(self, groups_ids):
+    groups = []
+    if groups_ids is None:
+      curr_id = self.NORM_GROUP_SINGLE
+      curr_list = np.arange(self.get_size()).astype(np.int32)
+      groups.append(self.Group(curr_id, curr_list))
+    else:
+      ids = np.unique(groups_ids)
+      for id in ids:
+        curr_list = np.nonzero(groups_ids == id)[0].astype(np.int32)
+        groups.append(self.Group(id, curr_list))
+
+    return groups
+
+  def _process_group_data(self, new_data, old_data):
+    proc_data = new_data.copy()
+    for group in self.groups:
+      if group.id == self.NORM_GROUP_NONE:
+        proc_data[group.indices] = old_data[group.indices]
+      elif group.id != self.NORM_GROUP_SINGLE:
+        avg = np.mean(new_data[group.indices])
+        proc_data[group.indices] = avg
+    return proc_data
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/path.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/path.py
index c6855491e..37ae1146c 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/path.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/path.py
@@ -1,46 +1,47 @@
 import numpy as np
 from pybullet_envs.deep_mimic.env.env import Env
 
+
 class Path(object):
-    def __init__(self):
-        self.clear()
-        return
-
-    def pathlength(self):
-        return len(self.actions)
-
-    def is_valid(self):
-        valid = True
-        l = self.pathlength()
-        valid &= len(self.states) == l + 1
-        valid &= len(self.goals) == l + 1
-        valid &= len(self.actions) == l
-        valid &= len(self.logps) == l
-        valid &= len(self.rewards) == l
-        valid &= len(self.flags) == l
-
-        return valid
-
-    def check_vals(self):
-        for vals in [self.states, self.goals, self.actions, self.logps,
-                  self.rewards]:
-            for v in vals:
-                if not np.isfinite(v).all():
-                    return False
-        return True
-
-    def clear(self):
-        self.states = []
-        self.goals = []
-        self.actions = []
-        self.logps = []
-        self.rewards = []
-        self.flags = []
-        self.terminate = Env.Terminate.Null
-        return
-
-    def get_pathlen(self):
-        return len(self.rewards)
-
-    def calc_return(self):
-        return sum(self.rewards)
\ No newline at end of file
+
+  def __init__(self):
+    self.clear()
+    return
+
+  def pathlength(self):
+    return len(self.actions)
+
+  def is_valid(self):
+    valid = True
+    l = self.pathlength()
+    valid &= len(self.states) == l + 1
+    valid &= len(self.goals) == l + 1
+    valid &= len(self.actions) == l
+    valid &= len(self.logps) == l
+    valid &= len(self.rewards) == l
+    valid &= len(self.flags) == l
+
+    return valid
+
+  def check_vals(self):
+    for vals in [self.states, self.goals, self.actions, self.logps, self.rewards]:
+      for v in vals:
+        if not np.isfinite(v).all():
+          return False
+    return True
+
+  def clear(self):
+    self.states = []
+    self.goals = []
+    self.actions = []
+    self.logps = []
+    self.rewards = []
+    self.flags = []
+    self.terminate = Env.Terminate.Null
+    return
+
+  def get_pathlen(self):
+    return len(self.rewards)
+
+  def calc_return(self):
+    return sum(self.rewards)
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/pg_agent.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/pg_agent.py
index 75d1bae9a..2787e2356 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/pg_agent.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/pg_agent.py
@@ -13,341 +13,343 @@ import pybullet_utils.mpi_util as MPIUtil
 import pybullet_utils.math_util as MathUtil
 from pybullet_envs.deep_mimic.env.action_space import ActionSpace
 from pybullet_envs.deep_mimic.env.env import Env
-
 '''
 Policy Gradient Agent
 '''
 
+
 class PGAgent(TFAgent):
-    NAME = 'PG'
-
-    ACTOR_NET_KEY = 'ActorNet'
-    ACTOR_STEPSIZE_KEY = 'ActorStepsize'
-    ACTOR_MOMENTUM_KEY = 'ActorMomentum'
-    ACTOR_WEIGHT_DECAY_KEY = 'ActorWeightDecay'
-    ACTOR_INIT_OUTPUT_SCALE_KEY = 'ActorInitOutputScale'
-
-    CRITIC_NET_KEY = 'CriticNet'
-    CRITIC_STEPSIZE_KEY = 'CriticStepsize'
-    CRITIC_MOMENTUM_KEY = 'CriticMomentum'
-    CRITIC_WEIGHT_DECAY_KEY = 'CriticWeightDecay'
-    
-    EXP_ACTION_FLAG = 1 << 0
-
-    def __init__(self, world, id, json_data): 
-        self._exp_action = False
-        super().__init__(world, id, json_data)
-        return
-
-    def reset(self):
-        super().reset()
-        self._exp_action = False
-        return
-
-    def _check_action_space(self):
-        action_space = self.get_action_space()
-        return action_space == ActionSpace.Continuous
-
-    def _load_params(self, json_data):
-        super()._load_params(json_data)
-        self.val_min, self.val_max = self._calc_val_bounds(self.discount)
-        self.val_fail, self.val_succ = self._calc_term_vals(self.discount)
-        return
-
-    def _build_nets(self, json_data):
-        assert self.ACTOR_NET_KEY in json_data
-        assert self.CRITIC_NET_KEY in json_data
-
-        actor_net_name = json_data[self.ACTOR_NET_KEY]
-        critic_net_name = json_data[self.CRITIC_NET_KEY]
-        actor_init_output_scale = 1 if (self.ACTOR_INIT_OUTPUT_SCALE_KEY not in json_data) else json_data[self.ACTOR_INIT_OUTPUT_SCALE_KEY]
-        
-        s_size = self.get_state_size()
-        g_size = self.get_goal_size()
-        a_size = self.get_action_size()
-
-        # setup input tensors
-        self.s_tf = tf.placeholder(tf.float32, shape=[None, s_size], name="s") # observations
-        self.tar_val_tf = tf.placeholder(tf.float32, shape=[None], name="tar_val") # target value s
-        self.adv_tf = tf.placeholder(tf.float32, shape=[None], name="adv") # advantage
-        self.a_tf = tf.placeholder(tf.float32, shape=[None, a_size], name="a") # target actions
-        self.g_tf = tf.placeholder(tf.float32, shape=([None, g_size] if self.has_goal() else None), name="g") # goals
-
-        with tf.variable_scope('main'):
-            with tf.variable_scope('actor'):
-                self.actor_tf = self._build_net_actor(actor_net_name, actor_init_output_scale)
-            with tf.variable_scope('critic'):
-                self.critic_tf = self._build_net_critic(critic_net_name)
-
-        if (self.actor_tf != None):
-            Logger.print2('Built actor net: ' + actor_net_name)
-
-        if (self.critic_tf != None):
-            Logger.print2('Built critic net: ' + critic_net_name)
-
-        return
-
-    def _build_normalizers(self):
-        super()._build_normalizers()
-        with self.sess.as_default(), self.graph.as_default(), tf.variable_scope(self.tf_scope):
-            with tf.variable_scope(self.RESOURCE_SCOPE):
-                val_offset, val_scale = self._calc_val_offset_scale(self.discount)
-                self.val_norm = TFNormalizer(self.sess, 'val_norm', 1)
-                self.val_norm.set_mean_std(-val_offset, 1.0 / val_scale)
-        return
-
-    def _init_normalizers(self):
-        super()._init_normalizers()
-        with self.sess.as_default(), self.graph.as_default():
-            self.val_norm.update()
-        return
-
-    def _load_normalizers(self):
-        super()._load_normalizers()
-        self.val_norm.load()
-        return
-
-    def _build_losses(self, json_data):
-        actor_weight_decay = 0 if (self.ACTOR_WEIGHT_DECAY_KEY not in json_data) else json_data[self.ACTOR_WEIGHT_DECAY_KEY]
-        critic_weight_decay = 0 if (self.CRITIC_WEIGHT_DECAY_KEY not in json_data) else json_data[self.CRITIC_WEIGHT_DECAY_KEY]
-
-        norm_val_diff = self.val_norm.normalize_tf(self.tar_val_tf) - self.val_norm.normalize_tf(self.critic_tf)
-        self.critic_loss_tf = 0.5 * tf.reduce_mean(tf.square(norm_val_diff))
-
-        if (critic_weight_decay != 0):
-            self.critic_loss_tf += critic_weight_decay * self._weight_decay_loss('main/critic')
-        
-        norm_a_mean_tf = self.a_norm.normalize_tf(self.actor_tf)
-        norm_a_diff = self.a_norm.normalize_tf(self.a_tf) - norm_a_mean_tf
-
-        self.actor_loss_tf = tf.reduce_sum(tf.square(norm_a_diff), axis=-1)
-        self.actor_loss_tf *= self.adv_tf
-        self.actor_loss_tf = 0.5 * tf.reduce_mean(self.actor_loss_tf)
-
-        norm_a_bound_min = self.a_norm.normalize(self.a_bound_min)
-        norm_a_bound_max = self.a_norm.normalize(self.a_bound_max)
-        a_bound_loss = TFUtil.calc_bound_loss(norm_a_mean_tf, norm_a_bound_min, norm_a_bound_max)
-        a_bound_loss /= self.exp_params_curr.noise
-        self.actor_loss_tf += a_bound_loss
-
-        if (actor_weight_decay != 0):
-            self.actor_loss_tf += actor_weight_decay * self._weight_decay_loss('main/actor')
-        
-        return
-
-    def _build_solvers(self, json_data):
-        actor_stepsize = 0.001 if (self.ACTOR_STEPSIZE_KEY not in json_data) else json_data[self.ACTOR_STEPSIZE_KEY]
-        actor_momentum = 0.9 if (self.ACTOR_MOMENTUM_KEY not in json_data) else json_data[self.ACTOR_MOMENTUM_KEY]
-        critic_stepsize = 0.01 if (self.CRITIC_STEPSIZE_KEY not in json_data) else json_data[self.CRITIC_STEPSIZE_KEY]
-        critic_momentum = 0.9 if (self.CRITIC_MOMENTUM_KEY not in json_data) else json_data[self.CRITIC_MOMENTUM_KEY]
-        
-        critic_vars = self._tf_vars('main/critic')
-        critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize, momentum=critic_momentum)
-        self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars)
-        self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars)
-
-        actor_vars = self._tf_vars('main/actor')
-        actor_opt = tf.train.MomentumOptimizer(learning_rate=actor_stepsize, momentum=actor_momentum)
-        self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars)
-        self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars)
-
-        return
-
-    def _build_net_actor(self, net_name, init_output_scale):
-        norm_s_tf = self.s_norm.normalize_tf(self.s_tf)
-        input_tfs = [norm_s_tf]
-        if (self.has_goal()):
-            norm_g_tf = self.g_norm.normalize_tf(self.g_tf)
-            input_tfs += [norm_g_tf]
-        
-        h = NetBuilder.build_net(net_name, input_tfs)
-        norm_a_tf = tf.layers.dense(inputs=h, units=self.get_action_size(), activation=None,
-                                kernel_initializer=tf.random_uniform_initializer(minval=-init_output_scale, maxval=init_output_scale))
-        
-        a_tf = self.a_norm.unnormalize_tf(norm_a_tf)
-        return a_tf
-    
-    def _build_net_critic(self, net_name):
-        norm_s_tf = self.s_norm.normalize_tf(self.s_tf)
-        input_tfs = [norm_s_tf]
-        if (self.has_goal()):
-            norm_g_tf = self.g_norm.normalize_tf(self.g_tf)
-            input_tfs += [norm_g_tf]
-        
-        h = NetBuilder.build_net(net_name, input_tfs)
-        norm_val_tf = tf.layers.dense(inputs=h, units=1, activation=None,
-                                kernel_initializer=TFUtil.xavier_initializer);
-
-        norm_val_tf = tf.reshape(norm_val_tf, [-1])
-        val_tf = self.val_norm.unnormalize_tf(norm_val_tf)
-        return val_tf
-
-    def _initialize_vars(self):
-        super()._initialize_vars()
-        self._sync_solvers()
-        return
-
-    def _sync_solvers(self):
-        self.actor_solver.sync()
-        self.critic_solver.sync()
-        return
-
-    def _decide_action(self, s, g):
-        with self.sess.as_default(), self.graph.as_default():
-            self._exp_action = False
-            a = self._eval_actor(s, g)[0]
-            logp = 0
-
-            if self._enable_stoch_policy():
-                # epsilon-greedy
-                rand_action = MathUtil.flip_coin(self.exp_params_curr.rate)
-                if rand_action:
-                    norm_exp_noise = np.random.randn(*a.shape)
-                    norm_exp_noise *= self.exp_params_curr.noise
-                    exp_noise = norm_exp_noise * self.a_norm.std
-                    a += exp_noise
-
-                    logp = self._calc_action_logp(norm_exp_noise)
-                    self._exp_action = True
-
-        return a, logp
-
-    def _enable_stoch_policy(self):
-        return self.enable_training and (self._mode == self.Mode.TRAIN or self._mode == self.Mode.TRAIN_END)
-
-    def _eval_actor(self, s, g):
-        s = np.reshape(s, [-1, self.get_state_size()])
-        g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None
-          
-        feed = {
-            self.s_tf : s,
-            self.g_tf : g
-        }
-
-        a = self.actor_tf.eval(feed)
-        return a
-    
-    def _eval_critic(self, s, g):
-        with self.sess.as_default(), self.graph.as_default():
-            s = np.reshape(s, [-1, self.get_state_size()])
-            g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None
-
-            feed = {
-                self.s_tf : s,
-                self.g_tf : g
-            }
-
-            val = self.critic_tf.eval(feed)    
-        return val
-
-    def _record_flags(self):
-        flags = int(0)
-        if (self._exp_action):
-            flags = flags | self.EXP_ACTION_FLAG
-        return flags
-
-    def _train_step(self):
-        super()._train_step()
-
-        critic_loss = self._update_critic()
-        actor_loss = self._update_actor()
-        critic_loss = MPIUtil.reduce_avg(critic_loss)
-        actor_loss = MPIUtil.reduce_avg(actor_loss)
-
-        critic_stepsize = self.critic_solver.get_stepsize()
-        actor_stepsize = self.actor_solver.get_stepsize()
-        
-        self.logger.log_tabular('Critic_Loss', critic_loss)
-        self.logger.log_tabular('Critic_Stepsize', critic_stepsize)
-        self.logger.log_tabular('Actor_Loss', actor_loss) 
-        self.logger.log_tabular('Actor_Stepsize', actor_stepsize)
-
-        return
-
-    def _update_critic(self):
-        idx = self.replay_buffer.sample(self._local_mini_batch_size)
-        s = self.replay_buffer.get('states', idx)
-        g = self.replay_buffer.get('goals', idx) if self.has_goal() else None
-        
-        tar_V = self._calc_updated_vals(idx)
-        tar_V = np.clip(tar_V, self.val_min, self.val_max)
-
-        feed = {
-            self.s_tf: s,
-            self.g_tf: g,
-            self.tar_val_tf: tar_V
-        }
-
-        loss, grads = self.sess.run([self.critic_loss_tf, self.critic_grad_tf], feed)
-        self.critic_solver.update(grads)
-        return loss
-    
-    def _update_actor(self):
-        key = self.EXP_ACTION_FLAG
-        idx = self.replay_buffer.sample_filtered(self._local_mini_batch_size, key)
-        has_goal = self.has_goal()
-
-        s = self.replay_buffer.get('states', idx)
-        g = self.replay_buffer.get('goals', idx) if has_goal else None
-        a = self.replay_buffer.get('actions', idx)
-
-        V_new = self._calc_updated_vals(idx)
-        V_old = self._eval_critic(s, g)
-        adv = V_new - V_old
-
-        feed = {
-            self.s_tf: s,
-            self.g_tf: g,
-            self.a_tf: a,
-            self.adv_tf: adv
-        }
-
-        loss, grads = self.sess.run([self.actor_loss_tf, self.actor_grad_tf], feed)
-        self.actor_solver.update(grads)
-
-        return loss
-
-    def _calc_updated_vals(self, idx):
-        r = self.replay_buffer.get('rewards', idx)
-
-        if self.discount == 0:
-            new_V = r
-        else:
-            next_idx = self.replay_buffer.get_next_idx(idx)
-            s_next = self.replay_buffer.get('states', next_idx)
-            g_next = self.replay_buffer.get('goals', next_idx) if self.has_goal() else None
-
-            is_end = self.replay_buffer.is_path_end(idx)
-            is_fail = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Fail)
-            is_succ = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Succ)
-            is_fail = np.logical_and(is_end, is_fail) 
-            is_succ = np.logical_and(is_end, is_succ) 
-
-            V_next = self._eval_critic(s_next, g_next)
-            V_next[is_fail] = self.val_fail
-            V_next[is_succ] = self.val_succ
-
-            new_V = r + self.discount * V_next
-        return new_V
-
-    def _calc_action_logp(self, norm_action_deltas):
-        # norm action delta are for the normalized actions (scaled by self.a_norm.std) 
-        stdev = self.exp_params_curr.noise
-        assert stdev > 0
-
-        a_size = self.get_action_size()
-        logp = -0.5 / (stdev * stdev) * np.sum(np.square(norm_action_deltas), axis=-1)
-        logp += -0.5 * a_size * np.log(2 * np.pi)
-        logp += -a_size * np.log(stdev)
-        return logp
-
-    def _log_val(self, s, g):
-        val = self._eval_critic(s, g)
-        norm_val = self.val_norm.normalize(val)
-        self.world.env.log_val(self.id, norm_val[0])
-        return
-
-    def _build_replay_buffer(self, buffer_size):
-        super()._build_replay_buffer(buffer_size)
-        self.replay_buffer.add_filter_key(self.EXP_ACTION_FLAG)
-        return
+  NAME = 'PG'
+
+  ACTOR_NET_KEY = 'ActorNet'
+  ACTOR_STEPSIZE_KEY = 'ActorStepsize'
+  ACTOR_MOMENTUM_KEY = 'ActorMomentum'
+  ACTOR_WEIGHT_DECAY_KEY = 'ActorWeightDecay'
+  ACTOR_INIT_OUTPUT_SCALE_KEY = 'ActorInitOutputScale'
+
+  CRITIC_NET_KEY = 'CriticNet'
+  CRITIC_STEPSIZE_KEY = 'CriticStepsize'
+  CRITIC_MOMENTUM_KEY = 'CriticMomentum'
+  CRITIC_WEIGHT_DECAY_KEY = 'CriticWeightDecay'
+
+  EXP_ACTION_FLAG = 1 << 0
+
+  def __init__(self, world, id, json_data):
+    self._exp_action = False
+    super().__init__(world, id, json_data)
+    return
+
+  def reset(self):
+    super().reset()
+    self._exp_action = False
+    return
+
+  def _check_action_space(self):
+    action_space = self.get_action_space()
+    return action_space == ActionSpace.Continuous
+
+  def _load_params(self, json_data):
+    super()._load_params(json_data)
+    self.val_min, self.val_max = self._calc_val_bounds(self.discount)
+    self.val_fail, self.val_succ = self._calc_term_vals(self.discount)
+    return
+
+  def _build_nets(self, json_data):
+    assert self.ACTOR_NET_KEY in json_data
+    assert self.CRITIC_NET_KEY in json_data
+
+    actor_net_name = json_data[self.ACTOR_NET_KEY]
+    critic_net_name = json_data[self.CRITIC_NET_KEY]
+    actor_init_output_scale = 1 if (self.ACTOR_INIT_OUTPUT_SCALE_KEY not in json_data
+                                   ) else json_data[self.ACTOR_INIT_OUTPUT_SCALE_KEY]
+
+    s_size = self.get_state_size()
+    g_size = self.get_goal_size()
+    a_size = self.get_action_size()
+
+    # setup input tensors
+    self.s_tf = tf.placeholder(tf.float32, shape=[None, s_size], name="s")  # observations
+    self.tar_val_tf = tf.placeholder(tf.float32, shape=[None], name="tar_val")  # target value s
+    self.adv_tf = tf.placeholder(tf.float32, shape=[None], name="adv")  # advantage
+    self.a_tf = tf.placeholder(tf.float32, shape=[None, a_size], name="a")  # target actions
+    self.g_tf = tf.placeholder(tf.float32,
+                               shape=([None, g_size] if self.has_goal() else None),
+                               name="g")  # goals
+
+    with tf.variable_scope('main'):
+      with tf.variable_scope('actor'):
+        self.actor_tf = self._build_net_actor(actor_net_name, actor_init_output_scale)
+      with tf.variable_scope('critic'):
+        self.critic_tf = self._build_net_critic(critic_net_name)
+
+    if (self.actor_tf != None):
+      Logger.print2('Built actor net: ' + actor_net_name)
+
+    if (self.critic_tf != None):
+      Logger.print2('Built critic net: ' + critic_net_name)
+
+    return
+
+  def _build_normalizers(self):
+    super()._build_normalizers()
+    with self.sess.as_default(), self.graph.as_default(), tf.variable_scope(self.tf_scope):
+      with tf.variable_scope(self.RESOURCE_SCOPE):
+        val_offset, val_scale = self._calc_val_offset_scale(self.discount)
+        self.val_norm = TFNormalizer(self.sess, 'val_norm', 1)
+        self.val_norm.set_mean_std(-val_offset, 1.0 / val_scale)
+    return
+
+  def _init_normalizers(self):
+    super()._init_normalizers()
+    with self.sess.as_default(), self.graph.as_default():
+      self.val_norm.update()
+    return
+
+  def _load_normalizers(self):
+    super()._load_normalizers()
+    self.val_norm.load()
+    return
+
+  def _build_losses(self, json_data):
+    actor_weight_decay = 0 if (
+        self.ACTOR_WEIGHT_DECAY_KEY not in json_data) else json_data[self.ACTOR_WEIGHT_DECAY_KEY]
+    critic_weight_decay = 0 if (
+        self.CRITIC_WEIGHT_DECAY_KEY not in json_data) else json_data[self.CRITIC_WEIGHT_DECAY_KEY]
+
+    norm_val_diff = self.val_norm.normalize_tf(self.tar_val_tf) - self.val_norm.normalize_tf(
+        self.critic_tf)
+    self.critic_loss_tf = 0.5 * tf.reduce_mean(tf.square(norm_val_diff))
+
+    if (critic_weight_decay != 0):
+      self.critic_loss_tf += critic_weight_decay * self._weight_decay_loss('main/critic')
+
+    norm_a_mean_tf = self.a_norm.normalize_tf(self.actor_tf)
+    norm_a_diff = self.a_norm.normalize_tf(self.a_tf) - norm_a_mean_tf
+
+    self.actor_loss_tf = tf.reduce_sum(tf.square(norm_a_diff), axis=-1)
+    self.actor_loss_tf *= self.adv_tf
+    self.actor_loss_tf = 0.5 * tf.reduce_mean(self.actor_loss_tf)
+
+    norm_a_bound_min = self.a_norm.normalize(self.a_bound_min)
+    norm_a_bound_max = self.a_norm.normalize(self.a_bound_max)
+    a_bound_loss = TFUtil.calc_bound_loss(norm_a_mean_tf, norm_a_bound_min, norm_a_bound_max)
+    a_bound_loss /= self.exp_params_curr.noise
+    self.actor_loss_tf += a_bound_loss
+
+    if (actor_weight_decay != 0):
+      self.actor_loss_tf += actor_weight_decay * self._weight_decay_loss('main/actor')
+
+    return
+
+  def _build_solvers(self, json_data):
+    actor_stepsize = 0.001 if (
+        self.ACTOR_STEPSIZE_KEY not in json_data) else json_data[self.ACTOR_STEPSIZE_KEY]
+    actor_momentum = 0.9 if (
+        self.ACTOR_MOMENTUM_KEY not in json_data) else json_data[self.ACTOR_MOMENTUM_KEY]
+    critic_stepsize = 0.01 if (
+        self.CRITIC_STEPSIZE_KEY not in json_data) else json_data[self.CRITIC_STEPSIZE_KEY]
+    critic_momentum = 0.9 if (
+        self.CRITIC_MOMENTUM_KEY not in json_data) else json_data[self.CRITIC_MOMENTUM_KEY]
+
+    critic_vars = self._tf_vars('main/critic')
+    critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize,
+                                            momentum=critic_momentum)
+    self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars)
+    self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars)
+
+    actor_vars = self._tf_vars('main/actor')
+    actor_opt = tf.train.MomentumOptimizer(learning_rate=actor_stepsize, momentum=actor_momentum)
+    self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars)
+    self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars)
+
+    return
+
+  def _build_net_actor(self, net_name, init_output_scale):
+    norm_s_tf = self.s_norm.normalize_tf(self.s_tf)
+    input_tfs = [norm_s_tf]
+    if (self.has_goal()):
+      norm_g_tf = self.g_norm.normalize_tf(self.g_tf)
+      input_tfs += [norm_g_tf]
+
+    h = NetBuilder.build_net(net_name, input_tfs)
+    norm_a_tf = tf.layers.dense(inputs=h,
+                                units=self.get_action_size(),
+                                activation=None,
+                                kernel_initializer=tf.random_uniform_initializer(
+                                    minval=-init_output_scale, maxval=init_output_scale))
+
+    a_tf = self.a_norm.unnormalize_tf(norm_a_tf)
+    return a_tf
+
+  def _build_net_critic(self, net_name):
+    norm_s_tf = self.s_norm.normalize_tf(self.s_tf)
+    input_tfs = [norm_s_tf]
+    if (self.has_goal()):
+      norm_g_tf = self.g_norm.normalize_tf(self.g_tf)
+      input_tfs += [norm_g_tf]
+
+    h = NetBuilder.build_net(net_name, input_tfs)
+    norm_val_tf = tf.layers.dense(inputs=h,
+                                  units=1,
+                                  activation=None,
+                                  kernel_initializer=TFUtil.xavier_initializer)
+
+    norm_val_tf = tf.reshape(norm_val_tf, [-1])
+    val_tf = self.val_norm.unnormalize_tf(norm_val_tf)
+    return val_tf
+
+  def _initialize_vars(self):
+    super()._initialize_vars()
+    self._sync_solvers()
+    return
+
+  def _sync_solvers(self):
+    self.actor_solver.sync()
+    self.critic_solver.sync()
+    return
+
+  def _decide_action(self, s, g):
+    with self.sess.as_default(), self.graph.as_default():
+      self._exp_action = False
+      a = self._eval_actor(s, g)[0]
+      logp = 0
+
+      if self._enable_stoch_policy():
+        # epsilon-greedy
+        rand_action = MathUtil.flip_coin(self.exp_params_curr.rate)
+        if rand_action:
+          norm_exp_noise = np.random.randn(*a.shape)
+          norm_exp_noise *= self.exp_params_curr.noise
+          exp_noise = norm_exp_noise * self.a_norm.std
+          a += exp_noise
+
+          logp = self._calc_action_logp(norm_exp_noise)
+          self._exp_action = True
+
+    return a, logp
+
+  def _enable_stoch_policy(self):
+    return self.enable_training and (self._mode == self.Mode.TRAIN or
+                                     self._mode == self.Mode.TRAIN_END)
+
+  def _eval_actor(self, s, g):
+    s = np.reshape(s, [-1, self.get_state_size()])
+    g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None
+
+    feed = {self.s_tf: s, self.g_tf: g}
+
+    a = self.actor_tf.eval(feed)
+    return a
+
+  def _eval_critic(self, s, g):
+    with self.sess.as_default(), self.graph.as_default():
+      s = np.reshape(s, [-1, self.get_state_size()])
+      g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None
+
+      feed = {self.s_tf: s, self.g_tf: g}
+
+      val = self.critic_tf.eval(feed)
+    return val
+
+  def _record_flags(self):
+    flags = int(0)
+    if (self._exp_action):
+      flags = flags | self.EXP_ACTION_FLAG
+    return flags
+
+  def _train_step(self):
+    super()._train_step()
+
+    critic_loss = self._update_critic()
+    actor_loss = self._update_actor()
+    critic_loss = MPIUtil.reduce_avg(critic_loss)
+    actor_loss = MPIUtil.reduce_avg(actor_loss)
+
+    critic_stepsize = self.critic_solver.get_stepsize()
+    actor_stepsize = self.actor_solver.get_stepsize()
+
+    self.logger.log_tabular('Critic_Loss', critic_loss)
+    self.logger.log_tabular('Critic_Stepsize', critic_stepsize)
+    self.logger.log_tabular('Actor_Loss', actor_loss)
+    self.logger.log_tabular('Actor_Stepsize', actor_stepsize)
+
+    return
+
+  def _update_critic(self):
+    idx = self.replay_buffer.sample(self._local_mini_batch_size)
+    s = self.replay_buffer.get('states', idx)
+    g = self.replay_buffer.get('goals', idx) if self.has_goal() else None
+
+    tar_V = self._calc_updated_vals(idx)
+    tar_V = np.clip(tar_V, self.val_min, self.val_max)
+
+    feed = {self.s_tf: s, self.g_tf: g, self.tar_val_tf: tar_V}
+
+    loss, grads = self.sess.run([self.critic_loss_tf, self.critic_grad_tf], feed)
+    self.critic_solver.update(grads)
+    return loss
+
+  def _update_actor(self):
+    key = self.EXP_ACTION_FLAG
+    idx = self.replay_buffer.sample_filtered(self._local_mini_batch_size, key)
+    has_goal = self.has_goal()
+
+    s = self.replay_buffer.get('states', idx)
+    g = self.replay_buffer.get('goals', idx) if has_goal else None
+    a = self.replay_buffer.get('actions', idx)
+
+    V_new = self._calc_updated_vals(idx)
+    V_old = self._eval_critic(s, g)
+    adv = V_new - V_old
+
+    feed = {self.s_tf: s, self.g_tf: g, self.a_tf: a, self.adv_tf: adv}
+
+    loss, grads = self.sess.run([self.actor_loss_tf, self.actor_grad_tf], feed)
+    self.actor_solver.update(grads)
+
+    return loss
+
+  def _calc_updated_vals(self, idx):
+    r = self.replay_buffer.get('rewards', idx)
+
+    if self.discount == 0:
+      new_V = r
+    else:
+      next_idx = self.replay_buffer.get_next_idx(idx)
+      s_next = self.replay_buffer.get('states', next_idx)
+      g_next = self.replay_buffer.get('goals', next_idx) if self.has_goal() else None
+
+      is_end = self.replay_buffer.is_path_end(idx)
+      is_fail = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Fail)
+      is_succ = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Succ)
+      is_fail = np.logical_and(is_end, is_fail)
+      is_succ = np.logical_and(is_end, is_succ)
+
+      V_next = self._eval_critic(s_next, g_next)
+      V_next[is_fail] = self.val_fail
+      V_next[is_succ] = self.val_succ
+
+      new_V = r + self.discount * V_next
+    return new_V
+
+  def _calc_action_logp(self, norm_action_deltas):
+    # norm action delta are for the normalized actions (scaled by self.a_norm.std)
+    stdev = self.exp_params_curr.noise
+    assert stdev > 0
+
+    a_size = self.get_action_size()
+    logp = -0.5 / (stdev * stdev) * np.sum(np.square(norm_action_deltas), axis=-1)
+    logp += -0.5 * a_size * np.log(2 * np.pi)
+    logp += -a_size * np.log(stdev)
+    return logp
+
+  def _log_val(self, s, g):
+    val = self._eval_critic(s, g)
+    norm_val = self.val_norm.normalize(val)
+    self.world.env.log_val(self.id, norm_val[0])
+    return
+
+  def _build_replay_buffer(self, buffer_size):
+    super()._build_replay_buffer(buffer_size)
+    self.replay_buffer.add_filter_key(self.EXP_ACTION_FLAG)
+    return
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/ppo_agent.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/ppo_agent.py
index ff66ba004..125a1bb38 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/ppo_agent.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/ppo_agent.py
@@ -10,359 +10,374 @@ from pybullet_utils.logger import Logger
 import pybullet_utils.mpi_util as MPIUtil
 import pybullet_utils.math_util as MathUtil
 from pybullet_envs.deep_mimic.env.env import Env
-
 '''
 Proximal Policy Optimization Agent
 '''
 
+
 class PPOAgent(PGAgent):
-    NAME = "PPO"
-    EPOCHS_KEY = "Epochs"
-    BATCH_SIZE_KEY = "BatchSize"
-    RATIO_CLIP_KEY = "RatioClip"
-    NORM_ADV_CLIP_KEY = "NormAdvClip"
-    TD_LAMBDA_KEY = "TDLambda"
-    TAR_CLIP_FRAC = "TarClipFrac"
-    ACTOR_STEPSIZE_DECAY = "ActorStepsizeDecay"
-
-    def __init__(self, world, id, json_data): 
-        super().__init__(world, id, json_data)
-        return
-
-    def _load_params(self, json_data):
-        super()._load_params(json_data)
-
-        self.epochs = 1 if (self.EPOCHS_KEY not in json_data) else json_data[self.EPOCHS_KEY]
-        self.batch_size = 1024 if (self.BATCH_SIZE_KEY not in json_data) else json_data[self.BATCH_SIZE_KEY]
-        self.ratio_clip = 0.2 if (self.RATIO_CLIP_KEY not in json_data) else json_data[self.RATIO_CLIP_KEY]
-        self.norm_adv_clip = 5 if (self.NORM_ADV_CLIP_KEY not in json_data) else json_data[self.NORM_ADV_CLIP_KEY]
-        self.td_lambda = 0.95 if (self.TD_LAMBDA_KEY not in json_data) else json_data[self.TD_LAMBDA_KEY]
-        self.tar_clip_frac = -1 if (self.TAR_CLIP_FRAC not in json_data) else json_data[self.TAR_CLIP_FRAC]
-        self.actor_stepsize_decay = 0.5 if (self.ACTOR_STEPSIZE_DECAY not in json_data) else json_data[self.ACTOR_STEPSIZE_DECAY]
-
-        num_procs = MPIUtil.get_num_procs()
-        local_batch_size = int(self.batch_size / num_procs)
-        min_replay_size = 2 * local_batch_size # needed to prevent buffer overflow
-        assert(self.replay_buffer_size > min_replay_size)
-
-        self.replay_buffer_size = np.maximum(min_replay_size, self.replay_buffer_size)
-
-        return
-
-    def _build_nets(self, json_data):
-        assert self.ACTOR_NET_KEY in json_data
-        assert self.CRITIC_NET_KEY in json_data
-
-        actor_net_name = json_data[self.ACTOR_NET_KEY]
-        critic_net_name = json_data[self.CRITIC_NET_KEY]
-        actor_init_output_scale = 1 if (self.ACTOR_INIT_OUTPUT_SCALE_KEY not in json_data) else json_data[self.ACTOR_INIT_OUTPUT_SCALE_KEY]
-
-        s_size = self.get_state_size()
-        g_size = self.get_goal_size()
-        a_size = self.get_action_size()
-
-        # setup input tensors
-        self.s_tf = tf.placeholder(tf.float32, shape=[None, s_size], name="s")
-        self.a_tf = tf.placeholder(tf.float32, shape=[None, a_size], name="a")
-        self.tar_val_tf = tf.placeholder(tf.float32, shape=[None], name="tar_val")
-        self.adv_tf = tf.placeholder(tf.float32, shape=[None], name="adv")
-        self.g_tf = tf.placeholder(tf.float32, shape=([None, g_size] if self.has_goal() else None), name="g")
-        self.old_logp_tf = tf.placeholder(tf.float32, shape=[None], name="old_logp")
-        self.exp_mask_tf = tf.placeholder(tf.float32, shape=[None], name="exp_mask")
-
-        with tf.variable_scope('main'):
-            with tf.variable_scope('actor'):
-                self.a_mean_tf = self._build_net_actor(actor_net_name, actor_init_output_scale)
-            with tf.variable_scope('critic'):
-                self.critic_tf = self._build_net_critic(critic_net_name)
-                
-        if (self.a_mean_tf != None):
-            Logger.print2('Built actor net: ' + actor_net_name)
-
-        if (self.critic_tf != None):
-            Logger.print2('Built critic net: ' + critic_net_name)
-        
-        self.norm_a_std_tf = self.exp_params_curr.noise * tf.ones(a_size)
-        norm_a_noise_tf = self.norm_a_std_tf * tf.random_normal(shape=tf.shape(self.a_mean_tf))
-        norm_a_noise_tf *= tf.expand_dims(self.exp_mask_tf, axis=-1)
-        self.sample_a_tf = self.a_mean_tf + norm_a_noise_tf * self.a_norm.std_tf
-        self.sample_a_logp_tf = TFUtil.calc_logp_gaussian(x_tf=norm_a_noise_tf, mean_tf=None, std_tf=self.norm_a_std_tf)
-
-        return
-
-    def _build_losses(self, json_data):
-        actor_weight_decay = 0 if (self.ACTOR_WEIGHT_DECAY_KEY not in json_data) else json_data[self.ACTOR_WEIGHT_DECAY_KEY]
-        critic_weight_decay = 0 if (self.CRITIC_WEIGHT_DECAY_KEY not in json_data) else json_data[self.CRITIC_WEIGHT_DECAY_KEY]
-        
-        norm_val_diff = self.val_norm.normalize_tf(self.tar_val_tf) - self.val_norm.normalize_tf(self.critic_tf)
-        self.critic_loss_tf = 0.5 * tf.reduce_mean(tf.square(norm_val_diff))
-
-        if (critic_weight_decay != 0):
-            self.critic_loss_tf += critic_weight_decay * self._weight_decay_loss('main/critic')
-        
-        norm_tar_a_tf = self.a_norm.normalize_tf(self.a_tf)
-        self._norm_a_mean_tf = self.a_norm.normalize_tf(self.a_mean_tf)
-
-        self.logp_tf = TFUtil.calc_logp_gaussian(norm_tar_a_tf, self._norm_a_mean_tf, self.norm_a_std_tf)
-        ratio_tf = tf.exp(self.logp_tf - self.old_logp_tf)
-        actor_loss0 = self.adv_tf * ratio_tf
-        actor_loss1 = self.adv_tf * tf.clip_by_value(ratio_tf, 1.0 - self.ratio_clip, 1 + self.ratio_clip)
-        self.actor_loss_tf = -tf.reduce_mean(tf.minimum(actor_loss0, actor_loss1))
-
-        norm_a_bound_min = self.a_norm.normalize(self.a_bound_min)
-        norm_a_bound_max = self.a_norm.normalize(self.a_bound_max)
-        a_bound_loss = TFUtil.calc_bound_loss(self._norm_a_mean_tf, norm_a_bound_min, norm_a_bound_max)
-        self.actor_loss_tf += a_bound_loss
-
-        if (actor_weight_decay != 0):
-            self.actor_loss_tf += actor_weight_decay * self._weight_decay_loss('main/actor')
-        
-        # for debugging
-        self.clip_frac_tf = tf.reduce_mean(tf.to_float(tf.greater(tf.abs(ratio_tf - 1.0), self.ratio_clip)))
-
-        return
-
-    def _build_solvers(self, json_data):
-        actor_stepsize = 0.001 if (self.ACTOR_STEPSIZE_KEY not in json_data) else json_data[self.ACTOR_STEPSIZE_KEY]
-        actor_momentum = 0.9 if (self.ACTOR_MOMENTUM_KEY not in json_data) else json_data[self.ACTOR_MOMENTUM_KEY]
-        critic_stepsize = 0.01 if (self.CRITIC_STEPSIZE_KEY not in json_data) else json_data[self.CRITIC_STEPSIZE_KEY]
-        critic_momentum = 0.9 if (self.CRITIC_MOMENTUM_KEY not in json_data) else json_data[self.CRITIC_MOMENTUM_KEY]
-        
-        critic_vars = self._tf_vars('main/critic')
-        critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize, momentum=critic_momentum)
-        self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars)
-        self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars)
-
-        self._actor_stepsize_tf = tf.get_variable(dtype=tf.float32, name='actor_stepsize', initializer=actor_stepsize, trainable=False)
-        self._actor_stepsize_ph = tf.get_variable(dtype=tf.float32, name='actor_stepsize_ph', shape=[])
-        self._actor_stepsize_update_op = self._actor_stepsize_tf.assign(self._actor_stepsize_ph)
-
-        actor_vars = self._tf_vars('main/actor')
-        actor_opt = tf.train.MomentumOptimizer(learning_rate=self._actor_stepsize_tf, momentum=actor_momentum)
-        self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars)
-        self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars)
-        
-        return
-
-    def _decide_action(self, s, g):
-        with self.sess.as_default(), self.graph.as_default():
-            self._exp_action = self._enable_stoch_policy() and MathUtil.flip_coin(self.exp_params_curr.rate)
-            #print("_decide_action._exp_action=",self._exp_action)
-            a, logp = self._eval_actor(s, g, self._exp_action)
-        return a[0], logp[0]
-
-    def _eval_actor(self, s, g, enable_exp):
-        s = np.reshape(s, [-1, self.get_state_size()])
-        g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None
-          
-        feed = {
-            self.s_tf : s,
-            self.g_tf : g,
-            self.exp_mask_tf: np.array([1 if enable_exp else 0])
-        }
-
-        a, logp = self.sess.run([self.sample_a_tf, self.sample_a_logp_tf], feed_dict=feed)
-        return a, logp
-
-    def _train_step(self):
-        adv_eps = 1e-5
-
-        start_idx = self.replay_buffer.buffer_tail
-        end_idx = self.replay_buffer.buffer_head
-        assert(start_idx == 0)
-        assert(self.replay_buffer.get_current_size() <= self.replay_buffer.buffer_size) # must avoid overflow
-        assert(start_idx < end_idx)
-
-        idx = np.array(list(range(start_idx, end_idx)))        
-        end_mask = self.replay_buffer.is_path_end(idx)
-        end_mask = np.logical_not(end_mask) 
-        
-        vals = self._compute_batch_vals(start_idx, end_idx)
-        new_vals = self._compute_batch_new_vals(start_idx, end_idx, vals)
-
-        valid_idx = idx[end_mask]
-        exp_idx = self.replay_buffer.get_idx_filtered(self.EXP_ACTION_FLAG).copy()
-        num_valid_idx = valid_idx.shape[0]
-        num_exp_idx = exp_idx.shape[0]
-        exp_idx = np.column_stack([exp_idx, np.array(list(range(0, num_exp_idx)), dtype=np.int32)])
-        
-        local_sample_count = valid_idx.size
-        global_sample_count = int(MPIUtil.reduce_sum(local_sample_count))
-        mini_batches = int(np.ceil(global_sample_count / self.mini_batch_size))
-        
-        adv = new_vals[exp_idx[:,0]] - vals[exp_idx[:,0]]
-        new_vals = np.clip(new_vals, self.val_min, self.val_max)
-
-        adv_mean = np.mean(adv)
-        adv_std = np.std(adv)
-        adv = (adv - adv_mean) / (adv_std + adv_eps)
-        adv = np.clip(adv, -self.norm_adv_clip, self.norm_adv_clip)
-
-        critic_loss = 0
-        actor_loss = 0
-        actor_clip_frac = 0
-
-        for e in range(self.epochs):
-            np.random.shuffle(valid_idx)
-            np.random.shuffle(exp_idx)
-
-            for b in range(mini_batches):
-                batch_idx_beg = b * self._local_mini_batch_size
-                batch_idx_end = batch_idx_beg + self._local_mini_batch_size
-
-                critic_batch = np.array(range(batch_idx_beg, batch_idx_end), dtype=np.int32)
-                actor_batch = critic_batch.copy()
-                critic_batch = np.mod(critic_batch, num_valid_idx)
-                actor_batch = np.mod(actor_batch, num_exp_idx)
-                shuffle_actor = (actor_batch[-1] < actor_batch[0]) or (actor_batch[-1] == num_exp_idx - 1)
-
-                critic_batch = valid_idx[critic_batch]
-                actor_batch = exp_idx[actor_batch]
-                critic_batch_vals = new_vals[critic_batch]
-                actor_batch_adv = adv[actor_batch[:,1]]
-
-                critic_s = self.replay_buffer.get('states', critic_batch)
-                critic_g = self.replay_buffer.get('goals', critic_batch) if self.has_goal() else None
-                curr_critic_loss = self._update_critic(critic_s, critic_g, critic_batch_vals)
-
-                actor_s = self.replay_buffer.get("states", actor_batch[:,0])
-                actor_g = self.replay_buffer.get("goals", actor_batch[:,0]) if self.has_goal() else None
-                actor_a = self.replay_buffer.get("actions", actor_batch[:,0])
-                actor_logp = self.replay_buffer.get("logps", actor_batch[:,0])
-                curr_actor_loss, curr_actor_clip_frac = self._update_actor(actor_s, actor_g, actor_a, actor_logp, actor_batch_adv)
-                
-                critic_loss += curr_critic_loss
-                actor_loss += np.abs(curr_actor_loss)
-                actor_clip_frac += curr_actor_clip_frac
-
-                if (shuffle_actor):
-                    np.random.shuffle(exp_idx)
-
-        total_batches = mini_batches * self.epochs
-        critic_loss /= total_batches
-        actor_loss /= total_batches
-        actor_clip_frac /= total_batches
-
-        critic_loss = MPIUtil.reduce_avg(critic_loss)
-        actor_loss = MPIUtil.reduce_avg(actor_loss)
-        actor_clip_frac = MPIUtil.reduce_avg(actor_clip_frac)
-
-        critic_stepsize = self.critic_solver.get_stepsize()
-        actor_stepsize = self.update_actor_stepsize(actor_clip_frac)
-
-        self.logger.log_tabular('Critic_Loss', critic_loss)
-        self.logger.log_tabular('Critic_Stepsize', critic_stepsize)
-        self.logger.log_tabular('Actor_Loss', actor_loss) 
-        self.logger.log_tabular('Actor_Stepsize', actor_stepsize)
-        self.logger.log_tabular('Clip_Frac', actor_clip_frac)
-        self.logger.log_tabular('Adv_Mean', adv_mean)
-        self.logger.log_tabular('Adv_Std', adv_std)
-
-        self.replay_buffer.clear()
-
-        return
-
-    def _get_iters_per_update(self):
-        return 1
-
-    def _valid_train_step(self):
-        samples = self.replay_buffer.get_current_size()
-        exp_samples = self.replay_buffer.count_filtered(self.EXP_ACTION_FLAG)
-        global_sample_count = int(MPIUtil.reduce_sum(samples))
-        global_exp_min = int(MPIUtil.reduce_min(exp_samples))
-        return (global_sample_count > self.batch_size) and (global_exp_min > 0)
-
-    def _compute_batch_vals(self, start_idx, end_idx):
-        states = self.replay_buffer.get_all("states")[start_idx:end_idx]
-        goals = self.replay_buffer.get_all("goals")[start_idx:end_idx] if self.has_goal() else None
-        
-        idx = np.array(list(range(start_idx, end_idx)))        
-        is_end = self.replay_buffer.is_path_end(idx)
-        is_fail = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Fail)
-        is_succ = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Succ)
-        is_fail = np.logical_and(is_end, is_fail) 
-        is_succ = np.logical_and(is_end, is_succ) 
-
-        vals = self._eval_critic(states, goals)
-        vals[is_fail] = self.val_fail
-        vals[is_succ] = self.val_succ
-
-        return vals
-
-    def _compute_batch_new_vals(self, start_idx, end_idx, val_buffer):
-        rewards = self.replay_buffer.get_all("rewards")[start_idx:end_idx]
-
-        if self.discount == 0:
-            new_vals = rewards.copy()
+  NAME = "PPO"
+  EPOCHS_KEY = "Epochs"
+  BATCH_SIZE_KEY = "BatchSize"
+  RATIO_CLIP_KEY = "RatioClip"
+  NORM_ADV_CLIP_KEY = "NormAdvClip"
+  TD_LAMBDA_KEY = "TDLambda"
+  TAR_CLIP_FRAC = "TarClipFrac"
+  ACTOR_STEPSIZE_DECAY = "ActorStepsizeDecay"
+
+  def __init__(self, world, id, json_data):
+    super().__init__(world, id, json_data)
+    return
+
+  def _load_params(self, json_data):
+    super()._load_params(json_data)
+
+    self.epochs = 1 if (self.EPOCHS_KEY not in json_data) else json_data[self.EPOCHS_KEY]
+    self.batch_size = 1024 if (
+        self.BATCH_SIZE_KEY not in json_data) else json_data[self.BATCH_SIZE_KEY]
+    self.ratio_clip = 0.2 if (
+        self.RATIO_CLIP_KEY not in json_data) else json_data[self.RATIO_CLIP_KEY]
+    self.norm_adv_clip = 5 if (
+        self.NORM_ADV_CLIP_KEY not in json_data) else json_data[self.NORM_ADV_CLIP_KEY]
+    self.td_lambda = 0.95 if (
+        self.TD_LAMBDA_KEY not in json_data) else json_data[self.TD_LAMBDA_KEY]
+    self.tar_clip_frac = -1 if (
+        self.TAR_CLIP_FRAC not in json_data) else json_data[self.TAR_CLIP_FRAC]
+    self.actor_stepsize_decay = 0.5 if (
+        self.ACTOR_STEPSIZE_DECAY not in json_data) else json_data[self.ACTOR_STEPSIZE_DECAY]
+
+    num_procs = MPIUtil.get_num_procs()
+    local_batch_size = int(self.batch_size / num_procs)
+    min_replay_size = 2 * local_batch_size  # needed to prevent buffer overflow
+    assert (self.replay_buffer_size > min_replay_size)
+
+    self.replay_buffer_size = np.maximum(min_replay_size, self.replay_buffer_size)
+
+    return
+
+  def _build_nets(self, json_data):
+    assert self.ACTOR_NET_KEY in json_data
+    assert self.CRITIC_NET_KEY in json_data
+
+    actor_net_name = json_data[self.ACTOR_NET_KEY]
+    critic_net_name = json_data[self.CRITIC_NET_KEY]
+    actor_init_output_scale = 1 if (self.ACTOR_INIT_OUTPUT_SCALE_KEY not in json_data
+                                   ) else json_data[self.ACTOR_INIT_OUTPUT_SCALE_KEY]
+
+    s_size = self.get_state_size()
+    g_size = self.get_goal_size()
+    a_size = self.get_action_size()
+
+    # setup input tensors
+    self.s_tf = tf.placeholder(tf.float32, shape=[None, s_size], name="s")
+    self.a_tf = tf.placeholder(tf.float32, shape=[None, a_size], name="a")
+    self.tar_val_tf = tf.placeholder(tf.float32, shape=[None], name="tar_val")
+    self.adv_tf = tf.placeholder(tf.float32, shape=[None], name="adv")
+    self.g_tf = tf.placeholder(tf.float32,
+                               shape=([None, g_size] if self.has_goal() else None),
+                               name="g")
+    self.old_logp_tf = tf.placeholder(tf.float32, shape=[None], name="old_logp")
+    self.exp_mask_tf = tf.placeholder(tf.float32, shape=[None], name="exp_mask")
+
+    with tf.variable_scope('main'):
+      with tf.variable_scope('actor'):
+        self.a_mean_tf = self._build_net_actor(actor_net_name, actor_init_output_scale)
+      with tf.variable_scope('critic'):
+        self.critic_tf = self._build_net_critic(critic_net_name)
+
+    if (self.a_mean_tf != None):
+      Logger.print2('Built actor net: ' + actor_net_name)
+
+    if (self.critic_tf != None):
+      Logger.print2('Built critic net: ' + critic_net_name)
+
+    self.norm_a_std_tf = self.exp_params_curr.noise * tf.ones(a_size)
+    norm_a_noise_tf = self.norm_a_std_tf * tf.random_normal(shape=tf.shape(self.a_mean_tf))
+    norm_a_noise_tf *= tf.expand_dims(self.exp_mask_tf, axis=-1)
+    self.sample_a_tf = self.a_mean_tf + norm_a_noise_tf * self.a_norm.std_tf
+    self.sample_a_logp_tf = TFUtil.calc_logp_gaussian(x_tf=norm_a_noise_tf,
+                                                      mean_tf=None,
+                                                      std_tf=self.norm_a_std_tf)
+
+    return
+
+  def _build_losses(self, json_data):
+    actor_weight_decay = 0 if (
+        self.ACTOR_WEIGHT_DECAY_KEY not in json_data) else json_data[self.ACTOR_WEIGHT_DECAY_KEY]
+    critic_weight_decay = 0 if (
+        self.CRITIC_WEIGHT_DECAY_KEY not in json_data) else json_data[self.CRITIC_WEIGHT_DECAY_KEY]
+
+    norm_val_diff = self.val_norm.normalize_tf(self.tar_val_tf) - self.val_norm.normalize_tf(
+        self.critic_tf)
+    self.critic_loss_tf = 0.5 * tf.reduce_mean(tf.square(norm_val_diff))
+
+    if (critic_weight_decay != 0):
+      self.critic_loss_tf += critic_weight_decay * self._weight_decay_loss('main/critic')
+
+    norm_tar_a_tf = self.a_norm.normalize_tf(self.a_tf)
+    self._norm_a_mean_tf = self.a_norm.normalize_tf(self.a_mean_tf)
+
+    self.logp_tf = TFUtil.calc_logp_gaussian(norm_tar_a_tf, self._norm_a_mean_tf,
+                                             self.norm_a_std_tf)
+    ratio_tf = tf.exp(self.logp_tf - self.old_logp_tf)
+    actor_loss0 = self.adv_tf * ratio_tf
+    actor_loss1 = self.adv_tf * tf.clip_by_value(ratio_tf, 1.0 - self.ratio_clip,
+                                                 1 + self.ratio_clip)
+    self.actor_loss_tf = -tf.reduce_mean(tf.minimum(actor_loss0, actor_loss1))
+
+    norm_a_bound_min = self.a_norm.normalize(self.a_bound_min)
+    norm_a_bound_max = self.a_norm.normalize(self.a_bound_max)
+    a_bound_loss = TFUtil.calc_bound_loss(self._norm_a_mean_tf, norm_a_bound_min, norm_a_bound_max)
+    self.actor_loss_tf += a_bound_loss
+
+    if (actor_weight_decay != 0):
+      self.actor_loss_tf += actor_weight_decay * self._weight_decay_loss('main/actor')
+
+    # for debugging
+    self.clip_frac_tf = tf.reduce_mean(
+        tf.to_float(tf.greater(tf.abs(ratio_tf - 1.0), self.ratio_clip)))
+
+    return
+
+  def _build_solvers(self, json_data):
+    actor_stepsize = 0.001 if (
+        self.ACTOR_STEPSIZE_KEY not in json_data) else json_data[self.ACTOR_STEPSIZE_KEY]
+    actor_momentum = 0.9 if (
+        self.ACTOR_MOMENTUM_KEY not in json_data) else json_data[self.ACTOR_MOMENTUM_KEY]
+    critic_stepsize = 0.01 if (
+        self.CRITIC_STEPSIZE_KEY not in json_data) else json_data[self.CRITIC_STEPSIZE_KEY]
+    critic_momentum = 0.9 if (
+        self.CRITIC_MOMENTUM_KEY not in json_data) else json_data[self.CRITIC_MOMENTUM_KEY]
+
+    critic_vars = self._tf_vars('main/critic')
+    critic_opt = tf.train.MomentumOptimizer(learning_rate=critic_stepsize,
+                                            momentum=critic_momentum)
+    self.critic_grad_tf = tf.gradients(self.critic_loss_tf, critic_vars)
+    self.critic_solver = MPISolver(self.sess, critic_opt, critic_vars)
+
+    self._actor_stepsize_tf = tf.get_variable(dtype=tf.float32,
+                                              name='actor_stepsize',
+                                              initializer=actor_stepsize,
+                                              trainable=False)
+    self._actor_stepsize_ph = tf.get_variable(dtype=tf.float32, name='actor_stepsize_ph', shape=[])
+    self._actor_stepsize_update_op = self._actor_stepsize_tf.assign(self._actor_stepsize_ph)
+
+    actor_vars = self._tf_vars('main/actor')
+    actor_opt = tf.train.MomentumOptimizer(learning_rate=self._actor_stepsize_tf,
+                                           momentum=actor_momentum)
+    self.actor_grad_tf = tf.gradients(self.actor_loss_tf, actor_vars)
+    self.actor_solver = MPISolver(self.sess, actor_opt, actor_vars)
+
+    return
+
+  def _decide_action(self, s, g):
+    with self.sess.as_default(), self.graph.as_default():
+      self._exp_action = self._enable_stoch_policy() and MathUtil.flip_coin(
+          self.exp_params_curr.rate)
+      #print("_decide_action._exp_action=",self._exp_action)
+      a, logp = self._eval_actor(s, g, self._exp_action)
+    return a[0], logp[0]
+
+  def _eval_actor(self, s, g, enable_exp):
+    s = np.reshape(s, [-1, self.get_state_size()])
+    g = np.reshape(g, [-1, self.get_goal_size()]) if self.has_goal() else None
+
+    feed = {self.s_tf: s, self.g_tf: g, self.exp_mask_tf: np.array([1 if enable_exp else 0])}
+
+    a, logp = self.sess.run([self.sample_a_tf, self.sample_a_logp_tf], feed_dict=feed)
+    return a, logp
+
+  def _train_step(self):
+    adv_eps = 1e-5
+
+    start_idx = self.replay_buffer.buffer_tail
+    end_idx = self.replay_buffer.buffer_head
+    assert (start_idx == 0)
+    assert (self.replay_buffer.get_current_size() <= self.replay_buffer.buffer_size
+           )  # must avoid overflow
+    assert (start_idx < end_idx)
+
+    idx = np.array(list(range(start_idx, end_idx)))
+    end_mask = self.replay_buffer.is_path_end(idx)
+    end_mask = np.logical_not(end_mask)
+
+    vals = self._compute_batch_vals(start_idx, end_idx)
+    new_vals = self._compute_batch_new_vals(start_idx, end_idx, vals)
+
+    valid_idx = idx[end_mask]
+    exp_idx = self.replay_buffer.get_idx_filtered(self.EXP_ACTION_FLAG).copy()
+    num_valid_idx = valid_idx.shape[0]
+    num_exp_idx = exp_idx.shape[0]
+    exp_idx = np.column_stack([exp_idx, np.array(list(range(0, num_exp_idx)), dtype=np.int32)])
+
+    local_sample_count = valid_idx.size
+    global_sample_count = int(MPIUtil.reduce_sum(local_sample_count))
+    mini_batches = int(np.ceil(global_sample_count / self.mini_batch_size))
+
+    adv = new_vals[exp_idx[:, 0]] - vals[exp_idx[:, 0]]
+    new_vals = np.clip(new_vals, self.val_min, self.val_max)
+
+    adv_mean = np.mean(adv)
+    adv_std = np.std(adv)
+    adv = (adv - adv_mean) / (adv_std + adv_eps)
+    adv = np.clip(adv, -self.norm_adv_clip, self.norm_adv_clip)
+
+    critic_loss = 0
+    actor_loss = 0
+    actor_clip_frac = 0
+
+    for e in range(self.epochs):
+      np.random.shuffle(valid_idx)
+      np.random.shuffle(exp_idx)
+
+      for b in range(mini_batches):
+        batch_idx_beg = b * self._local_mini_batch_size
+        batch_idx_end = batch_idx_beg + self._local_mini_batch_size
+
+        critic_batch = np.array(range(batch_idx_beg, batch_idx_end), dtype=np.int32)
+        actor_batch = critic_batch.copy()
+        critic_batch = np.mod(critic_batch, num_valid_idx)
+        actor_batch = np.mod(actor_batch, num_exp_idx)
+        shuffle_actor = (actor_batch[-1] < actor_batch[0]) or (actor_batch[-1] == num_exp_idx - 1)
+
+        critic_batch = valid_idx[critic_batch]
+        actor_batch = exp_idx[actor_batch]
+        critic_batch_vals = new_vals[critic_batch]
+        actor_batch_adv = adv[actor_batch[:, 1]]
+
+        critic_s = self.replay_buffer.get('states', critic_batch)
+        critic_g = self.replay_buffer.get('goals', critic_batch) if self.has_goal() else None
+        curr_critic_loss = self._update_critic(critic_s, critic_g, critic_batch_vals)
+
+        actor_s = self.replay_buffer.get("states", actor_batch[:, 0])
+        actor_g = self.replay_buffer.get("goals", actor_batch[:, 0]) if self.has_goal() else None
+        actor_a = self.replay_buffer.get("actions", actor_batch[:, 0])
+        actor_logp = self.replay_buffer.get("logps", actor_batch[:, 0])
+        curr_actor_loss, curr_actor_clip_frac = self._update_actor(actor_s, actor_g, actor_a,
+                                                                   actor_logp, actor_batch_adv)
+
+        critic_loss += curr_critic_loss
+        actor_loss += np.abs(curr_actor_loss)
+        actor_clip_frac += curr_actor_clip_frac
+
+        if (shuffle_actor):
+          np.random.shuffle(exp_idx)
+
+    total_batches = mini_batches * self.epochs
+    critic_loss /= total_batches
+    actor_loss /= total_batches
+    actor_clip_frac /= total_batches
+
+    critic_loss = MPIUtil.reduce_avg(critic_loss)
+    actor_loss = MPIUtil.reduce_avg(actor_loss)
+    actor_clip_frac = MPIUtil.reduce_avg(actor_clip_frac)
+
+    critic_stepsize = self.critic_solver.get_stepsize()
+    actor_stepsize = self.update_actor_stepsize(actor_clip_frac)
+
+    self.logger.log_tabular('Critic_Loss', critic_loss)
+    self.logger.log_tabular('Critic_Stepsize', critic_stepsize)
+    self.logger.log_tabular('Actor_Loss', actor_loss)
+    self.logger.log_tabular('Actor_Stepsize', actor_stepsize)
+    self.logger.log_tabular('Clip_Frac', actor_clip_frac)
+    self.logger.log_tabular('Adv_Mean', adv_mean)
+    self.logger.log_tabular('Adv_Std', adv_std)
+
+    self.replay_buffer.clear()
+
+    return
+
+  def _get_iters_per_update(self):
+    return 1
+
+  def _valid_train_step(self):
+    samples = self.replay_buffer.get_current_size()
+    exp_samples = self.replay_buffer.count_filtered(self.EXP_ACTION_FLAG)
+    global_sample_count = int(MPIUtil.reduce_sum(samples))
+    global_exp_min = int(MPIUtil.reduce_min(exp_samples))
+    return (global_sample_count > self.batch_size) and (global_exp_min > 0)
+
+  def _compute_batch_vals(self, start_idx, end_idx):
+    states = self.replay_buffer.get_all("states")[start_idx:end_idx]
+    goals = self.replay_buffer.get_all("goals")[start_idx:end_idx] if self.has_goal() else None
+
+    idx = np.array(list(range(start_idx, end_idx)))
+    is_end = self.replay_buffer.is_path_end(idx)
+    is_fail = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Fail)
+    is_succ = self.replay_buffer.check_terminal_flag(idx, Env.Terminate.Succ)
+    is_fail = np.logical_and(is_end, is_fail)
+    is_succ = np.logical_and(is_end, is_succ)
+
+    vals = self._eval_critic(states, goals)
+    vals[is_fail] = self.val_fail
+    vals[is_succ] = self.val_succ
+
+    return vals
+
+  def _compute_batch_new_vals(self, start_idx, end_idx, val_buffer):
+    rewards = self.replay_buffer.get_all("rewards")[start_idx:end_idx]
+
+    if self.discount == 0:
+      new_vals = rewards.copy()
+    else:
+      new_vals = np.zeros_like(val_buffer)
+
+      curr_idx = start_idx
+      while curr_idx < end_idx:
+        idx0 = curr_idx - start_idx
+        idx1 = self.replay_buffer.get_path_end(curr_idx) - start_idx
+        r = rewards[idx0:idx1]
+        v = val_buffer[idx0:(idx1 + 1)]
+
+        new_vals[idx0:idx1] = RLUtil.compute_return(r, self.discount, self.td_lambda, v)
+        curr_idx = idx1 + start_idx + 1
+
+    return new_vals
+
+  def _update_critic(self, s, g, tar_vals):
+    feed = {self.s_tf: s, self.g_tf: g, self.tar_val_tf: tar_vals}
+
+    loss, grads = self.sess.run([self.critic_loss_tf, self.critic_grad_tf], feed)
+    self.critic_solver.update(grads)
+    return loss
+
+  def _update_actor(self, s, g, a, logp, adv):
+    feed = {self.s_tf: s, self.g_tf: g, self.a_tf: a, self.adv_tf: adv, self.old_logp_tf: logp}
+
+    loss, grads, clip_frac = self.sess.run(
+        [self.actor_loss_tf, self.actor_grad_tf, self.clip_frac_tf], feed)
+    self.actor_solver.update(grads)
+
+    return loss, clip_frac
+
+  def update_actor_stepsize(self, clip_frac):
+    clip_tol = 1.5
+    step_scale = 2
+    max_stepsize = 1e-2
+    min_stepsize = 1e-8
+    warmup_iters = 5
+
+    actor_stepsize = self.actor_solver.get_stepsize()
+    if (self.tar_clip_frac >= 0 and self.iter > warmup_iters):
+      min_clip = self.tar_clip_frac / clip_tol
+      max_clip = self.tar_clip_frac * clip_tol
+      under_tol = clip_frac < min_clip
+      over_tol = clip_frac > max_clip
+
+      if (over_tol or under_tol):
+        if (over_tol):
+          actor_stepsize *= self.actor_stepsize_decay
         else:
-            new_vals = np.zeros_like(val_buffer)
-
-            curr_idx = start_idx
-            while curr_idx < end_idx:
-                idx0 = curr_idx - start_idx
-                idx1 = self.replay_buffer.get_path_end(curr_idx) - start_idx
-                r = rewards[idx0:idx1]
-                v = val_buffer[idx0:(idx1 + 1)]
-
-                new_vals[idx0:idx1] = RLUtil.compute_return(r, self.discount, self.td_lambda, v)
-                curr_idx = idx1 + start_idx + 1
-        
-        return new_vals
-
-    def _update_critic(self, s, g, tar_vals):
-        feed = {
-            self.s_tf: s,
-            self.g_tf: g,
-            self.tar_val_tf: tar_vals
-        }
-
-        loss, grads = self.sess.run([self.critic_loss_tf, self.critic_grad_tf], feed)
-        self.critic_solver.update(grads)
-        return loss
-    
-    def _update_actor(self, s, g, a, logp, adv):
-        feed = {
-            self.s_tf: s,
-            self.g_tf: g,
-            self.a_tf: a,
-            self.adv_tf: adv,
-            self.old_logp_tf: logp
-        }
-
-        loss, grads, clip_frac = self.sess.run([self.actor_loss_tf, self.actor_grad_tf,
-                                                        self.clip_frac_tf], feed)
-        self.actor_solver.update(grads)
-
-        return loss, clip_frac
-
-    def update_actor_stepsize(self, clip_frac):
-        clip_tol = 1.5
-        step_scale = 2
-        max_stepsize = 1e-2
-        min_stepsize = 1e-8
-        warmup_iters = 5
-
-        actor_stepsize = self.actor_solver.get_stepsize()
-        if (self.tar_clip_frac >= 0 and self.iter > warmup_iters):
-            min_clip = self.tar_clip_frac / clip_tol
-            max_clip = self.tar_clip_frac * clip_tol
-            under_tol = clip_frac < min_clip
-            over_tol = clip_frac > max_clip
-
-            if (over_tol or under_tol):
-                if (over_tol):
-                    actor_stepsize *= self.actor_stepsize_decay
-                else:
-                    actor_stepsize /= self.actor_stepsize_decay
-
-                actor_stepsize = np.clip(actor_stepsize, min_stepsize, max_stepsize)
-                self.set_actor_stepsize(actor_stepsize)
-
-        return actor_stepsize
-
-    def set_actor_stepsize(self, stepsize):
-        feed = {
-            self._actor_stepsize_ph: stepsize,
-        }
-        self.sess.run(self._actor_stepsize_update_op, feed)
-        return
+          actor_stepsize /= self.actor_stepsize_decay
+
+        actor_stepsize = np.clip(actor_stepsize, min_stepsize, max_stepsize)
+        self.set_actor_stepsize(actor_stepsize)
+
+    return actor_stepsize
+
+  def set_actor_stepsize(self, stepsize):
+    feed = {
+        self._actor_stepsize_ph: stepsize,
+    }
+    self.sess.run(self._actor_stepsize_update_op, feed)
+    return
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/replay_buffer.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/replay_buffer.py
index 284515851..f002c7572 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/replay_buffer.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/replay_buffer.py
@@ -5,347 +5,353 @@ import inspect as inspect
 from pybullet_envs.deep_mimic.env.env import Env
 import pybullet_utils.math_util as MathUtil
 
+
 class ReplayBuffer(object):
-    TERMINATE_KEY = 'terminate'
-    PATH_START_KEY = 'path_start'
-    PATH_END_KEY = 'path_end'
-
-    def __init__(self, buffer_size):
-        assert buffer_size > 0
-
-        self.buffer_size = buffer_size
-        self.total_count = 0
-        self.buffer_head = 0
-        self.buffer_tail = MathUtil.INVALID_IDX
-        self.num_paths = 0
-        self._sample_buffers = dict()
-        self.buffers = None
-
-        self.clear()
-        return
-
-    def sample(self, n):
-        curr_size = self.get_current_size()
-        assert curr_size > 0
-
-        idx = np.empty(n, dtype=int)
-        # makes sure that the end states are not sampled
-        for i in range(n):
-            while True:
-                curr_idx = np.random.randint(0, curr_size, size=1)[0]
-                curr_idx += self.buffer_tail
-                curr_idx = np.mod(curr_idx, self.buffer_size)
-
-                if not self.is_path_end(curr_idx):
-                    break
-            idx[i] = curr_idx
-
-        return idx
-
-    def sample_filtered(self, n, key):
-        assert key in self._sample_buffers
-        curr_buffer = self._sample_buffers[key]
-        idx = curr_buffer.sample(n)
-        return idx
-
-    def count_filtered(self, key):
-        curr_buffer = self._sample_buffers[key]
-        return curr_buffer.count
-
-    def get(self, key, idx):
-        return self.buffers[key][idx]
-
-    def get_all(self, key):
-        return self.buffers[key]
-
-    def get_idx_filtered(self, key):
-        assert key in self._sample_buffers
-        curr_buffer = self._sample_buffers[key]
-        idx = curr_buffer.slot_to_idx[:curr_buffer.count]
-        return idx
-    
-    def get_path_start(self, idx):
-        return self.buffers[self.PATH_START_KEY][idx]
-
-    def get_path_end(self, idx):
-        return self.buffers[self.PATH_END_KEY][idx]
-
-    def get_pathlen(self, idx):
-        is_array = isinstance(idx, np.ndarray) or isinstance(idx, list)
-        if not is_array:
-            idx = [idx]
-
-        n = len(idx)
-        start_idx = self.get_path_start(idx)
-        end_idx = self.get_path_end(idx)
-        pathlen = np.empty(n, dtype=int)
-
-        for i in range(n):
-            curr_start = start_idx[i]
-            curr_end = end_idx[i]
-            if curr_start < curr_end:
-                curr_len = curr_end - curr_start
+  TERMINATE_KEY = 'terminate'
+  PATH_START_KEY = 'path_start'
+  PATH_END_KEY = 'path_end'
+
+  def __init__(self, buffer_size):
+    assert buffer_size > 0
+
+    self.buffer_size = buffer_size
+    self.total_count = 0
+    self.buffer_head = 0
+    self.buffer_tail = MathUtil.INVALID_IDX
+    self.num_paths = 0
+    self._sample_buffers = dict()
+    self.buffers = None
+
+    self.clear()
+    return
+
+  def sample(self, n):
+    curr_size = self.get_current_size()
+    assert curr_size > 0
+
+    idx = np.empty(n, dtype=int)
+    # makes sure that the end states are not sampled
+    for i in range(n):
+      while True:
+        curr_idx = np.random.randint(0, curr_size, size=1)[0]
+        curr_idx += self.buffer_tail
+        curr_idx = np.mod(curr_idx, self.buffer_size)
+
+        if not self.is_path_end(curr_idx):
+          break
+      idx[i] = curr_idx
+
+    return idx
+
+  def sample_filtered(self, n, key):
+    assert key in self._sample_buffers
+    curr_buffer = self._sample_buffers[key]
+    idx = curr_buffer.sample(n)
+    return idx
+
+  def count_filtered(self, key):
+    curr_buffer = self._sample_buffers[key]
+    return curr_buffer.count
+
+  def get(self, key, idx):
+    return self.buffers[key][idx]
+
+  def get_all(self, key):
+    return self.buffers[key]
+
+  def get_idx_filtered(self, key):
+    assert key in self._sample_buffers
+    curr_buffer = self._sample_buffers[key]
+    idx = curr_buffer.slot_to_idx[:curr_buffer.count]
+    return idx
+
+  def get_path_start(self, idx):
+    return self.buffers[self.PATH_START_KEY][idx]
+
+  def get_path_end(self, idx):
+    return self.buffers[self.PATH_END_KEY][idx]
+
+  def get_pathlen(self, idx):
+    is_array = isinstance(idx, np.ndarray) or isinstance(idx, list)
+    if not is_array:
+      idx = [idx]
+
+    n = len(idx)
+    start_idx = self.get_path_start(idx)
+    end_idx = self.get_path_end(idx)
+    pathlen = np.empty(n, dtype=int)
+
+    for i in range(n):
+      curr_start = start_idx[i]
+      curr_end = end_idx[i]
+      if curr_start < curr_end:
+        curr_len = curr_end - curr_start
+      else:
+        curr_len = self.buffer_size - curr_start + curr_end
+      pathlen[i] = curr_len
+
+    if not is_array:
+      pathlen = pathlen[0]
+
+    return pathlen
+
+  def is_valid_path(self, idx):
+    start_idx = self.get_path_start(idx)
+    valid = start_idx != MathUtil.INVALID_IDX
+    return valid
+
+  def store(self, path):
+    start_idx = MathUtil.INVALID_IDX
+    n = path.pathlength()
+
+    if (n > 0):
+      assert path.is_valid()
+
+      if path.check_vals():
+        if self.buffers is None:
+          self._init_buffers(path)
+
+        idx = self._request_idx(n + 1)
+        self._store_path(path, idx)
+        self._add_sample_buffers(idx)
+
+        self.num_paths += 1
+        self.total_count += n + 1
+        start_idx = idx[0]
+      else:
+        Logger.print2('Invalid path data value detected')
+
+    return start_idx
+
+  def clear(self):
+    self.buffer_head = 0
+    self.buffer_tail = MathUtil.INVALID_IDX
+    self.num_paths = 0
+
+    for key in self._sample_buffers:
+      self._sample_buffers[key].clear()
+    return
+
+  def get_next_idx(self, idx):
+    next_idx = np.mod(idx + 1, self.buffer_size)
+    return next_idx
+
+  def is_terminal_state(self, idx):
+    terminate_flags = self.buffers[self.TERMINATE_KEY][idx]
+    terminate = terminate_flags != Env.Terminate.Null.value
+    is_end = self.is_path_end(idx)
+    terminal_state = np.logical_and(terminate, is_end)
+    return terminal_state
+
+  def check_terminal_flag(self, idx, flag):
+    terminate_flags = self.buffers[self.TERMINATE_KEY][idx]
+    terminate = terminate_flags == flag.value
+    return terminate
+
+  def is_path_end(self, idx):
+    is_end = self.buffers[self.PATH_END_KEY][idx] == idx
+    return is_end
+
+  def add_filter_key(self, key):
+    assert self.get_current_size() == 0
+    if key not in self._sample_buffers:
+      self._sample_buffers[key] = SampleBuffer(self.buffer_size)
+    return
+
+  def get_current_size(self):
+    if self.buffer_tail == MathUtil.INVALID_IDX:
+      return 0
+    elif self.buffer_tail < self.buffer_head:
+      return self.buffer_head - self.buffer_tail
+    else:
+      return self.buffer_size - self.buffer_tail + self.buffer_head
+
+  def _check_flags(self, key, flags):
+    return (flags & key) == key
+
+  def _add_sample_buffers(self, idx):
+    flags = self.buffers['flags']
+    for key in self._sample_buffers:
+      curr_buffer = self._sample_buffers[key]
+      filter_idx = [
+          i for i in idx if (self._check_flags(key, flags[i]) and not self.is_path_end(i))
+      ]
+      curr_buffer.add(filter_idx)
+    return
+
+  def _free_sample_buffers(self, idx):
+    for key in self._sample_buffers:
+      curr_buffer = self._sample_buffers[key]
+      curr_buffer.free(idx)
+    return
+
+  def _init_buffers(self, path):
+    self.buffers = dict()
+    self.buffers[self.PATH_START_KEY] = MathUtil.INVALID_IDX * np.ones(self.buffer_size, dtype=int)
+    self.buffers[self.PATH_END_KEY] = MathUtil.INVALID_IDX * np.ones(self.buffer_size, dtype=int)
+
+    for key in dir(path):
+      val = getattr(path, key)
+      if not key.startswith('__') and not inspect.ismethod(val):
+        if key == self.TERMINATE_KEY:
+          self.buffers[self.TERMINATE_KEY] = np.zeros(shape=[self.buffer_size], dtype=int)
+        else:
+          val_type = type(val[0])
+          is_array = val_type == np.ndarray
+          if is_array:
+            shape = [self.buffer_size, val[0].shape[0]]
+            dtype = val[0].dtype
+          else:
+            shape = [self.buffer_size]
+            dtype = val_type
+
+          self.buffers[key] = np.zeros(shape, dtype=dtype)
+    return
+
+  def _request_idx(self, n):
+    assert n + 1 < self.buffer_size  # bad things can happen if path is too long
+
+    remainder = n
+    idx = []
+
+    start_idx = self.buffer_head
+    while remainder > 0:
+      end_idx = np.minimum(start_idx + remainder, self.buffer_size)
+      remainder -= (end_idx - start_idx)
+
+      free_idx = list(range(start_idx, end_idx))
+      self._free_idx(free_idx)
+      idx += free_idx
+      start_idx = 0
+
+    self.buffer_head = (self.buffer_head + n) % self.buffer_size
+    return idx
+
+  def _free_idx(self, idx):
+    assert (idx[0] <= idx[-1])
+    n = len(idx)
+    if self.buffer_tail != MathUtil.INVALID_IDX:
+      update_tail = idx[0] <= idx[-1] and idx[0] <= self.buffer_tail and idx[-1] >= self.buffer_tail
+      update_tail |= idx[0] > idx[-1] and (idx[0] <= self.buffer_tail or
+                                           idx[-1] >= self.buffer_tail)
+
+      if update_tail:
+        i = 0
+        while i < n:
+          curr_idx = idx[i]
+          if self.is_valid_path(curr_idx):
+            start_idx = self.get_path_start(curr_idx)
+            end_idx = self.get_path_end(curr_idx)
+            pathlen = self.get_pathlen(curr_idx)
+
+            if start_idx < end_idx:
+              self.buffers[self.PATH_START_KEY][start_idx:end_idx + 1] = MathUtil.INVALID_IDX
+              self._free_sample_buffers(list(range(start_idx, end_idx + 1)))
             else:
-                curr_len = self.buffer_size - curr_start + curr_end
-            pathlen[i] = curr_len
-
-        if not is_array:
-            pathlen = pathlen[0]
-
-        return pathlen
-
-    def is_valid_path(self, idx):
-        start_idx = self.get_path_start(idx)
-        valid = start_idx != MathUtil.INVALID_IDX
-        return valid
-
-    def store(self, path):
-        start_idx = MathUtil.INVALID_IDX
-        n = path.pathlength()
-        
-        if (n > 0):
-            assert path.is_valid()
-
-            if path.check_vals():
-                if self.buffers is None:
-                    self._init_buffers(path)
+              self.buffers[self.PATH_START_KEY][start_idx:self.buffer_size] = MathUtil.INVALID_IDX
+              self.buffers[self.PATH_START_KEY][0:end_idx + 1] = MathUtil.INVALID_IDX
+              self._free_sample_buffers(list(range(start_idx, self.buffer_size)))
+              self._free_sample_buffers(list(range(0, end_idx + 1)))
+
+            self.num_paths -= 1
+            i += pathlen + 1
+            self.buffer_tail = (end_idx + 1) % self.buffer_size
+          else:
+            i += 1
+    else:
+      self.buffer_tail = idx[0]
+    return
+
+  def _store_path(self, path, idx):
+    n = path.pathlength()
+    for key, data in self.buffers.items():
+      if key != self.PATH_START_KEY and key != self.PATH_END_KEY and key != self.TERMINATE_KEY:
+        val = getattr(path, key)
+        val_len = len(val)
+        assert val_len == n or val_len == n + 1
+        data[idx[:val_len]] = val
+
+    self.buffers[self.TERMINATE_KEY][idx] = path.terminate.value
+    self.buffers[self.PATH_START_KEY][idx] = idx[0]
+    self.buffers[self.PATH_END_KEY][idx] = idx[-1]
+    return
 
-                idx = self._request_idx(n + 1)
-                self._store_path(path, idx)
-                self._add_sample_buffers(idx)
-
-                self.num_paths += 1
-                self.total_count += n + 1
-                start_idx = idx[0]
-            else:
-                Logger.print2('Invalid path data value detected')
-        
-        return start_idx
-
-    def clear(self):
-        self.buffer_head = 0
-        self.buffer_tail = MathUtil.INVALID_IDX
-        self.num_paths = 0
-
-        for key in self._sample_buffers:
-            self._sample_buffers[key].clear()
-        return
-
-    def get_next_idx(self, idx):
-        next_idx = np.mod(idx + 1, self.buffer_size)
-        return next_idx
-
-    def is_terminal_state(self, idx):
-        terminate_flags = self.buffers[self.TERMINATE_KEY][idx]
-        terminate = terminate_flags != Env.Terminate.Null.value
-        is_end = self.is_path_end(idx)
-        terminal_state = np.logical_and(terminate, is_end)
-        return terminal_state
-
-    def check_terminal_flag(self, idx, flag):
-        terminate_flags = self.buffers[self.TERMINATE_KEY][idx]
-        terminate = terminate_flags == flag.value
-        return terminate
-
-    def is_path_end(self, idx):
-        is_end = self.buffers[self.PATH_END_KEY][idx] == idx
-        return is_end
-
-    def add_filter_key(self, key):
-        assert self.get_current_size() == 0
-        if key not in self._sample_buffers:
-            self._sample_buffers[key] = SampleBuffer(self.buffer_size)
-        return
-
-    def get_current_size(self):
-        if self.buffer_tail == MathUtil.INVALID_IDX:
-            return 0
-        elif self.buffer_tail < self.buffer_head:
-            return self.buffer_head - self.buffer_tail
-        else:
-            return self.buffer_size - self.buffer_tail + self.buffer_head
-
-    def _check_flags(self, key, flags):
-        return (flags & key) == key
-
-    def _add_sample_buffers(self, idx):
-        flags = self.buffers['flags']
-        for key in self._sample_buffers:
-            curr_buffer = self._sample_buffers[key]
-            filter_idx = [i for i in idx if (self._check_flags(key, flags[i]) and not self.is_path_end(i))]
-            curr_buffer.add(filter_idx)
-        return
-
-    def _free_sample_buffers(self, idx):
-        for key in self._sample_buffers:
-            curr_buffer = self._sample_buffers[key]
-            curr_buffer.free(idx)    
-        return
-
-    def _init_buffers(self, path):
-        self.buffers = dict()
-        self.buffers[self.PATH_START_KEY] = MathUtil.INVALID_IDX * np.ones(self.buffer_size, dtype=int);
-        self.buffers[self.PATH_END_KEY] = MathUtil.INVALID_IDX * np.ones(self.buffer_size, dtype=int);
-
-        for key in dir(path):
-            val = getattr(path, key)
-            if not key.startswith('__') and not inspect.ismethod(val):
-                if key == self.TERMINATE_KEY:
-                    self.buffers[self.TERMINATE_KEY] = np.zeros(shape=[self.buffer_size], dtype=int)
-                else:
-                    val_type = type(val[0])
-                    is_array = val_type == np.ndarray
-                    if is_array:
-                        shape = [self.buffer_size, val[0].shape[0]]
-                        dtype = val[0].dtype
-                    else:
-                        shape = [self.buffer_size]
-                        dtype = val_type
-                    
-                    self.buffers[key] = np.zeros(shape, dtype=dtype)
-        return
-
-    def _request_idx(self, n):
-        assert n + 1 < self.buffer_size # bad things can happen if path is too long
-
-        remainder = n
-        idx = []
-
-        start_idx = self.buffer_head
-        while remainder > 0:
-            end_idx = np.minimum(start_idx + remainder, self.buffer_size)
-            remainder -= (end_idx - start_idx)
-
-            free_idx = list(range(start_idx, end_idx))
-            self._free_idx(free_idx)
-            idx += free_idx
-            start_idx = 0
-
-        self.buffer_head = (self.buffer_head + n) % self.buffer_size
-        return idx
-
-    def _free_idx(self, idx):
-        assert(idx[0] <= idx[-1])
-        n = len(idx)
-        if self.buffer_tail != MathUtil.INVALID_IDX:
-            update_tail = idx[0] <= idx[-1] and idx[0] <= self.buffer_tail and idx[-1] >= self.buffer_tail
-            update_tail |= idx[0] > idx[-1] and (idx[0] <= self.buffer_tail or idx[-1] >= self.buffer_tail)
-            
-            if update_tail:
-                i = 0
-                while i < n:
-                    curr_idx = idx[i]
-                    if self.is_valid_path(curr_idx):
-                        start_idx = self.get_path_start(curr_idx)
-                        end_idx = self.get_path_end(curr_idx)
-                        pathlen = self.get_pathlen(curr_idx)
-
-                        if start_idx < end_idx:
-                            self.buffers[self.PATH_START_KEY][start_idx:end_idx + 1] = MathUtil.INVALID_IDX
-                            self._free_sample_buffers(list(range(start_idx, end_idx + 1)))
-                        else:
-                            self.buffers[self.PATH_START_KEY][start_idx:self.buffer_size] = MathUtil.INVALID_IDX
-                            self.buffers[self.PATH_START_KEY][0:end_idx + 1] = MathUtil.INVALID_IDX
-                            self._free_sample_buffers(list(range(start_idx, self.buffer_size)))
-                            self._free_sample_buffers(list(range(0, end_idx + 1)))
-                        
-                        self.num_paths -= 1
-                        i += pathlen + 1
-                        self.buffer_tail = (end_idx + 1) % self.buffer_size;
-                    else:
-                        i += 1
-        else:
-            self.buffer_tail = idx[0]
-        return
-
-    def _store_path(self, path, idx):
-        n = path.pathlength()
-        for key, data in self.buffers.items():
-            if key != self.PATH_START_KEY and key != self.PATH_END_KEY and key != self.TERMINATE_KEY:
-                val = getattr(path, key)
-                val_len = len(val)
-                assert val_len == n or val_len == n + 1
-                data[idx[:val_len]] = val
-
-        self.buffers[self.TERMINATE_KEY][idx] = path.terminate.value
-        self.buffers[self.PATH_START_KEY][idx] = idx[0]
-        self.buffers[self.PATH_END_KEY][idx] = idx[-1]
-        return
 
 class SampleBuffer(object):
-    def __init__(self, size):
-        self.idx_to_slot = np.empty(shape=[size], dtype=int)
-        self.slot_to_idx = np.empty(shape=[size], dtype=int)
-        self.count = 0
-        self.clear()
-        return
-    
-    def clear(self):
-        self.idx_to_slot.fill(MathUtil.INVALID_IDX)
-        self.slot_to_idx.fill(MathUtil.INVALID_IDX)
-        self.count = 0
-        return
-
-    def is_valid(self, idx):
-        return self.idx_to_slot[idx] != MathUtil.INVALID_IDX
-
-    def get_size(self):
-        return self.idx_to_slot.shape[0]
-
-    def add(self, idx):
-        for i in idx:
-            if not self.is_valid(i):
-                new_slot = self.count
-                assert new_slot >= 0
-
-                self.idx_to_slot[i] = new_slot
-                self.slot_to_idx[new_slot] = i
-                self.count += 1
-        return
-
-    def free(self, idx):
-        for i in idx:
-            if self.is_valid(i):
-                slot = self.idx_to_slot[i]
-                last_slot = self.count - 1
-                last_idx = self.slot_to_idx[last_slot]
-
-                self.idx_to_slot[last_idx] = slot
-                self.slot_to_idx[slot] = last_idx
-                self.idx_to_slot[i] = MathUtil.INVALID_IDX
-                self.slot_to_idx[last_slot] = MathUtil.INVALID_IDX
-                self.count -= 1
-        return
-
-    def sample(self, n):
-        if self.count > 0:
-            slots = np.random.randint(0, self.count, size=n)
-            idx = self.slot_to_idx[slots]
-        else:
-            idx = np.empty(shape=[0], dtype=int)
-        return idx
 
-    def check_consistency(self):
-        valid = True
-        if self.count < 0:
+  def __init__(self, size):
+    self.idx_to_slot = np.empty(shape=[size], dtype=int)
+    self.slot_to_idx = np.empty(shape=[size], dtype=int)
+    self.count = 0
+    self.clear()
+    return
+
+  def clear(self):
+    self.idx_to_slot.fill(MathUtil.INVALID_IDX)
+    self.slot_to_idx.fill(MathUtil.INVALID_IDX)
+    self.count = 0
+    return
+
+  def is_valid(self, idx):
+    return self.idx_to_slot[idx] != MathUtil.INVALID_IDX
+
+  def get_size(self):
+    return self.idx_to_slot.shape[0]
+
+  def add(self, idx):
+    for i in idx:
+      if not self.is_valid(i):
+        new_slot = self.count
+        assert new_slot >= 0
+
+        self.idx_to_slot[i] = new_slot
+        self.slot_to_idx[new_slot] = i
+        self.count += 1
+    return
+
+  def free(self, idx):
+    for i in idx:
+      if self.is_valid(i):
+        slot = self.idx_to_slot[i]
+        last_slot = self.count - 1
+        last_idx = self.slot_to_idx[last_slot]
+
+        self.idx_to_slot[last_idx] = slot
+        self.slot_to_idx[slot] = last_idx
+        self.idx_to_slot[i] = MathUtil.INVALID_IDX
+        self.slot_to_idx[last_slot] = MathUtil.INVALID_IDX
+        self.count -= 1
+    return
+
+  def sample(self, n):
+    if self.count > 0:
+      slots = np.random.randint(0, self.count, size=n)
+      idx = self.slot_to_idx[slots]
+    else:
+      idx = np.empty(shape=[0], dtype=int)
+    return idx
+
+  def check_consistency(self):
+    valid = True
+    if self.count < 0:
+      valid = False
+
+    if valid:
+      for i in range(self.get_size()):
+        if self.is_valid(i):
+          s = self.idx_to_slot[i]
+          if self.slot_to_idx[s] != i:
+            valid = False
+            break
+
+        s2i = self.slot_to_idx[i]
+        if s2i != MathUtil.INVALID_IDX:
+          i2s = self.idx_to_slot[s2i]
+          if i2s != i:
             valid = False
+            break
 
-        if valid:
-            for i in range(self.get_size()):
-                if self.is_valid(i):
-                    s = self.idx_to_slot[i]
-                    if self.slot_to_idx[s] != i:
-                        valid = False
-                        break
-
-                s2i = self.slot_to_idx[i] 
-                if s2i != MathUtil.INVALID_IDX:
-                    i2s = self.idx_to_slot[s2i]
-                    if i2s != i:
-                        valid = False
-                        break
-
-        count0 = np.sum(self.idx_to_slot == MathUtil.INVALID_IDX)
-        count1 = np.sum(self.slot_to_idx == MathUtil.INVALID_IDX)
-        valid &= count0 == count1
-        return valid
+    count0 = np.sum(self.idx_to_slot == MathUtil.INVALID_IDX)
+    count1 = np.sum(self.slot_to_idx == MathUtil.INVALID_IDX)
+    valid &= count0 == count1
+    return valid
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_agent.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_agent.py
index ce6b2a9f4..fe9a05d85 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_agent.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_agent.py
@@ -6,9 +6,9 @@ import sys
 from abc import abstractmethod
 import abc
 if sys.version_info >= (3, 4):
-    ABC = abc.ABC
+  ABC = abc.ABC
 else:
-    ABC = abc.ABCMeta('ABC', (), {})
+  ABC = abc.ABCMeta('ABC', (), {})
 
 from enum import Enum
 
@@ -20,583 +20,586 @@ from pybullet_utils.logger import Logger
 import pybullet_utils.mpi_util as MPIUtil
 import pybullet_utils.math_util as MathUtil
 
+
 class RLAgent(ABC):
-    class Mode(Enum):
-        TRAIN = 0
-        TEST = 1
-        TRAIN_END = 2
-
-    NAME = "None"
-    
-    UPDATE_PERIOD_KEY = "UpdatePeriod"
-    ITERS_PER_UPDATE = "ItersPerUpdate"
-    DISCOUNT_KEY = "Discount"
-    MINI_BATCH_SIZE_KEY = "MiniBatchSize"
-    REPLAY_BUFFER_SIZE_KEY = "ReplayBufferSize"
-    INIT_SAMPLES_KEY = "InitSamples"
-    NORMALIZER_SAMPLES_KEY = "NormalizerSamples"
-
-    OUTPUT_ITERS_KEY = "OutputIters"
-    INT_OUTPUT_ITERS_KEY = "IntOutputIters"
-    TEST_EPISODES_KEY = "TestEpisodes"
-
-    EXP_ANNEAL_SAMPLES_KEY = "ExpAnnealSamples"
-    EXP_PARAM_BEG_KEY = "ExpParamsBeg"
-    EXP_PARAM_END_KEY = "ExpParamsEnd"
-        
-    def __init__(self, world, id, json_data):
-        self.world = world
-        self.id = id
-        self.logger = Logger()
-        self._mode = self.Mode.TRAIN
-        
-        assert self._check_action_space(), \
-            Logger.print2("Invalid action space, got {:s}".format(str(self.get_action_space())))
-        
-        self._enable_training = True
-        self.path = Path()
-        self.iter = int(0)
-        self.start_time = time.time()
-        self._update_counter = 0
-
-        self.update_period = 1.0 # simulated time (seconds) before each training update
-        self.iters_per_update = int(1)
-        self.discount = 0.95
-        self.mini_batch_size = int(32)
-        self.replay_buffer_size = int(50000)
-        self.init_samples = int(1000)
-        self.normalizer_samples = np.inf
-        self._local_mini_batch_size = self.mini_batch_size # batch size for each work for multiprocessing
-        self._need_normalizer_update = True
-        self._total_sample_count = 0
-
-        self._output_dir = ""
-        self._int_output_dir = ""
-        self.output_iters = 100
-        self.int_output_iters = 100
-        
-        self.train_return = 0.0
-        self.test_episodes = int(0)
-        self.test_episode_count = int(0)
-        self.test_return = 0.0
-        self.avg_test_return = 0.0
-        
-        self.exp_anneal_samples = 320000
-        self.exp_params_beg = ExpParams()
-        self.exp_params_end = ExpParams()
-        self.exp_params_curr = ExpParams()
-
-        self._load_params(json_data)
-        self._build_replay_buffer(self.replay_buffer_size)
-        self._build_normalizers()
-        self._build_bounds()
-        self.reset()
-
-        return
-
-    def __str__(self):
-        action_space_str = str(self.get_action_space())
-        info_str = ""
-        info_str += '"ID": {:d},\n "Type": "{:s}",\n "ActionSpace": "{:s}",\n "StateDim": {:d},\n "GoalDim": {:d},\n "ActionDim": {:d}'.format(
-            self.id, self.NAME, action_space_str[action_space_str.rfind('.') + 1:], self.get_state_size(), self.get_goal_size(), self.get_action_size())
-        return "{\n" + info_str + "\n}"
-
-    def get_output_dir(self):
-        return self._output_dir
-    
-    def set_output_dir(self, out_dir):
-        self._output_dir = out_dir
-        if (self._output_dir != ""):
-            self.logger.configure_output_file(out_dir + "/agent" + str(self.id) + "_log.txt")
-        return
-
-    output_dir = property(get_output_dir, set_output_dir)
-
-    def get_int_output_dir(self):
-        return self._int_output_dir
-    
-    def set_int_output_dir(self, out_dir):
-        self._int_output_dir = out_dir
-        return
-
-    int_output_dir = property(get_int_output_dir, set_int_output_dir)
-
-    def reset(self):
-        self.path.clear()
-        return
-
-    def update(self, timestep):
-        if self.need_new_action():
-           #print("update_new_action!!!")
-           self._update_new_action()
-        
-
-        if (self._mode == self.Mode.TRAIN and self.enable_training):
-            self._update_counter += timestep
-
-            while self._update_counter >= self.update_period:
-                self._train()
-                self._update_exp_params()
-                self.world.env.set_sample_count(self._total_sample_count)
-                self._update_counter -= self.update_period
-
-        return
-
-    def end_episode(self):
-        if (self.path.pathlength() > 0):
-            self._end_path()
-
-            if (self._mode == self.Mode.TRAIN or self._mode == self.Mode.TRAIN_END):
-                if (self.enable_training and self.path.pathlength() > 0):
-                    self._store_path(self.path)
-            elif (self._mode == self.Mode.TEST):
-                self._update_test_return(self.path)
-            else:
-                assert False, Logger.print2("Unsupported RL agent mode" + str(self._mode))
-
-            self._update_mode()
-        return
-
-    def has_goal(self):
-        return self.get_goal_size() > 0
-
-    def predict_val(self):
-        return 0
-
-    def get_enable_training(self):
-        return self._enable_training
-
-    def set_enable_training(self, enable):
-        print("set_enable_training=", enable)
-        self._enable_training = enable
-        if (self._enable_training):
-            self.reset()
-        return
-
-    enable_training = property(get_enable_training, set_enable_training)
-
-    def enable_testing(self):
-        return self.test_episodes > 0
-    
-    def get_name(self):
-        return self.NAME
-
-    @abstractmethod
-    def save_model(self, out_path):
-        pass
-
-    @abstractmethod
-    def load_model(self, in_path):
-        pass
-
-    @abstractmethod
-    def _decide_action(self, s, g):
-        pass
-
-    @abstractmethod
-    def _get_output_path(self):
-        pass
-
-    @abstractmethod
-    def _get_int_output_path(self):
-        pass
-
-    @abstractmethod
-    def _train_step(self):
-        pass
-    
-    @abstractmethod
-    def _check_action_space(self):
-        pass
-
-    def get_action_space(self):
-        return self.world.env.get_action_space(self.id)
-
-    def get_state_size(self):
-        return self.world.env.get_state_size(self.id)
-
-    def get_goal_size(self):
-        return self.world.env.get_goal_size(self.id)
-
-    def get_action_size(self):
-        return self.world.env.get_action_size(self.id)
-
-    def get_num_actions(self):
-        return self.world.env.get_num_actions(self.id)
-    
-    def need_new_action(self):
-        return self.world.env.need_new_action(self.id)
-
-    def _build_normalizers(self):
-        self.s_norm = Normalizer(self.get_state_size(), self.world.env.build_state_norm_groups(self.id))
-        self.s_norm.set_mean_std(-self.world.env.build_state_offset(self.id), 
-                                 1 / self.world.env.build_state_scale(self.id))
-
-        self.g_norm = Normalizer(self.get_goal_size(), self.world.env.build_goal_norm_groups(self.id))
-        self.g_norm.set_mean_std(-self.world.env.build_goal_offset(self.id), 
-                                 1 / self.world.env.build_goal_scale(self.id))
-
-        self.a_norm = Normalizer(self.world.env.get_action_size())
-        self.a_norm.set_mean_std(-self.world.env.build_action_offset(self.id), 
-                                 1 / self.world.env.build_action_scale(self.id))
-        return
-
-    def _build_bounds(self):
-        self.a_bound_min = self.world.env.build_action_bound_min(self.id)
-        self.a_bound_max = self.world.env.build_action_bound_max(self.id)
-        return
-
-    def _load_params(self, json_data):
-        if (self.UPDATE_PERIOD_KEY in json_data):
-            self.update_period = int(json_data[self.UPDATE_PERIOD_KEY])
-        
-        if (self.ITERS_PER_UPDATE in json_data):
-            self.iters_per_update = int(json_data[self.ITERS_PER_UPDATE])
-                    
-        if (self.DISCOUNT_KEY in json_data):
-            self.discount = json_data[self.DISCOUNT_KEY]
-        
-        if (self.MINI_BATCH_SIZE_KEY in json_data):
-            self.mini_batch_size = int(json_data[self.MINI_BATCH_SIZE_KEY])
-            
-        if (self.REPLAY_BUFFER_SIZE_KEY in json_data):
-            self.replay_buffer_size = int(json_data[self.REPLAY_BUFFER_SIZE_KEY])
-            
-        if (self.INIT_SAMPLES_KEY in json_data):
-            self.init_samples = int(json_data[self.INIT_SAMPLES_KEY])
-
-        if (self.NORMALIZER_SAMPLES_KEY in json_data):
-            self.normalizer_samples = int(json_data[self.NORMALIZER_SAMPLES_KEY])
-
-        if (self.OUTPUT_ITERS_KEY in json_data):
-            self.output_iters = json_data[self.OUTPUT_ITERS_KEY]
-
-        if (self.INT_OUTPUT_ITERS_KEY in json_data):
-            self.int_output_iters = json_data[self.INT_OUTPUT_ITERS_KEY]
-            
-        if (self.TEST_EPISODES_KEY in json_data):
-            self.test_episodes = int(json_data[self.TEST_EPISODES_KEY])
-
-        if (self.EXP_ANNEAL_SAMPLES_KEY in json_data):
-            self.exp_anneal_samples = json_data[self.EXP_ANNEAL_SAMPLES_KEY]
-
-        if (self.EXP_PARAM_BEG_KEY in json_data):
-            self.exp_params_beg.load(json_data[self.EXP_PARAM_BEG_KEY])
-
-        if (self.EXP_PARAM_END_KEY in json_data):
-            self.exp_params_end.load(json_data[self.EXP_PARAM_END_KEY])
-        
-        num_procs = MPIUtil.get_num_procs()
-        self._local_mini_batch_size = int(np.ceil(self.mini_batch_size / num_procs))
-        self._local_mini_batch_size = np.maximum(self._local_mini_batch_size, 1)
-        self.mini_batch_size = self._local_mini_batch_size * num_procs
-        
-        assert(self.exp_params_beg.noise == self.exp_params_end.noise) # noise std should not change
-        self.exp_params_curr = copy.deepcopy(self.exp_params_beg)
-        self.exp_params_end.noise = self.exp_params_beg.noise
-
-        self._need_normalizer_update = self.normalizer_samples > 0
-
-        return
-
-    def _record_state(self):
-        s = self.world.env.record_state(self.id)
-        return s
-
-    def _record_goal(self):
-        g = self.world.env.record_goal(self.id)
-        return g
-
-    def _record_reward(self):
-        r = self.world.env.calc_reward(self.id)
-        return r
-
-    def _apply_action(self, a):
-        self.world.env.set_action(self.id, a)
-        return
-
-    def _record_flags(self):
-        return int(0)
-
-    def _is_first_step(self):
-        return len(self.path.states) == 0
-
-    def _end_path(self):
-        s = self._record_state()
-        g = self._record_goal()
-        r = self._record_reward()
-
-        self.path.rewards.append(r)
-        self.path.states.append(s)
-        self.path.goals.append(g)
-        self.path.terminate = self.world.env.check_terminate(self.id)
-
-        return
-
-    def _update_new_action(self):
-        #print("_update_new_action!")
-        s = self._record_state()
-        #np.savetxt("pb_record_state_s.csv", s, delimiter=",")
-        g = self._record_goal()
-
-        if not (self._is_first_step()):
-            r = self._record_reward()
-            self.path.rewards.append(r)
-        
-        a, logp = self._decide_action(s=s, g=g)
-        assert len(np.shape(a)) == 1
-        assert len(np.shape(logp)) <= 1
-
-        flags = self._record_flags()
-        self._apply_action(a)
-
-        self.path.states.append(s)
-        self.path.goals.append(g)
-        self.path.actions.append(a)
-        self.path.logps.append(logp)
-        self.path.flags.append(flags)
-        
-        if self._enable_draw():
-            self._log_val(s, g)
-        
-        return
-    
-    def _update_exp_params(self):
-        lerp = float(self._total_sample_count) / self.exp_anneal_samples
-        lerp = np.clip(lerp, 0.0, 1.0)
-        self.exp_params_curr = self.exp_params_beg.lerp(self.exp_params_end, lerp)
-        return
-
-    def _update_test_return(self, path):
-        path_reward = path.calc_return()
-        self.test_return += path_reward
-        self.test_episode_count += 1
-        return
-
-    def _update_mode(self):
-        if (self._mode == self.Mode.TRAIN):
-            self._update_mode_train()
-        elif (self._mode == self.Mode.TRAIN_END):
-            self._update_mode_train_end()
-        elif (self._mode == self.Mode.TEST):
-            self._update_mode_test()
-        else:
-            assert False, Logger.print2("Unsupported RL agent mode" + str(self._mode))
-        return
-
-    def _update_mode_train(self):
-        return
-
-    def _update_mode_train_end(self):
-        self._init_mode_test()
-        return
-
-    def _update_mode_test(self):
-        if (self.test_episode_count * MPIUtil.get_num_procs() >= self.test_episodes):
-            global_return = MPIUtil.reduce_sum(self.test_return)
-            global_count = MPIUtil.reduce_sum(self.test_episode_count)
-            avg_return = global_return / global_count
-            self.avg_test_return = avg_return
-
-            if self.enable_training:
-                self._init_mode_train()
-        return
-
-    def _init_mode_train(self):
-        self._mode = self.Mode.TRAIN
-        self.world.env.set_mode(self._mode)
-        return
-
-    def _init_mode_train_end(self):
-        self._mode = self.Mode.TRAIN_END
-        return
-
-    def _init_mode_test(self):
-        self._mode = self.Mode.TEST
-        self.test_return = 0.0
-        self.test_episode_count = 0
-        self.world.env.set_mode(self._mode)
-        return
-
-    def _enable_output(self):
-        return MPIUtil.is_root_proc() and self.output_dir != ""
-
-    def _enable_int_output(self):
-        return MPIUtil.is_root_proc() and self.int_output_dir != ""
-    
-    def _calc_val_bounds(self, discount):
-        r_min = self.world.env.get_reward_min(self.id)
-        r_max = self.world.env.get_reward_max(self.id)
-        assert(r_min <= r_max)
-
-        val_min = r_min / ( 1.0 - discount)
-        val_max = r_max / ( 1.0 - discount)
-        return val_min, val_max
-
-    def _calc_val_offset_scale(self, discount):
-        val_min, val_max = self._calc_val_bounds(discount)
-        val_offset = 0
-        val_scale = 1
-
-        if (np.isfinite(val_min) and np.isfinite(val_max)):
-            val_offset = -0.5 * (val_max + val_min)
-            val_scale = 2 / (val_max - val_min)
-
-        return val_offset, val_scale
-
-    def _calc_term_vals(self, discount):
-        r_fail = self.world.env.get_reward_fail(self.id)
-        r_succ = self.world.env.get_reward_succ(self.id)
-
-        r_min = self.world.env.get_reward_min(self.id)
-        r_max = self.world.env.get_reward_max(self.id)
-        assert(r_fail <= r_max and r_fail >= r_min)
-        assert(r_succ <= r_max and r_succ >= r_min)
-        assert(not np.isinf(r_fail))
-        assert(not np.isinf(r_succ))
-
-        if (discount == 0):
-            val_fail = 0
-            val_succ = 0
-        else:
-            val_fail = r_fail / (1.0 - discount)
-            val_succ = r_succ / (1.0 - discount)
-
-        return val_fail, val_succ
-    
-    def _update_iter(self, iter):
-        if (self._enable_output() and self.iter % self.output_iters == 0):
-            output_path = self._get_output_path()
-            output_dir = os.path.dirname(output_path)
-            if not os.path.exists(output_dir):
-                os.makedirs(output_dir)
-            self.save_model(output_path)
-
-        if (self._enable_int_output() and self.iter % self.int_output_iters == 0):
-            int_output_path = self._get_int_output_path()
-            int_output_dir = os.path.dirname(int_output_path)
-            if not os.path.exists(int_output_dir):
-                os.makedirs(int_output_dir)
-            self.save_model(int_output_path)
-
-        self.iter = iter
-        return
-
-    def _enable_draw(self):
-        return self.world.env.enable_draw
-
-    def _log_val(self, s, g):
-        pass
-
-    def _build_replay_buffer(self, buffer_size):
-        num_procs = MPIUtil.get_num_procs()
-        buffer_size = int(buffer_size / num_procs)
-        self.replay_buffer = ReplayBuffer(buffer_size=buffer_size)
-        self.replay_buffer_initialized = False
-        return
-    
-    def _store_path(self, path):
-        path_id = self.replay_buffer.store(path)
-        valid_path = path_id != MathUtil.INVALID_IDX
-
-        if valid_path:
-            self.train_return = path.calc_return()
-
-            if self._need_normalizer_update:
-                self._record_normalizers(path)
-
-        return path_id
-
-    def _record_normalizers(self, path):
-        states = np.array(path.states)
-        self.s_norm.record(states)
-
-        if self.has_goal():
-            goals = np.array(path.goals)
-            self.g_norm.record(goals)
-
-        return
-
-    def _update_normalizers(self):
-        self.s_norm.update()
-
-        if self.has_goal():
-            self.g_norm.update()
-        return
-
-    def _train(self):
-        samples = self.replay_buffer.total_count
-        self._total_sample_count = int(MPIUtil.reduce_sum(samples))
-        end_training = False
-        
-        if (self.replay_buffer_initialized):  
-            if (self._valid_train_step()):
-                prev_iter = self.iter
-                iters = self._get_iters_per_update()
-                avg_train_return = MPIUtil.reduce_avg(self.train_return)
-            
-                for i in range(iters):
-                    curr_iter = self.iter
-                    wall_time = time.time() - self.start_time
-                    wall_time /= 60 * 60 # store time in hours
-
-                    has_goal = self.has_goal()
-                    s_mean = np.mean(self.s_norm.mean)
-                    s_std = np.mean(self.s_norm.std)
-                    g_mean = np.mean(self.g_norm.mean) if has_goal else 0
-                    g_std = np.mean(self.g_norm.std) if has_goal else 0
-
-                    self.logger.log_tabular("Iteration", self.iter)
-                    self.logger.log_tabular("Wall_Time", wall_time)
-                    self.logger.log_tabular("Samples", self._total_sample_count)
-                    self.logger.log_tabular("Train_Return", avg_train_return)
-                    self.logger.log_tabular("Test_Return", self.avg_test_return)
-                    self.logger.log_tabular("State_Mean", s_mean)
-                    self.logger.log_tabular("State_Std", s_std)
-                    self.logger.log_tabular("Goal_Mean", g_mean)
-                    self.logger.log_tabular("Goal_Std", g_std)
-                    self._log_exp_params()
-
-                    self._update_iter(self.iter + 1)
-                    self._train_step()
-
-                    Logger.print2("Agent " + str(self.id))
-                    self.logger.print_tabular()
-                    Logger.print2("") 
-
-                    if (self._enable_output() and curr_iter % self.int_output_iters == 0):
-                        self.logger.dump_tabular()
-
-                if (prev_iter // self.int_output_iters != self.iter // self.int_output_iters):
-                    end_training = self.enable_testing()
-
-        else:
-
-            Logger.print2("Agent " + str(self.id))
-            Logger.print2("Samples: " + str(self._total_sample_count))
-            Logger.print2("") 
-
-            if (self._total_sample_count >= self.init_samples):
-                self.replay_buffer_initialized = True
-                end_training = self.enable_testing()
-        
-        if self._need_normalizer_update:
-            self._update_normalizers()
-            self._need_normalizer_update = self.normalizer_samples > self._total_sample_count
-
-        if end_training:
-            self._init_mode_train_end()
- 
-        return
-
-    def _get_iters_per_update(self):
-        return MPIUtil.get_num_procs() * self.iters_per_update
-
-    def _valid_train_step(self):
-        return True 
-
-    def _log_exp_params(self):
-        self.logger.log_tabular("Exp_Rate", self.exp_params_curr.rate)
-        self.logger.log_tabular("Exp_Noise", self.exp_params_curr.noise)
-        self.logger.log_tabular("Exp_Temp", self.exp_params_curr.temp)
-        return
+
+  class Mode(Enum):
+    TRAIN = 0
+    TEST = 1
+    TRAIN_END = 2
+
+  NAME = "None"
+
+  UPDATE_PERIOD_KEY = "UpdatePeriod"
+  ITERS_PER_UPDATE = "ItersPerUpdate"
+  DISCOUNT_KEY = "Discount"
+  MINI_BATCH_SIZE_KEY = "MiniBatchSize"
+  REPLAY_BUFFER_SIZE_KEY = "ReplayBufferSize"
+  INIT_SAMPLES_KEY = "InitSamples"
+  NORMALIZER_SAMPLES_KEY = "NormalizerSamples"
+
+  OUTPUT_ITERS_KEY = "OutputIters"
+  INT_OUTPUT_ITERS_KEY = "IntOutputIters"
+  TEST_EPISODES_KEY = "TestEpisodes"
+
+  EXP_ANNEAL_SAMPLES_KEY = "ExpAnnealSamples"
+  EXP_PARAM_BEG_KEY = "ExpParamsBeg"
+  EXP_PARAM_END_KEY = "ExpParamsEnd"
+
+  def __init__(self, world, id, json_data):
+    self.world = world
+    self.id = id
+    self.logger = Logger()
+    self._mode = self.Mode.TRAIN
+
+    assert self._check_action_space(), \
+        Logger.print2("Invalid action space, got {:s}".format(str(self.get_action_space())))
+
+    self._enable_training = True
+    self.path = Path()
+    self.iter = int(0)
+    self.start_time = time.time()
+    self._update_counter = 0
+
+    self.update_period = 1.0  # simulated time (seconds) before each training update
+    self.iters_per_update = int(1)
+    self.discount = 0.95
+    self.mini_batch_size = int(32)
+    self.replay_buffer_size = int(50000)
+    self.init_samples = int(1000)
+    self.normalizer_samples = np.inf
+    self._local_mini_batch_size = self.mini_batch_size  # batch size for each work for multiprocessing
+    self._need_normalizer_update = True
+    self._total_sample_count = 0
+
+    self._output_dir = ""
+    self._int_output_dir = ""
+    self.output_iters = 100
+    self.int_output_iters = 100
+
+    self.train_return = 0.0
+    self.test_episodes = int(0)
+    self.test_episode_count = int(0)
+    self.test_return = 0.0
+    self.avg_test_return = 0.0
+
+    self.exp_anneal_samples = 320000
+    self.exp_params_beg = ExpParams()
+    self.exp_params_end = ExpParams()
+    self.exp_params_curr = ExpParams()
+
+    self._load_params(json_data)
+    self._build_replay_buffer(self.replay_buffer_size)
+    self._build_normalizers()
+    self._build_bounds()
+    self.reset()
+
+    return
+
+  def __str__(self):
+    action_space_str = str(self.get_action_space())
+    info_str = ""
+    info_str += '"ID": {:d},\n "Type": "{:s}",\n "ActionSpace": "{:s}",\n "StateDim": {:d},\n "GoalDim": {:d},\n "ActionDim": {:d}'.format(
+        self.id, self.NAME, action_space_str[action_space_str.rfind('.') + 1:],
+        self.get_state_size(), self.get_goal_size(), self.get_action_size())
+    return "{\n" + info_str + "\n}"
+
+  def get_output_dir(self):
+    return self._output_dir
+
+  def set_output_dir(self, out_dir):
+    self._output_dir = out_dir
+    if (self._output_dir != ""):
+      self.logger.configure_output_file(out_dir + "/agent" + str(self.id) + "_log.txt")
+    return
+
+  output_dir = property(get_output_dir, set_output_dir)
+
+  def get_int_output_dir(self):
+    return self._int_output_dir
+
+  def set_int_output_dir(self, out_dir):
+    self._int_output_dir = out_dir
+    return
+
+  int_output_dir = property(get_int_output_dir, set_int_output_dir)
+
+  def reset(self):
+    self.path.clear()
+    return
+
+  def update(self, timestep):
+    if self.need_new_action():
+      #print("update_new_action!!!")
+      self._update_new_action()
+
+    if (self._mode == self.Mode.TRAIN and self.enable_training):
+      self._update_counter += timestep
+
+      while self._update_counter >= self.update_period:
+        self._train()
+        self._update_exp_params()
+        self.world.env.set_sample_count(self._total_sample_count)
+        self._update_counter -= self.update_period
+
+    return
+
+  def end_episode(self):
+    if (self.path.pathlength() > 0):
+      self._end_path()
+
+      if (self._mode == self.Mode.TRAIN or self._mode == self.Mode.TRAIN_END):
+        if (self.enable_training and self.path.pathlength() > 0):
+          self._store_path(self.path)
+      elif (self._mode == self.Mode.TEST):
+        self._update_test_return(self.path)
+      else:
+        assert False, Logger.print2("Unsupported RL agent mode" + str(self._mode))
+
+      self._update_mode()
+    return
+
+  def has_goal(self):
+    return self.get_goal_size() > 0
+
+  def predict_val(self):
+    return 0
+
+  def get_enable_training(self):
+    return self._enable_training
+
+  def set_enable_training(self, enable):
+    print("set_enable_training=", enable)
+    self._enable_training = enable
+    if (self._enable_training):
+      self.reset()
+    return
+
+  enable_training = property(get_enable_training, set_enable_training)
+
+  def enable_testing(self):
+    return self.test_episodes > 0
+
+  def get_name(self):
+    return self.NAME
+
+  @abstractmethod
+  def save_model(self, out_path):
+    pass
+
+  @abstractmethod
+  def load_model(self, in_path):
+    pass
+
+  @abstractmethod
+  def _decide_action(self, s, g):
+    pass
+
+  @abstractmethod
+  def _get_output_path(self):
+    pass
+
+  @abstractmethod
+  def _get_int_output_path(self):
+    pass
+
+  @abstractmethod
+  def _train_step(self):
+    pass
+
+  @abstractmethod
+  def _check_action_space(self):
+    pass
+
+  def get_action_space(self):
+    return self.world.env.get_action_space(self.id)
+
+  def get_state_size(self):
+    return self.world.env.get_state_size(self.id)
+
+  def get_goal_size(self):
+    return self.world.env.get_goal_size(self.id)
+
+  def get_action_size(self):
+    return self.world.env.get_action_size(self.id)
+
+  def get_num_actions(self):
+    return self.world.env.get_num_actions(self.id)
+
+  def need_new_action(self):
+    return self.world.env.need_new_action(self.id)
+
+  def _build_normalizers(self):
+    self.s_norm = Normalizer(self.get_state_size(),
+                             self.world.env.build_state_norm_groups(self.id))
+    self.s_norm.set_mean_std(-self.world.env.build_state_offset(self.id),
+                             1 / self.world.env.build_state_scale(self.id))
+
+    self.g_norm = Normalizer(self.get_goal_size(), self.world.env.build_goal_norm_groups(self.id))
+    self.g_norm.set_mean_std(-self.world.env.build_goal_offset(self.id),
+                             1 / self.world.env.build_goal_scale(self.id))
+
+    self.a_norm = Normalizer(self.world.env.get_action_size())
+    self.a_norm.set_mean_std(-self.world.env.build_action_offset(self.id),
+                             1 / self.world.env.build_action_scale(self.id))
+    return
+
+  def _build_bounds(self):
+    self.a_bound_min = self.world.env.build_action_bound_min(self.id)
+    self.a_bound_max = self.world.env.build_action_bound_max(self.id)
+    return
+
+  def _load_params(self, json_data):
+    if (self.UPDATE_PERIOD_KEY in json_data):
+      self.update_period = int(json_data[self.UPDATE_PERIOD_KEY])
+
+    if (self.ITERS_PER_UPDATE in json_data):
+      self.iters_per_update = int(json_data[self.ITERS_PER_UPDATE])
+
+    if (self.DISCOUNT_KEY in json_data):
+      self.discount = json_data[self.DISCOUNT_KEY]
+
+    if (self.MINI_BATCH_SIZE_KEY in json_data):
+      self.mini_batch_size = int(json_data[self.MINI_BATCH_SIZE_KEY])
+
+    if (self.REPLAY_BUFFER_SIZE_KEY in json_data):
+      self.replay_buffer_size = int(json_data[self.REPLAY_BUFFER_SIZE_KEY])
+
+    if (self.INIT_SAMPLES_KEY in json_data):
+      self.init_samples = int(json_data[self.INIT_SAMPLES_KEY])
+
+    if (self.NORMALIZER_SAMPLES_KEY in json_data):
+      self.normalizer_samples = int(json_data[self.NORMALIZER_SAMPLES_KEY])
+
+    if (self.OUTPUT_ITERS_KEY in json_data):
+      self.output_iters = json_data[self.OUTPUT_ITERS_KEY]
+
+    if (self.INT_OUTPUT_ITERS_KEY in json_data):
+      self.int_output_iters = json_data[self.INT_OUTPUT_ITERS_KEY]
+
+    if (self.TEST_EPISODES_KEY in json_data):
+      self.test_episodes = int(json_data[self.TEST_EPISODES_KEY])
+
+    if (self.EXP_ANNEAL_SAMPLES_KEY in json_data):
+      self.exp_anneal_samples = json_data[self.EXP_ANNEAL_SAMPLES_KEY]
+
+    if (self.EXP_PARAM_BEG_KEY in json_data):
+      self.exp_params_beg.load(json_data[self.EXP_PARAM_BEG_KEY])
+
+    if (self.EXP_PARAM_END_KEY in json_data):
+      self.exp_params_end.load(json_data[self.EXP_PARAM_END_KEY])
+
+    num_procs = MPIUtil.get_num_procs()
+    self._local_mini_batch_size = int(np.ceil(self.mini_batch_size / num_procs))
+    self._local_mini_batch_size = np.maximum(self._local_mini_batch_size, 1)
+    self.mini_batch_size = self._local_mini_batch_size * num_procs
+
+    assert (self.exp_params_beg.noise == self.exp_params_end.noise)  # noise std should not change
+    self.exp_params_curr = copy.deepcopy(self.exp_params_beg)
+    self.exp_params_end.noise = self.exp_params_beg.noise
+
+    self._need_normalizer_update = self.normalizer_samples > 0
+
+    return
+
+  def _record_state(self):
+    s = self.world.env.record_state(self.id)
+    return s
+
+  def _record_goal(self):
+    g = self.world.env.record_goal(self.id)
+    return g
+
+  def _record_reward(self):
+    r = self.world.env.calc_reward(self.id)
+    return r
+
+  def _apply_action(self, a):
+    self.world.env.set_action(self.id, a)
+    return
+
+  def _record_flags(self):
+    return int(0)
+
+  def _is_first_step(self):
+    return len(self.path.states) == 0
+
+  def _end_path(self):
+    s = self._record_state()
+    g = self._record_goal()
+    r = self._record_reward()
+
+    self.path.rewards.append(r)
+    self.path.states.append(s)
+    self.path.goals.append(g)
+    self.path.terminate = self.world.env.check_terminate(self.id)
+
+    return
+
+  def _update_new_action(self):
+    #print("_update_new_action!")
+    s = self._record_state()
+    #np.savetxt("pb_record_state_s.csv", s, delimiter=",")
+    g = self._record_goal()
+
+    if not (self._is_first_step()):
+      r = self._record_reward()
+      self.path.rewards.append(r)
+
+    a, logp = self._decide_action(s=s, g=g)
+    assert len(np.shape(a)) == 1
+    assert len(np.shape(logp)) <= 1
+
+    flags = self._record_flags()
+    self._apply_action(a)
+
+    self.path.states.append(s)
+    self.path.goals.append(g)
+    self.path.actions.append(a)
+    self.path.logps.append(logp)
+    self.path.flags.append(flags)
+
+    if self._enable_draw():
+      self._log_val(s, g)
+
+    return
+
+  def _update_exp_params(self):
+    lerp = float(self._total_sample_count) / self.exp_anneal_samples
+    lerp = np.clip(lerp, 0.0, 1.0)
+    self.exp_params_curr = self.exp_params_beg.lerp(self.exp_params_end, lerp)
+    return
+
+  def _update_test_return(self, path):
+    path_reward = path.calc_return()
+    self.test_return += path_reward
+    self.test_episode_count += 1
+    return
+
+  def _update_mode(self):
+    if (self._mode == self.Mode.TRAIN):
+      self._update_mode_train()
+    elif (self._mode == self.Mode.TRAIN_END):
+      self._update_mode_train_end()
+    elif (self._mode == self.Mode.TEST):
+      self._update_mode_test()
+    else:
+      assert False, Logger.print2("Unsupported RL agent mode" + str(self._mode))
+    return
+
+  def _update_mode_train(self):
+    return
+
+  def _update_mode_train_end(self):
+    self._init_mode_test()
+    return
+
+  def _update_mode_test(self):
+    if (self.test_episode_count * MPIUtil.get_num_procs() >= self.test_episodes):
+      global_return = MPIUtil.reduce_sum(self.test_return)
+      global_count = MPIUtil.reduce_sum(self.test_episode_count)
+      avg_return = global_return / global_count
+      self.avg_test_return = avg_return
+
+      if self.enable_training:
+        self._init_mode_train()
+    return
+
+  def _init_mode_train(self):
+    self._mode = self.Mode.TRAIN
+    self.world.env.set_mode(self._mode)
+    return
+
+  def _init_mode_train_end(self):
+    self._mode = self.Mode.TRAIN_END
+    return
+
+  def _init_mode_test(self):
+    self._mode = self.Mode.TEST
+    self.test_return = 0.0
+    self.test_episode_count = 0
+    self.world.env.set_mode(self._mode)
+    return
+
+  def _enable_output(self):
+    return MPIUtil.is_root_proc() and self.output_dir != ""
+
+  def _enable_int_output(self):
+    return MPIUtil.is_root_proc() and self.int_output_dir != ""
+
+  def _calc_val_bounds(self, discount):
+    r_min = self.world.env.get_reward_min(self.id)
+    r_max = self.world.env.get_reward_max(self.id)
+    assert (r_min <= r_max)
+
+    val_min = r_min / (1.0 - discount)
+    val_max = r_max / (1.0 - discount)
+    return val_min, val_max
+
+  def _calc_val_offset_scale(self, discount):
+    val_min, val_max = self._calc_val_bounds(discount)
+    val_offset = 0
+    val_scale = 1
+
+    if (np.isfinite(val_min) and np.isfinite(val_max)):
+      val_offset = -0.5 * (val_max + val_min)
+      val_scale = 2 / (val_max - val_min)
+
+    return val_offset, val_scale
+
+  def _calc_term_vals(self, discount):
+    r_fail = self.world.env.get_reward_fail(self.id)
+    r_succ = self.world.env.get_reward_succ(self.id)
+
+    r_min = self.world.env.get_reward_min(self.id)
+    r_max = self.world.env.get_reward_max(self.id)
+    assert (r_fail <= r_max and r_fail >= r_min)
+    assert (r_succ <= r_max and r_succ >= r_min)
+    assert (not np.isinf(r_fail))
+    assert (not np.isinf(r_succ))
+
+    if (discount == 0):
+      val_fail = 0
+      val_succ = 0
+    else:
+      val_fail = r_fail / (1.0 - discount)
+      val_succ = r_succ / (1.0 - discount)
+
+    return val_fail, val_succ
+
+  def _update_iter(self, iter):
+    if (self._enable_output() and self.iter % self.output_iters == 0):
+      output_path = self._get_output_path()
+      output_dir = os.path.dirname(output_path)
+      if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+      self.save_model(output_path)
+
+    if (self._enable_int_output() and self.iter % self.int_output_iters == 0):
+      int_output_path = self._get_int_output_path()
+      int_output_dir = os.path.dirname(int_output_path)
+      if not os.path.exists(int_output_dir):
+        os.makedirs(int_output_dir)
+      self.save_model(int_output_path)
+
+    self.iter = iter
+    return
+
+  def _enable_draw(self):
+    return self.world.env.enable_draw
+
+  def _log_val(self, s, g):
+    pass
+
+  def _build_replay_buffer(self, buffer_size):
+    num_procs = MPIUtil.get_num_procs()
+    buffer_size = int(buffer_size / num_procs)
+    self.replay_buffer = ReplayBuffer(buffer_size=buffer_size)
+    self.replay_buffer_initialized = False
+    return
+
+  def _store_path(self, path):
+    path_id = self.replay_buffer.store(path)
+    valid_path = path_id != MathUtil.INVALID_IDX
+
+    if valid_path:
+      self.train_return = path.calc_return()
+
+      if self._need_normalizer_update:
+        self._record_normalizers(path)
+
+    return path_id
+
+  def _record_normalizers(self, path):
+    states = np.array(path.states)
+    self.s_norm.record(states)
+
+    if self.has_goal():
+      goals = np.array(path.goals)
+      self.g_norm.record(goals)
+
+    return
+
+  def _update_normalizers(self):
+    self.s_norm.update()
+
+    if self.has_goal():
+      self.g_norm.update()
+    return
+
+  def _train(self):
+    samples = self.replay_buffer.total_count
+    self._total_sample_count = int(MPIUtil.reduce_sum(samples))
+    end_training = False
+
+    if (self.replay_buffer_initialized):
+      if (self._valid_train_step()):
+        prev_iter = self.iter
+        iters = self._get_iters_per_update()
+        avg_train_return = MPIUtil.reduce_avg(self.train_return)
+
+        for i in range(iters):
+          curr_iter = self.iter
+          wall_time = time.time() - self.start_time
+          wall_time /= 60 * 60  # store time in hours
+
+          has_goal = self.has_goal()
+          s_mean = np.mean(self.s_norm.mean)
+          s_std = np.mean(self.s_norm.std)
+          g_mean = np.mean(self.g_norm.mean) if has_goal else 0
+          g_std = np.mean(self.g_norm.std) if has_goal else 0
+
+          self.logger.log_tabular("Iteration", self.iter)
+          self.logger.log_tabular("Wall_Time", wall_time)
+          self.logger.log_tabular("Samples", self._total_sample_count)
+          self.logger.log_tabular("Train_Return", avg_train_return)
+          self.logger.log_tabular("Test_Return", self.avg_test_return)
+          self.logger.log_tabular("State_Mean", s_mean)
+          self.logger.log_tabular("State_Std", s_std)
+          self.logger.log_tabular("Goal_Mean", g_mean)
+          self.logger.log_tabular("Goal_Std", g_std)
+          self._log_exp_params()
+
+          self._update_iter(self.iter + 1)
+          self._train_step()
+
+          Logger.print2("Agent " + str(self.id))
+          self.logger.print_tabular()
+          Logger.print2("")
+
+          if (self._enable_output() and curr_iter % self.int_output_iters == 0):
+            self.logger.dump_tabular()
+
+        if (prev_iter // self.int_output_iters != self.iter // self.int_output_iters):
+          end_training = self.enable_testing()
+
+    else:
+
+      Logger.print2("Agent " + str(self.id))
+      Logger.print2("Samples: " + str(self._total_sample_count))
+      Logger.print2("")
+
+      if (self._total_sample_count >= self.init_samples):
+        self.replay_buffer_initialized = True
+        end_training = self.enable_testing()
+
+    if self._need_normalizer_update:
+      self._update_normalizers()
+      self._need_normalizer_update = self.normalizer_samples > self._total_sample_count
+
+    if end_training:
+      self._init_mode_train_end()
+
+    return
+
+  def _get_iters_per_update(self):
+    return MPIUtil.get_num_procs() * self.iters_per_update
+
+  def _valid_train_step(self):
+    return True
+
+  def _log_exp_params(self):
+    self.logger.log_tabular("Exp_Rate", self.exp_params_curr.rate)
+    self.logger.log_tabular("Exp_Noise", self.exp_params_curr.noise)
+    self.logger.log_tabular("Exp_Temp", self.exp_params_curr.temp)
+    return
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_util.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_util.py
index 12c682a19..71132e15d 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_util.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_util.py
@@ -1,18 +1,19 @@
 import numpy as np
 
+
 def compute_return(rewards, gamma, td_lambda, val_t):
-    # computes td-lambda return of path
-    path_len = len(rewards)
-    assert len(val_t) == path_len + 1
+  # computes td-lambda return of path
+  path_len = len(rewards)
+  assert len(val_t) == path_len + 1
+
+  return_t = np.zeros(path_len)
+  last_val = rewards[-1] + gamma * val_t[-1]
+  return_t[-1] = last_val
 
-    return_t = np.zeros(path_len)
-    last_val = rewards[-1] + gamma * val_t[-1]
-    return_t[-1] = last_val
+  for i in reversed(range(0, path_len - 1)):
+    curr_r = rewards[i]
+    next_ret = return_t[i + 1]
+    curr_val = curr_r + gamma * ((1.0 - td_lambda) * val_t[i + 1] + td_lambda * next_ret)
+    return_t[i] = curr_val
 
-    for i in reversed(range(0, path_len - 1)):
-        curr_r = rewards[i]
-        next_ret = return_t[i + 1]
-        curr_val = curr_r + gamma * ((1.0 - td_lambda) * val_t[i + 1] + td_lambda * next_ret)
-        return_t[i] = curr_val
-    
-    return return_t
\ No newline at end of file
+  return return_t
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_world.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_world.py
index a1761bc30..32841eade 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_world.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_world.py
@@ -5,139 +5,140 @@ from pybullet_envs.deep_mimic.learning.rl_agent import RLAgent
 from pybullet_utils.logger import Logger
 import pybullet_data
 
+
 class RLWorld(object):
-    def __init__(self, env, arg_parser):
-        TFUtil.disable_gpu()
-
-        self.env = env
-        self.arg_parser = arg_parser
-        self._enable_training = True
-        self.train_agents = []
-        self.parse_args(arg_parser)
-
-        self.build_agents()
-        
-        return
-
-    def get_enable_training(self):
-        return self._enable_training
-    
-    def set_enable_training(self, enable):
-        self._enable_training = enable
-        for i in range(len(self.agents)):
-            curr_agent = self.agents[i]
-            if curr_agent is not None:
-                enable_curr_train = self.train_agents[i] if (len(self.train_agents) > 0) else True
-                curr_agent.enable_training = self.enable_training and enable_curr_train
-
-        if (self._enable_training):
-            self.env.set_mode(RLAgent.Mode.TRAIN)
-        else:
-            self.env.set_mode(RLAgent.Mode.TEST)
-
-        return
-
-    enable_training = property(get_enable_training, set_enable_training)
-    
-    def parse_args(self, arg_parser):
-        self.train_agents = self.arg_parser.parse_bools('train_agents')
-        num_agents = self.env.get_num_agents()
-        assert(len(self.train_agents) == num_agents or len(self.train_agents) == 0)
-
-        return
-
-    def shutdown(self):
-        self.env.shutdown()
-        return
-
-    def build_agents(self):
-        num_agents = self.env.get_num_agents()
-        print("num_agents=",num_agents)
-        self.agents = []
-
-        Logger.print2('')
-        Logger.print2('Num Agents: {:d}'.format(num_agents))
-
-        agent_files = self.arg_parser.parse_strings('agent_files')
-        print("len(agent_files)=",len(agent_files))
-        assert(len(agent_files) == num_agents or len(agent_files) == 0)
-
-        model_files = self.arg_parser.parse_strings('model_files')
-        assert(len(model_files) == num_agents or len(model_files) == 0)
-
-        output_path = self.arg_parser.parse_string('output_path')
-        int_output_path = self.arg_parser.parse_string('int_output_path')
-
-        for i in range(num_agents):
-            curr_file = agent_files[i]
-            curr_agent = self._build_agent(i, curr_file)
-
-            if curr_agent is not None:
-                curr_agent.output_dir = output_path
-                curr_agent.int_output_dir = int_output_path
-                Logger.print2(str(curr_agent))
-
-                if (len(model_files) > 0):
-                    curr_model_file = model_files[i]
-                    if curr_model_file != 'none':
-                        curr_agent.load_model(pybullet_data.getDataPath()+"/"+curr_model_file)
-
-            self.agents.append(curr_agent)
-            Logger.print2('')
-
-        self.set_enable_training(self.enable_training)
-
-        return
-
-    def update(self, timestep):
-        #print("world update!\n")
-        self._update_agents(timestep)
-        self._update_env(timestep)
-        return
-
-    def reset(self):
-        self._reset_agents()
-        self._reset_env()
-        return
-
-    def end_episode(self):
-        self._end_episode_agents();
-        return
-
-    def _update_env(self, timestep):
-        self.env.update(timestep)
-        return
-
-    def _update_agents(self, timestep):
-        #print("len(agents)=",len(self.agents))
-        for agent in self.agents:
-            if (agent is not None):
-                agent.update(timestep)
-        return
-
-    def _reset_env(self):
-        self.env.reset()
-        return
-
-    def _reset_agents(self):
-        for agent in self.agents:
-            if (agent != None):
-                agent.reset()
-        return
-
-    def _end_episode_agents(self):
-        for agent in self.agents:
-            if (agent != None):
-                agent.end_episode()
-        return
-
-    def _build_agent(self, id, agent_file):
-        Logger.print2('Agent {:d}: {}'.format(id, agent_file))
-        if (agent_file == 'none'):
-            agent = None
-        else:
-            agent = AgentBuilder.build_agent(self, id, agent_file)
-            assert (agent != None), 'Failed to build agent {:d} from: {}'.format(id, agent_file)
-        
-        return agent
-        
+
+  def __init__(self, env, arg_parser):
+    TFUtil.disable_gpu()
+
+    self.env = env
+    self.arg_parser = arg_parser
+    self._enable_training = True
+    self.train_agents = []
+    self.parse_args(arg_parser)
+
+    self.build_agents()
+
+    return
+
+  def get_enable_training(self):
+    return self._enable_training
+
+  def set_enable_training(self, enable):
+    self._enable_training = enable
+    for i in range(len(self.agents)):
+      curr_agent = self.agents[i]
+      if curr_agent is not None:
+        enable_curr_train = self.train_agents[i] if (len(self.train_agents) > 0) else True
+        curr_agent.enable_training = self.enable_training and enable_curr_train
+
+    if (self._enable_training):
+      self.env.set_mode(RLAgent.Mode.TRAIN)
+    else:
+      self.env.set_mode(RLAgent.Mode.TEST)
+
+    return
+
+  enable_training = property(get_enable_training, set_enable_training)
+
+  def parse_args(self, arg_parser):
+    self.train_agents = self.arg_parser.parse_bools('train_agents')
+    num_agents = self.env.get_num_agents()
+    assert (len(self.train_agents) == num_agents or len(self.train_agents) == 0)
+
+    return
+
+  def shutdown(self):
+    self.env.shutdown()
+    return
+
+  def build_agents(self):
+    num_agents = self.env.get_num_agents()
+    print("num_agents=", num_agents)
+    self.agents = []
+
+    Logger.print2('')
+    Logger.print2('Num Agents: {:d}'.format(num_agents))
+
+    agent_files = self.arg_parser.parse_strings('agent_files')
+    print("len(agent_files)=", len(agent_files))
+    assert (len(agent_files) == num_agents or len(agent_files) == 0)
+
+    model_files = self.arg_parser.parse_strings('model_files')
+    assert (len(model_files) == num_agents or len(model_files) == 0)
+
+    output_path = self.arg_parser.parse_string('output_path')
+    int_output_path = self.arg_parser.parse_string('int_output_path')
+
+    for i in range(num_agents):
+      curr_file = agent_files[i]
+      curr_agent = self._build_agent(i, curr_file)
+
+      if curr_agent is not None:
+        curr_agent.output_dir = output_path
+        curr_agent.int_output_dir = int_output_path
+        Logger.print2(str(curr_agent))
+
+        if (len(model_files) > 0):
+          curr_model_file = model_files[i]
+          if curr_model_file != 'none':
+            curr_agent.load_model(pybullet_data.getDataPath() + "/" + curr_model_file)
+
+      self.agents.append(curr_agent)
+      Logger.print2('')
+
+    self.set_enable_training(self.enable_training)
+
+    return
+
+  def update(self, timestep):
+    #print("world update!\n")
+    self._update_agents(timestep)
+    self._update_env(timestep)
+    return
+
+  def reset(self):
+    self._reset_agents()
+    self._reset_env()
+    return
+
+  def end_episode(self):
+    self._end_episode_agents()
+    return
+
+  def _update_env(self, timestep):
+    self.env.update(timestep)
+    return
+
+  def _update_agents(self, timestep):
+    #print("len(agents)=",len(self.agents))
+    for agent in self.agents:
+      if (agent is not None):
+        agent.update(timestep)
+    return
+
+  def _reset_env(self):
+    self.env.reset()
+    return
+
+  def _reset_agents(self):
+    for agent in self.agents:
+      if (agent != None):
+        agent.reset()
+    return
+
+  def _end_episode_agents(self):
+    for agent in self.agents:
+      if (agent != None):
+        agent.end_episode()
+    return
+
+  def _build_agent(self, id, agent_file):
+    Logger.print2('Agent {:d}: {}'.format(id, agent_file))
+    if (agent_file == 'none'):
+      agent = None
+    else:
+      agent = AgentBuilder.build_agent(self, id, agent_file)
+      assert (agent != None), 'Failed to build agent {:d} from: {}'.format(id, agent_file)
+
+    return agent
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/mpi_solver.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/mpi_solver.py
index f2d18051c..0077f4b11 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/mpi_solver.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/mpi_solver.py
@@ -8,96 +8,97 @@ from pybullet_utils.logger import Logger
 
 from pybullet_envs.deep_mimic.learning.solvers.solver import Solver
 
+
 class MPISolver(Solver):
-    CHECK_SYNC_ITERS = 1000
-
-    def __init__(self, sess, optimizer, vars):
-        super().__init__(vars)
-        self.sess = sess
-        self.optimizer = optimizer
-        self._build_grad_feed(vars)
-        self._update = optimizer.apply_gradients(zip(self._grad_tf_list, self.vars))
-        self._set_flat_vars = TFUtil.SetFromFlat(sess, self.vars)
-        self._get_flat_vars = TFUtil.GetFlat(sess, self.vars)
-
-        self.iter = 0
-        grad_dim = self._calc_grad_dim()
-        self._flat_grad = np.zeros(grad_dim, dtype=np.float32)
-        self._global_flat_grad = np.zeros(grad_dim, dtype=np.float32)
-        
-        return
-
-    def get_stepsize(self):
-        return self.optimizer._learning_rate_tensor.eval()
-
-    def update(self, grads=None, grad_scale=1.0):
-        if grads is not None:
-            self._flat_grad = MathUtil.flatten(grads)
-        else:
-            self._flat_grad.fill(0)
-        return self.update_flatgrad(self._flat_grad, grad_scale)
-
-    def update_flatgrad(self, flat_grad, grad_scale=1.0):
-        if self.iter % self.CHECK_SYNC_ITERS == 0:
-            assert self.check_synced(), Logger.print2('Network parameters desynchronized')
-        
-        if grad_scale != 1.0:
-            flat_grad *= grad_scale
-
-        MPI.COMM_WORLD.Allreduce(flat_grad, self._global_flat_grad, op=MPI.SUM)
-        self._global_flat_grad /= MPIUtil.get_num_procs()
-
-        self._load_flat_grad(self._global_flat_grad)
-        self.sess.run([self._update], self._grad_feed)
-        self.iter += 1
-
-        return
-
-    def sync(self):
-        vars = self._get_flat_vars()
-        MPIUtil.bcast(vars)
-        self._set_flat_vars(vars)
-        return
-
-    def check_synced(self):
-        synced = True
-        if self._is_root():
-            vars = self._get_flat_vars()
-            MPIUtil.bcast(vars)
-        else:
-            vars_local = self._get_flat_vars()
-            vars_root = np.empty_like(vars_local)
-            MPIUtil.bcast(vars_root)
-            synced = (vars_local == vars_root).all()
-        return synced
-
-    def _is_root(self):
-        return MPIUtil.is_root_proc()
-    
-    def _build_grad_feed(self, vars):
-        self._grad_tf_list = []
-        self._grad_buffers = []
-        for v in self.vars:
-            shape = v.get_shape()
-            grad = np.zeros(shape)
-            grad_tf = tf.placeholder(tf.float32, shape=shape)
-            self._grad_buffers.append(grad)
-            self._grad_tf_list.append(grad_tf)
-
-        self._grad_feed = dict({g_tf: g for g_tf, g in zip(self._grad_tf_list, self._grad_buffers)})
-        
-        return
-
-    def _calc_grad_dim(self):
-        grad_dim = 0
-        for grad in self._grad_buffers:
-            grad_dim += grad.size
-        return grad_dim
-
-    def _load_flat_grad(self, flat_grad):
-        start = 0
-        for g in self._grad_buffers:
-            size = g.size
-            np.copyto(g, np.reshape(flat_grad[start:start + size], g.shape))
-            start += size
-        return
\ No newline at end of file
+  CHECK_SYNC_ITERS = 1000
+
+  def __init__(self, sess, optimizer, vars):
+    super().__init__(vars)
+    self.sess = sess
+    self.optimizer = optimizer
+    self._build_grad_feed(vars)
+    self._update = optimizer.apply_gradients(zip(self._grad_tf_list, self.vars))
+    self._set_flat_vars = TFUtil.SetFromFlat(sess, self.vars)
+    self._get_flat_vars = TFUtil.GetFlat(sess, self.vars)
+
+    self.iter = 0
+    grad_dim = self._calc_grad_dim()
+    self._flat_grad = np.zeros(grad_dim, dtype=np.float32)
+    self._global_flat_grad = np.zeros(grad_dim, dtype=np.float32)
+
+    return
+
+  def get_stepsize(self):
+    return self.optimizer._learning_rate_tensor.eval()
+
+  def update(self, grads=None, grad_scale=1.0):
+    if grads is not None:
+      self._flat_grad = MathUtil.flatten(grads)
+    else:
+      self._flat_grad.fill(0)
+    return self.update_flatgrad(self._flat_grad, grad_scale)
+
+  def update_flatgrad(self, flat_grad, grad_scale=1.0):
+    if self.iter % self.CHECK_SYNC_ITERS == 0:
+      assert self.check_synced(), Logger.print2('Network parameters desynchronized')
+
+    if grad_scale != 1.0:
+      flat_grad *= grad_scale
+
+    MPI.COMM_WORLD.Allreduce(flat_grad, self._global_flat_grad, op=MPI.SUM)
+    self._global_flat_grad /= MPIUtil.get_num_procs()
+
+    self._load_flat_grad(self._global_flat_grad)
+    self.sess.run([self._update], self._grad_feed)
+    self.iter += 1
+
+    return
+
+  def sync(self):
+    vars = self._get_flat_vars()
+    MPIUtil.bcast(vars)
+    self._set_flat_vars(vars)
+    return
+
+  def check_synced(self):
+    synced = True
+    if self._is_root():
+      vars = self._get_flat_vars()
+      MPIUtil.bcast(vars)
+    else:
+      vars_local = self._get_flat_vars()
+      vars_root = np.empty_like(vars_local)
+      MPIUtil.bcast(vars_root)
+      synced = (vars_local == vars_root).all()
+    return synced
+
+  def _is_root(self):
+    return MPIUtil.is_root_proc()
+
+  def _build_grad_feed(self, vars):
+    self._grad_tf_list = []
+    self._grad_buffers = []
+    for v in self.vars:
+      shape = v.get_shape()
+      grad = np.zeros(shape)
+      grad_tf = tf.placeholder(tf.float32, shape=shape)
+      self._grad_buffers.append(grad)
+      self._grad_tf_list.append(grad_tf)
+
+    self._grad_feed = dict({g_tf: g for g_tf, g in zip(self._grad_tf_list, self._grad_buffers)})
+
+    return
+
+  def _calc_grad_dim(self):
+    grad_dim = 0
+    for grad in self._grad_buffers:
+      grad_dim += grad.size
+    return grad_dim
+
+  def _load_flat_grad(self, flat_grad):
+    start = 0
+    for g in self._grad_buffers:
+      size = g.size
+      np.copyto(g, np.reshape(flat_grad[start:start + size], g.shape))
+      start += size
+    return
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/solver.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/solver.py
index cd2765272..3491b40ed 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/solver.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/solvers/solver.py
@@ -1,15 +1,17 @@
 from abc import abstractmethod
 import sys, abc
 if sys.version_info >= (3, 4):
-    ABC = abc.ABC
+  ABC = abc.ABC
 else:
-    ABC = abc.ABCMeta('ABC', (), {})
+  ABC = abc.ABCMeta('ABC', (), {})
+
 
 class Solver(ABC):
-    def __init__(self, vars):
-        self.vars = vars
-        return
 
-    @abstractmethod
-    def update(self, grads):
-        pass
+  def __init__(self, vars):
+    self.vars = vars
+    return
+
+  @abstractmethod
+  def update(self, grads):
+    pass
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_agent.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_agent.py
index 95d707140..1770e5329 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_agent.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_agent.py
@@ -6,144 +6,148 @@ from pybullet_envs.deep_mimic.learning.rl_agent import RLAgent
 from pybullet_utils.logger import Logger
 from pybullet_envs.deep_mimic.learning.tf_normalizer import TFNormalizer
 
+
 class TFAgent(RLAgent):
-    RESOURCE_SCOPE = 'resource'
-    SOLVER_SCOPE = 'solvers'
-
-    def __init__(self, world, id, json_data):
-        self.tf_scope = 'agent'
-        self.graph = tf.Graph()
-        self.sess = tf.Session(graph=self.graph)
-
-        super().__init__(world, id, json_data)
-        self._build_graph(json_data)
-        self._init_normalizers()
-        return
-
-    def __del__(self):
-        self.sess.close()
-        return
-
-    def save_model(self, out_path):
-        with self.sess.as_default(), self.graph.as_default():
-            try:
-                save_path = self.saver.save(self.sess, out_path, write_meta_graph=False, write_state=False)
-                Logger.print2('Model saved to: ' + save_path)
-            except:
-                Logger.print2("Failed to save model to: " + save_path)
-        return
-
-    def load_model(self, in_path):
-        with self.sess.as_default(), self.graph.as_default():
-            self.saver.restore(self.sess, in_path)
-            self._load_normalizers()
-            Logger.print2('Model loaded from: ' + in_path)
-        return
-
-    def _get_output_path(self):
-        assert(self.output_dir != '')
-        file_path = self.output_dir + '/agent' + str(self.id) + '_model.ckpt'
-        return file_path
-
-    def _get_int_output_path(self):
-        assert(self.int_output_dir != '')
-        file_path = self.int_output_dir + ('/agent{:d}_models/agent{:d}_int_model_{:010d}.ckpt').format(self.id, self.id, self.iter)
-        return file_path
-
-    def _build_graph(self, json_data):
-        with self.sess.as_default(), self.graph.as_default():
-            with tf.variable_scope(self.tf_scope):
-                self._build_nets(json_data)
-                
-                with tf.variable_scope(self.SOLVER_SCOPE):
-                    self._build_losses(json_data)
-                    self._build_solvers(json_data)
-
-                self._initialize_vars()
-                self._build_saver()
-        return
-
-    def _init_normalizers(self):
-        with self.sess.as_default(), self.graph.as_default():
-            # update normalizers to sync the tensorflow tensors
-            self.s_norm.update()
-            self.g_norm.update()
-            self.a_norm.update()
-        return
-
-    @abstractmethod
-    def _build_nets(self, json_data):
-        pass
-
-    @abstractmethod
-    def _build_losses(self, json_data):
-        pass
-
-    @abstractmethod
-    def _build_solvers(self, json_data):
-        pass
-
-    def _tf_vars(self, scope=''):
-        with self.sess.as_default(), self.graph.as_default():
-            res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.tf_scope + '/' + scope)
-            assert len(res) > 0
-        return res
-
-    def _build_normalizers(self):
-        with self.sess.as_default(), self.graph.as_default(), tf.variable_scope(self.tf_scope):
-            with tf.variable_scope(self.RESOURCE_SCOPE):
-                self.s_norm = TFNormalizer(self.sess, 's_norm', self.get_state_size(), self.world.env.build_state_norm_groups(self.id))
-                state_offset = -self.world.env.build_state_offset(self.id)
-                print("state_offset=",state_offset)
-                state_scale = 1 / self.world.env.build_state_scale(self.id)
-                print("state_scale=",state_scale)
-                self.s_norm.set_mean_std(-self.world.env.build_state_offset(self.id), 
-                                         1 / self.world.env.build_state_scale(self.id))
-                
-                self.g_norm = TFNormalizer(self.sess, 'g_norm', self.get_goal_size(), self.world.env.build_goal_norm_groups(self.id))
-                self.g_norm.set_mean_std(-self.world.env.build_goal_offset(self.id), 
-                                         1 / self.world.env.build_goal_scale(self.id))
-
-                self.a_norm = TFNormalizer(self.sess, 'a_norm', self.get_action_size())
-                self.a_norm.set_mean_std(-self.world.env.build_action_offset(self.id), 
-                                         1 / self.world.env.build_action_scale(self.id))
-        return
-
-    def _load_normalizers(self):
-        self.s_norm.load()
-        self.g_norm.load()
-        self.a_norm.load()
-        return
-
-    def _update_normalizers(self):
-        with self.sess.as_default(), self.graph.as_default():
-            super()._update_normalizers()
-        return
-
-    def _initialize_vars(self):
-        self.sess.run(tf.global_variables_initializer())
-        return
-
-    def _build_saver(self):
-        vars = self._get_saver_vars()
-        self.saver = tf.train.Saver(vars, max_to_keep=0)
-        return
-
-    def _get_saver_vars(self):
-        with self.sess.as_default(), self.graph.as_default():
-            vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.tf_scope)
-            vars = [v for v in vars if '/' + self.SOLVER_SCOPE + '/' not in v.name]
-            #vars = [v for v in vars if '/target/' not in v.name]
-            assert len(vars) > 0
-        return vars
-    
-    def _weight_decay_loss(self, scope):
-        vars = self._tf_vars(scope)
-        vars_no_bias = [v for v in vars if 'bias' not in v.name]
-        loss = tf.add_n([tf.nn.l2_loss(v) for v in vars_no_bias])
-        return loss
-
-    def _train(self):
-        with self.sess.as_default(), self.graph.as_default():
-            super()._train()
-        return
\ No newline at end of file
+  RESOURCE_SCOPE = 'resource'
+  SOLVER_SCOPE = 'solvers'
+
+  def __init__(self, world, id, json_data):
+    self.tf_scope = 'agent'
+    self.graph = tf.Graph()
+    self.sess = tf.Session(graph=self.graph)
+
+    super().__init__(world, id, json_data)
+    self._build_graph(json_data)
+    self._init_normalizers()
+    return
+
+  def __del__(self):
+    self.sess.close()
+    return
+
+  def save_model(self, out_path):
+    with self.sess.as_default(), self.graph.as_default():
+      try:
+        save_path = self.saver.save(self.sess, out_path, write_meta_graph=False, write_state=False)
+        Logger.print2('Model saved to: ' + save_path)
+      except:
+        Logger.print2("Failed to save model to: " + save_path)
+    return
+
+  def load_model(self, in_path):
+    with self.sess.as_default(), self.graph.as_default():
+      self.saver.restore(self.sess, in_path)
+      self._load_normalizers()
+      Logger.print2('Model loaded from: ' + in_path)
+    return
+
+  def _get_output_path(self):
+    assert (self.output_dir != '')
+    file_path = self.output_dir + '/agent' + str(self.id) + '_model.ckpt'
+    return file_path
+
+  def _get_int_output_path(self):
+    assert (self.int_output_dir != '')
+    file_path = self.int_output_dir + (
+        '/agent{:d}_models/agent{:d}_int_model_{:010d}.ckpt').format(self.id, self.id, self.iter)
+    return file_path
+
+  def _build_graph(self, json_data):
+    with self.sess.as_default(), self.graph.as_default():
+      with tf.variable_scope(self.tf_scope):
+        self._build_nets(json_data)
+
+        with tf.variable_scope(self.SOLVER_SCOPE):
+          self._build_losses(json_data)
+          self._build_solvers(json_data)
+
+        self._initialize_vars()
+        self._build_saver()
+    return
+
+  def _init_normalizers(self):
+    with self.sess.as_default(), self.graph.as_default():
+      # update normalizers to sync the tensorflow tensors
+      self.s_norm.update()
+      self.g_norm.update()
+      self.a_norm.update()
+    return
+
+  @abstractmethod
+  def _build_nets(self, json_data):
+    pass
+
+  @abstractmethod
+  def _build_losses(self, json_data):
+    pass
+
+  @abstractmethod
+  def _build_solvers(self, json_data):
+    pass
+
+  def _tf_vars(self, scope=''):
+    with self.sess.as_default(), self.graph.as_default():
+      res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.tf_scope + '/' + scope)
+      assert len(res) > 0
+    return res
+
+  def _build_normalizers(self):
+    with self.sess.as_default(), self.graph.as_default(), tf.variable_scope(self.tf_scope):
+      with tf.variable_scope(self.RESOURCE_SCOPE):
+        self.s_norm = TFNormalizer(self.sess, 's_norm', self.get_state_size(),
+                                   self.world.env.build_state_norm_groups(self.id))
+        state_offset = -self.world.env.build_state_offset(self.id)
+        print("state_offset=", state_offset)
+        state_scale = 1 / self.world.env.build_state_scale(self.id)
+        print("state_scale=", state_scale)
+        self.s_norm.set_mean_std(-self.world.env.build_state_offset(self.id),
+                                 1 / self.world.env.build_state_scale(self.id))
+
+        self.g_norm = TFNormalizer(self.sess, 'g_norm', self.get_goal_size(),
+                                   self.world.env.build_goal_norm_groups(self.id))
+        self.g_norm.set_mean_std(-self.world.env.build_goal_offset(self.id),
+                                 1 / self.world.env.build_goal_scale(self.id))
+
+        self.a_norm = TFNormalizer(self.sess, 'a_norm', self.get_action_size())
+        self.a_norm.set_mean_std(-self.world.env.build_action_offset(self.id),
+                                 1 / self.world.env.build_action_scale(self.id))
+    return
+
+  def _load_normalizers(self):
+    self.s_norm.load()
+    self.g_norm.load()
+    self.a_norm.load()
+    return
+
+  def _update_normalizers(self):
+    with self.sess.as_default(), self.graph.as_default():
+      super()._update_normalizers()
+    return
+
+  def _initialize_vars(self):
+    self.sess.run(tf.global_variables_initializer())
+    return
+
+  def _build_saver(self):
+    vars = self._get_saver_vars()
+    self.saver = tf.train.Saver(vars, max_to_keep=0)
+    return
+
+  def _get_saver_vars(self):
+    with self.sess.as_default(), self.graph.as_default():
+      vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.tf_scope)
+      vars = [v for v in vars if '/' + self.SOLVER_SCOPE + '/' not in v.name]
+      #vars = [v for v in vars if '/target/' not in v.name]
+      assert len(vars) > 0
+    return vars
+
+  def _weight_decay_loss(self, scope):
+    vars = self._tf_vars(scope)
+    vars_no_bias = [v for v in vars if 'bias' not in v.name]
+    loss = tf.add_n([tf.nn.l2_loss(v) for v in vars_no_bias])
+    return loss
+
+  def _train(self):
+    with self.sess.as_default(), self.graph.as_default():
+      super()._train()
+    return
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_normalizer.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_normalizer.py
index 82f5745f0..907eec85b 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_normalizer.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_normalizer.py
@@ -3,65 +3,72 @@ import copy
 import tensorflow as tf
 from pybullet_envs.deep_mimic.learning.normalizer import Normalizer
 
+
 class TFNormalizer(Normalizer):
 
-    def __init__(self, sess, scope, size, groups_ids=None, eps=0.02, clip=np.inf):
-        self.sess = sess
-        self.scope = scope
-        super().__init__(size, groups_ids, eps, clip)
+  def __init__(self, sess, scope, size, groups_ids=None, eps=0.02, clip=np.inf):
+    self.sess = sess
+    self.scope = scope
+    super().__init__(size, groups_ids, eps, clip)
+
+    with tf.variable_scope(self.scope):
+      self._build_resource_tf()
+    return
+
+  # initialze count when loading saved values so that things don't change to quickly during updates
+  def load(self):
+    self.count = self.count_tf.eval()[0]
+    self.mean = self.mean_tf.eval()
+    self.std = self.std_tf.eval()
+    self.mean_sq = self.calc_mean_sq(self.mean, self.std)
+    return
+
+  def update(self):
+    super().update()
+    self._update_resource_tf()
+    return
 
-        with tf.variable_scope(self.scope):
-            self._build_resource_tf()
-        return
+  def set_mean_std(self, mean, std):
+    super().set_mean_std(mean, std)
+    self._update_resource_tf()
+    return
 
-    # initialze count when loading saved values so that things don't change to quickly during updates
-    def load(self):
-        self.count = self.count_tf.eval()[0]
-        self.mean = self.mean_tf.eval()
-        self.std = self.std_tf.eval()
-        self.mean_sq = self.calc_mean_sq(self.mean, self.std)
-        return
+  def normalize_tf(self, x):
+    norm_x = (x - self.mean_tf) / self.std_tf
+    norm_x = tf.clip_by_value(norm_x, -self.clip, self.clip)
+    return norm_x
 
-    def update(self):
-        super().update()
-        self._update_resource_tf()
-        return
+  def unnormalize_tf(self, norm_x):
+    x = norm_x * self.std_tf + self.mean_tf
+    return x
 
-    def set_mean_std(self, mean, std):
-        super().set_mean_std(mean, std)
-        self._update_resource_tf()
-        return
+  def _build_resource_tf(self):
+    self.count_tf = tf.get_variable(dtype=tf.int32,
+                                    name='count',
+                                    initializer=np.array([self.count], dtype=np.int32),
+                                    trainable=False)
+    self.mean_tf = tf.get_variable(dtype=tf.float32,
+                                   name='mean',
+                                   initializer=self.mean.astype(np.float32),
+                                   trainable=False)
+    self.std_tf = tf.get_variable(dtype=tf.float32,
+                                  name='std',
+                                  initializer=self.std.astype(np.float32),
+                                  trainable=False)
 
-    def normalize_tf(self, x):
-        norm_x = (x - self.mean_tf) / self.std_tf
-        norm_x = tf.clip_by_value(norm_x, -self.clip, self.clip)
-        return norm_x
+    self.count_ph = tf.get_variable(dtype=tf.int32, name='count_ph', shape=[1])
+    self.mean_ph = tf.get_variable(dtype=tf.float32, name='mean_ph', shape=self.mean.shape)
+    self.std_ph = tf.get_variable(dtype=tf.float32, name='std_ph', shape=self.std.shape)
 
-    def unnormalize_tf(self, norm_x):
-        x = norm_x * self.std_tf + self.mean_tf
-        return x
-    
-    def _build_resource_tf(self):
-        self.count_tf = tf.get_variable(dtype=tf.int32, name='count', initializer=np.array([self.count], dtype=np.int32), trainable=False)
-        self.mean_tf = tf.get_variable(dtype=tf.float32, name='mean', initializer=self.mean.astype(np.float32), trainable=False)
-        self.std_tf = tf.get_variable(dtype=tf.float32, name='std', initializer=self.std.astype(np.float32), trainable=False)
-        
-        self.count_ph = tf.get_variable(dtype=tf.int32, name='count_ph', shape=[1])
-        self.mean_ph = tf.get_variable(dtype=tf.float32, name='mean_ph', shape=self.mean.shape)
-        self.std_ph = tf.get_variable(dtype=tf.float32, name='std_ph', shape=self.std.shape)
-        
-        self._update_op = tf.group(
-            self.count_tf.assign(self.count_ph),
-            self.mean_tf.assign(self.mean_ph),
-            self.std_tf.assign(self.std_ph)
-        )
-        return
+    self._update_op = tf.group(self.count_tf.assign(self.count_ph),
+                               self.mean_tf.assign(self.mean_ph), self.std_tf.assign(self.std_ph))
+    return
 
-    def _update_resource_tf(self):
-        feed = {
-            self.count_ph: np.array([self.count], dtype=np.int32),
-            self.mean_ph: self.mean,
-            self.std_ph: self.std
-        }
-        self.sess.run(self._update_op, feed_dict=feed)
-        return
+  def _update_resource_tf(self):
+    feed = {
+        self.count_ph: np.array([self.count], dtype=np.int32),
+        self.mean_ph: self.mean,
+        self.std_ph: self.std
+    }
+    self.sess.run(self._update_op, feed_dict=feed)
+    return
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_util.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_util.py
index b0a315bc3..043d8dcd9 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_util.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/tf_util.py
@@ -4,101 +4,116 @@ import os
 
 xavier_initializer = tf.contrib.layers.xavier_initializer()
 
+
 def disable_gpu():
-    os.environ["CUDA_VISIBLE_DEVICES"] = '-1'
-    return
+  os.environ["CUDA_VISIBLE_DEVICES"] = '-1'
+  return
+
 
 def var_shape(x):
-    out = [k.value for k in x.get_shape()]
-    assert all(isinstance(a, int) for a in out), "shape function assumes that shape is fully known"
-    return out
+  out = [k.value for k in x.get_shape()]
+  assert all(isinstance(a, int) for a in out), "shape function assumes that shape is fully known"
+  return out
+
 
 def intprod(x):
-    return int(np.prod(x))
+  return int(np.prod(x))
+
 
 def numel(x):
-    n = intprod(var_shape(x))
-    return n
+  n = intprod(var_shape(x))
+  return n
+
 
 def flat_grad(loss, var_list, grad_ys=None):
-    grads = tf.gradients(loss, var_list, grad_ys)
-    return tf.concat([tf.reshape(grad, [numel(v)]) for (v, grad) in zip(var_list, grads)], axis=0)
-
-def fc_net(input, layers_sizes, activation, reuse=None, flatten=False): # build fully connected network
-    curr_tf = input
-    for i, size in enumerate(layers_sizes):
-        with tf.variable_scope(str(i), reuse=reuse):
-            curr_tf = tf.layers.dense(inputs=curr_tf,
-                                    units=size,
-                                    kernel_initializer=xavier_initializer,
-                                    activation = activation if i < len(layers_sizes)-1 else None)
-    if flatten:
-        assert layers_sizes[-1] == 1
-        curr_tf = tf.reshape(curr_tf, [-1])
-
-    return curr_tf
+  grads = tf.gradients(loss, var_list, grad_ys)
+  return tf.concat([tf.reshape(grad, [numel(v)]) for (v, grad) in zip(var_list, grads)], axis=0)
+
+
+def fc_net(input, layers_sizes, activation, reuse=None,
+           flatten=False):  # build fully connected network
+  curr_tf = input
+  for i, size in enumerate(layers_sizes):
+    with tf.variable_scope(str(i), reuse=reuse):
+      curr_tf = tf.layers.dense(inputs=curr_tf,
+                                units=size,
+                                kernel_initializer=xavier_initializer,
+                                activation=activation if i < len(layers_sizes) - 1 else None)
+  if flatten:
+    assert layers_sizes[-1] == 1
+    curr_tf = tf.reshape(curr_tf, [-1])
+
+  return curr_tf
+
 
 def copy(sess, src, dst):
-    assert len(src) == len(dst)
-    sess.run(list(map(lambda v: v[1].assign(v[0]), zip(src, dst))))
-    return
+  assert len(src) == len(dst)
+  sess.run(list(map(lambda v: v[1].assign(v[0]), zip(src, dst))))
+  return
+
 
 def flat_grad(loss, var_list):
-    grads = tf.gradients(loss, var_list)
-    return tf.concat(axis=0, values=[tf.reshape(grad, [numel(v)])
-        for (v, grad) in zip(var_list, grads)])
+  grads = tf.gradients(loss, var_list)
+  return tf.concat(axis=0,
+                   values=[tf.reshape(grad, [numel(v)]) for (v, grad) in zip(var_list, grads)])
 
 
 def calc_logp_gaussian(x_tf, mean_tf, std_tf):
-    dim = tf.to_float(tf.shape(x_tf)[-1])
+  dim = tf.to_float(tf.shape(x_tf)[-1])
+
+  if mean_tf is None:
+    diff_tf = x_tf
+  else:
+    diff_tf = x_tf - mean_tf
+
+  logp_tf = -0.5 * tf.reduce_sum(tf.square(diff_tf / std_tf), axis=-1)
+  logp_tf += -0.5 * dim * np.log(2 * np.pi) - tf.reduce_sum(tf.log(std_tf), axis=-1)
 
-    if mean_tf is None:
-        diff_tf = x_tf
-    else:
-        diff_tf = x_tf - mean_tf
+  return logp_tf
 
-    logp_tf = -0.5 * tf.reduce_sum(tf.square(diff_tf / std_tf), axis=-1)
-    logp_tf += -0.5 * dim * np.log(2 * np.pi) - tf.reduce_sum(tf.log(std_tf), axis=-1)
-    
-    return logp_tf
 
 def calc_bound_loss(x_tf, bound_min, bound_max):
-    # penalty for violating bounds
-    violation_min = tf.minimum(x_tf - bound_min, 0)
-    violation_max = tf.maximum(x_tf - bound_max, 0)
-    violation = tf.reduce_sum(tf.square(violation_min), axis=-1) + tf.reduce_sum(tf.square(violation_max), axis=-1)
-    loss = 0.5 * tf.reduce_mean(violation)
-    return loss
+  # penalty for violating bounds
+  violation_min = tf.minimum(x_tf - bound_min, 0)
+  violation_max = tf.maximum(x_tf - bound_max, 0)
+  violation = tf.reduce_sum(tf.square(violation_min), axis=-1) + tf.reduce_sum(
+      tf.square(violation_max), axis=-1)
+  loss = 0.5 * tf.reduce_mean(violation)
+  return loss
+
 
 class SetFromFlat(object):
-    def __init__(self, sess, var_list, dtype=tf.float32):
-        assigns = []
-        shapes = list(map(var_shape, var_list))
-        total_size = np.sum([intprod(shape) for shape in shapes])
 
-        self.sess = sess
-        self.theta = tf.placeholder(dtype,[total_size])
-        start=0
-        assigns = []
+  def __init__(self, sess, var_list, dtype=tf.float32):
+    assigns = []
+    shapes = list(map(var_shape, var_list))
+    total_size = np.sum([intprod(shape) for shape in shapes])
+
+    self.sess = sess
+    self.theta = tf.placeholder(dtype, [total_size])
+    start = 0
+    assigns = []
 
-        for (shape,v) in zip(shapes,var_list):
-            size = intprod(shape)
-            assigns.append(tf.assign(v, tf.reshape(self.theta[start:start+size],shape)))
-            start += size
+    for (shape, v) in zip(shapes, var_list):
+      size = intprod(shape)
+      assigns.append(tf.assign(v, tf.reshape(self.theta[start:start + size], shape)))
+      start += size
 
-        self.op = tf.group(*assigns)
+    self.op = tf.group(*assigns)
 
-        return
+    return
+
+  def __call__(self, theta):
+    self.sess.run(self.op, feed_dict={self.theta: theta})
+    return
 
-    def __call__(self, theta):
-        self.sess.run(self.op, feed_dict={self.theta:theta})
-        return
 
 class GetFlat(object):
-    def __init__(self, sess, var_list):
-        self.sess = sess
-        self.op = tf.concat(axis=0, values=[tf.reshape(v, [numel(v)]) for v in var_list])
-        return
 
-    def __call__(self):
-        return self.sess.run(self.op)
\ No newline at end of file
+  def __init__(self, sess, var_list):
+    self.sess = sess
+    self.op = tf.concat(axis=0, values=[tf.reshape(v, [numel(v)]) for v in var_list])
+    return
+
+  def __call__(self):
+    return self.sess.run(self.op)
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/camera.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/camera.py
index 329eb4b65..d98993b61 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/camera.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/camera.py
@@ -1,26 +1,28 @@
-import numpy as np 
+import numpy as np
 
 from quaternion import qrot, qinverse
 
 
-def normalize_screen_coordinates(X, w, h): 
-    assert X.shape[-1] == 2
-    # Normalize so that [0, w] is mapped to [-1, 1], while preserving the aspect ratio
-    return X/w*2 - [1, h/w]
+def normalize_screen_coordinates(X, w, h):
+  assert X.shape[-1] == 2
+  # Normalize so that [0, w] is mapped to [-1, 1], while preserving the aspect ratio
+  return X / w * 2 - [1, h / w]
+
 
 def image_coordinates(X, w, h):
-    assert X.shape[-1] == 2
-    # Reverse camera frame normalization
-    return (X + [1, h/w])*w/2
+  assert X.shape[-1] == 2
+  # Reverse camera frame normalization
+  return (X + [1, h / w]) * w / 2
+
 
 def world_to_camera(X, R, t):
-    Rt = qinverse(R)
-    Q = np.tile(Rt, (*X.shape[:-1], 1))
-    V = X - t
-    return qrot(Q, V)
-    
-def camera_to_world(X, R, t):
-    Q = np.tile(R, (*X.shape[:-1], 1))
-    V = X
-    return qrot(Q, V) + t
+  Rt = qinverse(R)
+  Q = np.tile(Rt, (*X.shape[:-1], 1))
+  V = X - t
+  return qrot(Q, V)
 
+
+def camera_to_world(X, R, t):
+  Q = np.tile(R, (*X.shape[:-1], 1))
+  V = X
+  return qrot(Q, V) + t
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/deepmimic_json_generator.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/deepmimic_json_generator.py
index 23525cd72..872bac78b 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/deepmimic_json_generator.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/deepmimic_json_generator.py
@@ -3,54 +3,63 @@ from h36m_dataset import Human36mDataset
 from camera import *
 
 import numpy as np
-# In[2]: 
+# In[2]:
 
 joint_info = {
-	'joint_name':['root', 'right_hip', 'right_knee', 'right_ankle', 'left_hip', 'left_knee', 'left_ankle', 'chest', 'neck', 'nose', 'eye', 'left_shoulder', 'left_elbow', 'left_wrist', 'right_shoulder', 'right_elbow', 'right_wrist'],
-    'father':[0, 0, 1, 2, 0, 4, 5, 0, 7, 8, 9, 8, 11, 12, 8, 14, 15],
-    'side':['middle', 'right', 'right', 'right', 'left', 'left', 'left', 'middle', 'middle', 'middle', 'middle', 'left', 'left', 'left', 'right', 'right', 'right'] 
+    'joint_name': [
+        'root', 'right_hip', 'right_knee', 'right_ankle', 'left_hip', 'left_knee', 'left_ankle',
+        'chest', 'neck', 'nose', 'eye', 'left_shoulder', 'left_elbow', 'left_wrist',
+        'right_shoulder', 'right_elbow', 'right_wrist'
+    ],
+    'father': [0, 0, 1, 2, 0, 4, 5, 0, 7, 8, 9, 8, 11, 12, 8, 14, 15],
+    'side': [
+        'middle', 'right', 'right', 'right', 'left', 'left', 'left', 'middle', 'middle', 'middle',
+        'middle', 'left', 'left', 'left', 'right', 'right', 'right'
+    ]
 }
 
 # In[3]:
 
+
 def init_fb_h36m_dataset(dataset_path):
-    dataset = Human36mDataset(dataset_path)
-    print('Preparing Facebook Human3.6M Dataset...')
-    for subject in dataset.subjects():
-        for action in dataset[subject].keys():
-            anim = dataset[subject][action]
-            positions_3d = []
-            for cam in anim['cameras']:
-                pos_3d = world_to_camera(anim['positions'], R=cam['orientation'], t=cam['translation'])
-                pos_3d[:, 1:] -= pos_3d[:, :1] # Remove global offset, but keep trajectory in first position
-                positions_3d.append(pos_3d)
-            anim['positions_3d'] = positions_3d
-    return dataset
+  dataset = Human36mDataset(dataset_path)
+  print('Preparing Facebook Human3.6M Dataset...')
+  for subject in dataset.subjects():
+    for action in dataset[subject].keys():
+      anim = dataset[subject][action]
+      positions_3d = []
+      for cam in anim['cameras']:
+        pos_3d = world_to_camera(anim['positions'], R=cam['orientation'], t=cam['translation'])
+        pos_3d[:, 1:] -= pos_3d[:, :
+                                1]  # Remove global offset, but keep trajectory in first position
+        positions_3d.append(pos_3d)
+      anim['positions_3d'] = positions_3d
+  return dataset
+
 
 def pose3D_from_fb_h36m(dataset, subject, action, shift):
-    pose_seq = dataset[subject][action]['positions_3d'][0].copy()
-    trajectory = pose_seq[:, :1]
-    pose_seq[:, 1:] += trajectory
-    # Invert camera transformation
-    cam = dataset.cameras()[subject][0]
-    pose_seq = camera_to_world(pose_seq, 
-                                   R=cam['orientation'], 
-                                   t=cam['translation'])
-    x = pose_seq[:,:,0:1]
-    y = pose_seq[:,:,1:2] * -1
-    z = pose_seq[:,:,2:3] 
-    pose_seq = np.concatenate((x,z,y),axis=2)
-    # plus shift
-    pose_seq += np.array([[shift for i in range(pose_seq.shape[1])] for j in range(pose_seq.shape[0])])
-    return pose_seq
+  pose_seq = dataset[subject][action]['positions_3d'][0].copy()
+  trajectory = pose_seq[:, :1]
+  pose_seq[:, 1:] += trajectory
+  # Invert camera transformation
+  cam = dataset.cameras()[subject][0]
+  pose_seq = camera_to_world(pose_seq, R=cam['orientation'], t=cam['translation'])
+  x = pose_seq[:, :, 0:1]
+  y = pose_seq[:, :, 1:2] * -1
+  z = pose_seq[:, :, 2:3]
+  pose_seq = np.concatenate((x, z, y), axis=2)
+  # plus shift
+  pose_seq += np.array([[shift for i in range(pose_seq.shape[1])] for j in range(pose_seq.shape[0])
+                       ])
+  return pose_seq
 
-def rot_seq_to_deepmimic_json(rot_seq, loop, json_path):
-    to_json = {"Loop": loop, "Frames":[]}
-    rot_seq = np.around(rot_seq, decimals=6)
-    to_json["Frames"] = rot_seq.tolist()
-    # In[14]:
-    to_file = json.dumps(to_json)
-    file = open(json_path,"w")
-    file.write(to_file)
-    file.close()
 
+def rot_seq_to_deepmimic_json(rot_seq, loop, json_path):
+  to_json = {"Loop": loop, "Frames": []}
+  rot_seq = np.around(rot_seq, decimals=6)
+  to_json["Frames"] = rot_seq.tolist()
+  # In[14]:
+  to_file = json.dumps(to_json)
+  file = open(json_path, "w")
+  file.write(to_file)
+  file.close()
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/h36m_dataset.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/h36m_dataset.py
index bc961fbf4..9dd95f902 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/h36m_dataset.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/h36m_dataset.py
@@ -3,11 +3,13 @@ import copy
 from skeleton import Skeleton
 from mocap_dataset import MocapDataset
 from camera import normalize_screen_coordinates, image_coordinates
-       
-h36m_skeleton = Skeleton(parents=[-1,  0,  1,  2,  3,  4,  0,  6,  7,  8,  9,  0, 11, 12, 13, 14, 12,
-       16, 17, 18, 19, 20, 19, 22, 12, 24, 25, 26, 27, 28, 27, 30],
-       joints_left=[6, 7, 8, 9, 10, 16, 17, 18, 19, 20, 21, 22, 23],
-       joints_right=[1, 2, 3, 4, 5, 24, 25, 26, 27, 28, 29, 30, 31])
+
+h36m_skeleton = Skeleton(parents=[
+    -1, 0, 1, 2, 3, 4, 0, 6, 7, 8, 9, 0, 11, 12, 13, 14, 12, 16, 17, 18, 19, 20, 19, 22, 12, 24,
+    25, 26, 27, 28, 27, 30
+],
+                         joints_left=[6, 7, 8, 9, 10, 16, 17, 18, 19, 20, 21, 22, 23],
+                         joints_right=[1, 2, 3, 4, 5, 24, 25, 26, 27, 28, 29, 30, 31])
 
 h36m_cameras_intrinsic_params = [
     {
@@ -18,7 +20,7 @@ h36m_cameras_intrinsic_params = [
         'tangential_distortion': [-0.0009756988729350269, -0.00142447161488235],
         'res_w': 1000,
         'res_h': 1002,
-        'azimuth': 70, # Only used for visualization
+        'azimuth': 70,  # Only used for visualization
     },
     {
         'id': '55011271',
@@ -28,7 +30,7 @@ h36m_cameras_intrinsic_params = [
         'tangential_distortion': [-0.0016190266469493508, -0.0027408944442868233],
         'res_w': 1000,
         'res_h': 1000,
-        'azimuth': -70, # Only used for visualization
+        'azimuth': -70,  # Only used for visualization
     },
     {
         'id': '58860488',
@@ -38,7 +40,7 @@ h36m_cameras_intrinsic_params = [
         'tangential_distortion': [0.0014843869721516967, -0.0007599993259645998],
         'res_w': 1000,
         'res_h': 1000,
-        'azimuth': 110, # Only used for visualization
+        'azimuth': 110,  # Only used for visualization
     },
     {
         'id': '60457274',
@@ -48,26 +50,34 @@ h36m_cameras_intrinsic_params = [
         'tangential_distortion': [-0.0005872055771760643, -0.0018133620033040643],
         'res_w': 1000,
         'res_h': 1002,
-        'azimuth': -110, # Only used for visualization
+        'azimuth': -110,  # Only used for visualization
     },
 ]
 
 h36m_cameras_extrinsic_params = {
     'S1': [
         {
-            'orientation': [0.1407056450843811, -0.1500701755285263, -0.755240797996521, 0.6223280429840088],
+            'orientation': [
+                0.1407056450843811, -0.1500701755285263, -0.755240797996521, 0.6223280429840088
+            ],
             'translation': [1841.1070556640625, 4955.28466796875, 1563.4454345703125],
         },
         {
-            'orientation': [0.6157187819480896, -0.764836311340332, -0.14833825826644897, 0.11794740706682205],
+            'orientation': [
+                0.6157187819480896, -0.764836311340332, -0.14833825826644897, 0.11794740706682205
+            ],
             'translation': [1761.278564453125, -5078.0068359375, 1606.2650146484375],
         },
         {
-            'orientation': [0.14651472866535187, -0.14647851884365082, 0.7653023600578308, -0.6094175577163696],
+            'orientation': [
+                0.14651472866535187, -0.14647851884365082, 0.7653023600578308, -0.6094175577163696
+            ],
             'translation': [-1846.7777099609375, 5215.04638671875, 1491.972412109375],
         },
         {
-            'orientation': [0.5834008455276489, -0.7853162288665771, 0.14548823237419128, -0.14749594032764435],
+            'orientation': [
+                0.5834008455276489, -0.7853162288665771, 0.14548823237419128, -0.14749594032764435
+            ],
             'translation': [-1794.7896728515625, -3722.698974609375, 1574.8927001953125],
         },
     ],
@@ -91,158 +101,206 @@ h36m_cameras_extrinsic_params = {
     ],
     'S5': [
         {
-            'orientation': [0.1467377245426178, -0.162370964884758, -0.7551892995834351, 0.6178938746452332],
+            'orientation': [
+                0.1467377245426178, -0.162370964884758, -0.7551892995834351, 0.6178938746452332
+            ],
             'translation': [2097.3916015625, 4880.94482421875, 1605.732421875],
         },
         {
-            'orientation': [0.6159758567810059, -0.7626792192459106, -0.15728192031383514, 0.1189815029501915],
+            'orientation': [
+                0.6159758567810059, -0.7626792192459106, -0.15728192031383514, 0.1189815029501915
+            ],
             'translation': [2031.7008056640625, -5167.93310546875, 1612.923095703125],
         },
         {
-            'orientation': [0.14291371405124664, -0.12907841801643372, 0.7678384780883789, -0.6110143065452576],
+            'orientation': [
+                0.14291371405124664, -0.12907841801643372, 0.7678384780883789, -0.6110143065452576
+            ],
             'translation': [-1620.5948486328125, 5171.65869140625, 1496.43701171875],
         },
         {
-            'orientation': [0.5920479893684387, -0.7814217805862427, 0.1274748593568802, -0.15036417543888092],
+            'orientation': [
+                0.5920479893684387, -0.7814217805862427, 0.1274748593568802, -0.15036417543888092
+            ],
             'translation': [-1637.1737060546875, -3867.3173828125, 1547.033203125],
         },
     ],
     'S6': [
         {
-            'orientation': [0.1337897777557373, -0.15692396461963654, -0.7571090459823608, 0.6198879480361938],
+            'orientation': [
+                0.1337897777557373, -0.15692396461963654, -0.7571090459823608, 0.6198879480361938
+            ],
             'translation': [1935.4517822265625, 4950.24560546875, 1618.0838623046875],
         },
         {
-            'orientation': [0.6147197484970093, -0.7628812789916992, -0.16174767911434174, 0.11819244921207428],
+            'orientation': [
+                0.6147197484970093, -0.7628812789916992, -0.16174767911434174, 0.11819244921207428
+            ],
             'translation': [1969.803955078125, -5128.73876953125, 1632.77880859375],
         },
         {
-            'orientation': [0.1529948115348816, -0.13529130816459656, 0.7646096348762512, -0.6112781167030334],
+            'orientation': [
+                0.1529948115348816, -0.13529130816459656, 0.7646096348762512, -0.6112781167030334
+            ],
             'translation': [-1769.596435546875, 5185.361328125, 1476.993408203125],
         },
         {
-            'orientation': [0.5916101336479187, -0.7804774045944214, 0.12832270562648773, -0.1561593860387802],
+            'orientation': [
+                0.5916101336479187, -0.7804774045944214, 0.12832270562648773, -0.1561593860387802
+            ],
             'translation': [-1721.668701171875, -3884.13134765625, 1540.4879150390625],
         },
     ],
     'S7': [
         {
-            'orientation': [0.1435241848230362, -0.1631336808204651, -0.7548328638076782, 0.6188824772834778],
+            'orientation': [
+                0.1435241848230362, -0.1631336808204651, -0.7548328638076782, 0.6188824772834778
+            ],
             'translation': [1974.512939453125, 4926.3544921875, 1597.8326416015625],
         },
         {
-            'orientation': [0.6141672730445862, -0.7638262510299683, -0.1596645563840866, 0.1177929937839508],
+            'orientation': [
+                0.6141672730445862, -0.7638262510299683, -0.1596645563840866, 0.1177929937839508
+            ],
             'translation': [1937.0584716796875, -5119.7900390625, 1631.5665283203125],
         },
         {
-            'orientation': [0.14550060033798218, -0.12874816358089447, 0.7660516500473022, -0.6127139329910278],
+            'orientation': [
+                0.14550060033798218, -0.12874816358089447, 0.7660516500473022, -0.6127139329910278
+            ],
             'translation': [-1741.8111572265625, 5208.24951171875, 1464.8245849609375],
         },
         {
-            'orientation': [0.5912848114967346, -0.7821764349937439, 0.12445473670959473, -0.15196487307548523],
+            'orientation': [
+                0.5912848114967346, -0.7821764349937439, 0.12445473670959473, -0.15196487307548523
+            ],
             'translation': [-1734.7105712890625, -3832.42138671875, 1548.5830078125],
         },
     ],
     'S8': [
         {
-            'orientation': [0.14110587537288666, -0.15589867532253265, -0.7561917304992676, 0.619644045829773],
+            'orientation': [
+                0.14110587537288666, -0.15589867532253265, -0.7561917304992676, 0.619644045829773
+            ],
             'translation': [2150.65185546875, 4896.1611328125, 1611.9046630859375],
         },
         {
-            'orientation': [0.6169601678848267, -0.7647668123245239, -0.14846350252628326, 0.11158157885074615],
+            'orientation': [
+                0.6169601678848267, -0.7647668123245239, -0.14846350252628326, 0.11158157885074615
+            ],
             'translation': [2219.965576171875, -5148.453125, 1613.0440673828125],
         },
         {
-            'orientation': [0.1471444070339203, -0.13377119600772858, 0.7670128345489502, -0.6100369691848755],
+            'orientation': [
+                0.1471444070339203, -0.13377119600772858, 0.7670128345489502, -0.6100369691848755
+            ],
             'translation': [-1571.2215576171875, 5137.0185546875, 1498.1761474609375],
         },
         {
-            'orientation': [0.5927824378013611, -0.7825870513916016, 0.12147816270589828, -0.14631995558738708],
+            'orientation': [
+                0.5927824378013611, -0.7825870513916016, 0.12147816270589828, -0.14631995558738708
+            ],
             'translation': [-1476.913330078125, -3896.7412109375, 1547.97216796875],
         },
     ],
     'S9': [
         {
-            'orientation': [0.15540587902069092, -0.15548215806484222, -0.7532095313072205, 0.6199594736099243],
+            'orientation': [
+                0.15540587902069092, -0.15548215806484222, -0.7532095313072205, 0.6199594736099243
+            ],
             'translation': [2044.45849609375, 4935.1171875, 1481.2275390625],
         },
         {
-            'orientation': [0.618784487247467, -0.7634735107421875, -0.14132238924503326, 0.11933968216180801],
+            'orientation': [
+                0.618784487247467, -0.7634735107421875, -0.14132238924503326, 0.11933968216180801
+            ],
             'translation': [1990.959716796875, -5123.810546875, 1568.8048095703125],
         },
         {
-            'orientation': [0.13357827067375183, -0.1367100477218628, 0.7689454555511475, -0.6100738644599915],
+            'orientation': [
+                0.13357827067375183, -0.1367100477218628, 0.7689454555511475, -0.6100738644599915
+            ],
             'translation': [-1670.9921875, 5211.98583984375, 1528.387939453125],
         },
         {
-            'orientation': [0.5879399180412292, -0.7823407053947449, 0.1427614390850067, -0.14794869720935822],
+            'orientation': [
+                0.5879399180412292, -0.7823407053947449, 0.1427614390850067, -0.14794869720935822
+            ],
             'translation': [-1696.04345703125, -3827.099853515625, 1591.4127197265625],
         },
     ],
     'S11': [
         {
-            'orientation': [0.15232472121715546, -0.15442320704460144, -0.7547563314437866, 0.6191070079803467],
+            'orientation': [
+                0.15232472121715546, -0.15442320704460144, -0.7547563314437866, 0.6191070079803467
+            ],
             'translation': [2098.440185546875, 4926.5546875, 1500.278564453125],
         },
         {
-            'orientation': [0.6189449429512024, -0.7600917220115662, -0.15300633013248444, 0.1255258321762085],
+            'orientation': [
+                0.6189449429512024, -0.7600917220115662, -0.15300633013248444, 0.1255258321762085
+            ],
             'translation': [2083.182373046875, -4912.1728515625, 1561.07861328125],
         },
         {
-            'orientation': [0.14943228662014008, -0.15650227665901184, 0.7681233882904053, -0.6026304364204407],
+            'orientation': [
+                0.14943228662014008, -0.15650227665901184, 0.7681233882904053, -0.6026304364204407
+            ],
             'translation': [-1609.8153076171875, 5177.3359375, 1537.896728515625],
         },
         {
-            'orientation': [0.5894251465797424, -0.7818877100944519, 0.13991211354732513, -0.14715361595153809],
+            'orientation': [
+                0.5894251465797424, -0.7818877100944519, 0.13991211354732513, -0.14715361595153809
+            ],
             'translation': [-1590.738037109375, -3854.1689453125, 1578.017578125],
         },
     ],
 }
 
+
 class Human36mDataset(MocapDataset):
-    def __init__(self, path, remove_static_joints=True):
-        super().__init__(fps=50, skeleton=h36m_skeleton)
-        
-        self._cameras = copy.deepcopy(h36m_cameras_extrinsic_params)
-        for cameras in self._cameras.values():
-            for i, cam in enumerate(cameras):
-                cam.update(h36m_cameras_intrinsic_params[i])
-                for k, v in cam.items():
-                    if k not in ['id', 'res_w', 'res_h']:
-                        cam[k] = np.array(v, dtype='float32')
-                
-                # Normalize camera frame
-                cam['center'] = normalize_screen_coordinates(cam['center'], w=cam['res_w'], h=cam['res_h']).astype('float32')
-                cam['focal_length'] = cam['focal_length']/cam['res_w']*2
-                if 'translation' in cam:
-                    cam['translation'] = cam['translation']/1000 # mm to meters
-                
-                # Add intrinsic parameters vector
-                cam['intrinsic'] = np.concatenate((cam['focal_length'],
-                                                   cam['center'],
-                                                   cam['radial_distortion'],
-                                                   cam['tangential_distortion']))
-        
-        # Load serialized dataset
-        data = np.load(path)['positions_3d'].item()
-        
-        self._data = {}
-        for subject, actions in data.items():
-            self._data[subject] = {}
-            for action_name, positions in actions.items():
-                self._data[subject][action_name] = {
-                    'positions': positions,
-                    'cameras': self._cameras[subject],
-                }
-                
-        if remove_static_joints:
-            # Bring the skeleton to 17 joints instead of the original 32
-            self.remove_joints([4, 5, 9, 10, 11, 16, 20, 21, 22, 23, 24, 28, 29, 30, 31])
-            
-            # Rewire shoulders to the correct parents
-            self._skeleton._parents[11] = 8
-            self._skeleton._parents[14] = 8
-            
-    def supports_semi_supervised(self):
-        return True
-    
\ No newline at end of file
+
+  def __init__(self, path, remove_static_joints=True):
+    super().__init__(fps=50, skeleton=h36m_skeleton)
+
+    self._cameras = copy.deepcopy(h36m_cameras_extrinsic_params)
+    for cameras in self._cameras.values():
+      for i, cam in enumerate(cameras):
+        cam.update(h36m_cameras_intrinsic_params[i])
+        for k, v in cam.items():
+          if k not in ['id', 'res_w', 'res_h']:
+            cam[k] = np.array(v, dtype='float32')
+
+        # Normalize camera frame
+        cam['center'] = normalize_screen_coordinates(cam['center'], w=cam['res_w'],
+                                                     h=cam['res_h']).astype('float32')
+        cam['focal_length'] = cam['focal_length'] / cam['res_w'] * 2
+        if 'translation' in cam:
+          cam['translation'] = cam['translation'] / 1000  # mm to meters
+
+        # Add intrinsic parameters vector
+        cam['intrinsic'] = np.concatenate((cam['focal_length'], cam['center'],
+                                           cam['radial_distortion'], cam['tangential_distortion']))
+
+    # Load serialized dataset
+    data = np.load(path)['positions_3d'].item()
+
+    self._data = {}
+    for subject, actions in data.items():
+      self._data[subject] = {}
+      for action_name, positions in actions.items():
+        self._data[subject][action_name] = {
+            'positions': positions,
+            'cameras': self._cameras[subject],
+        }
+
+    if remove_static_joints:
+      # Bring the skeleton to 17 joints instead of the original 32
+      self.remove_joints([4, 5, 9, 10, 11, 16, 20, 21, 22, 23, 24, 28, 29, 30, 31])
+
+      # Rewire shoulders to the correct parents
+      self._skeleton._parents[11] = 8
+      self._skeleton._parents[14] = 8
+
+  def supports_semi_supervised(self):
+    return True
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/humanoid.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/humanoid.py
index f55e3cadb..c9f89c4a1 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/humanoid.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/humanoid.py
@@ -1,166 +1,198 @@
-import os,  inspect
+import os, inspect
 import math
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 from pybullet_utils.bullet_client import BulletClient
 import pybullet_data
 
-jointTypes = ["JOINT_REVOLUTE","JOINT_PRISMATIC",
-              "JOINT_SPHERICAL","JOINT_PLANAR","JOINT_FIXED"]
-              
+jointTypes = [
+    "JOINT_REVOLUTE", "JOINT_PRISMATIC", "JOINT_SPHERICAL", "JOINT_PLANAR", "JOINT_FIXED"
+]
+
+
 class HumanoidPose(object):
+
   def __init__(self):
     pass
-  
+
   def Reset(self):
-    
-    self._basePos = [0,0,0]
-    self._baseLinVel = [0,0,0]
-    self._baseOrn = [0,0,0,1]
-    self._baseAngVel = [0,0,0]
-    
-    self._chestRot = [0,0,0,1]
-    self._chestVel = [0,0,0]
-    self._neckRot = [0,0,0,1]
-    self._neckVel = [0,0,0]
-    
-    self._rightHipRot = [0,0,0,1]
-    self._rightHipVel = [0,0,0]
+
+    self._basePos = [0, 0, 0]
+    self._baseLinVel = [0, 0, 0]
+    self._baseOrn = [0, 0, 0, 1]
+    self._baseAngVel = [0, 0, 0]
+
+    self._chestRot = [0, 0, 0, 1]
+    self._chestVel = [0, 0, 0]
+    self._neckRot = [0, 0, 0, 1]
+    self._neckVel = [0, 0, 0]
+
+    self._rightHipRot = [0, 0, 0, 1]
+    self._rightHipVel = [0, 0, 0]
     self._rightKneeRot = [0]
     self._rightKneeVel = [0]
-    self._rightAnkleRot = [0,0,0,1]
-    self._rightAnkleVel = [0,0,0]
-    
-    self._rightShoulderRot = [0,0,0,1]
-    self._rightShoulderVel = [0,0,0]
+    self._rightAnkleRot = [0, 0, 0, 1]
+    self._rightAnkleVel = [0, 0, 0]
+
+    self._rightShoulderRot = [0, 0, 0, 1]
+    self._rightShoulderVel = [0, 0, 0]
     self._rightElbowRot = [0]
     self._rightElbowVel = [0]
 
-    self._leftHipRot = [0,0,0,1]
-    self._leftHipVel = [0,0,0]
+    self._leftHipRot = [0, 0, 0, 1]
+    self._leftHipVel = [0, 0, 0]
     self._leftKneeRot = [0]
     self._leftKneeVel = [0]
-    self._leftAnkleRot = [0,0,0,1]
-    self._leftAnkleVel = [0,0,0]
-    
-    self._leftShoulderRot = [0,0,0,1]
-    self._leftShoulderVel = [0,0,0]
+    self._leftAnkleRot = [0, 0, 0, 1]
+    self._leftAnkleVel = [0, 0, 0]
+
+    self._leftShoulderRot = [0, 0, 0, 1]
+    self._leftShoulderVel = [0, 0, 0]
     self._leftElbowRot = [0]
     self._leftElbowVel = [0]
 
-  def ComputeLinVel(self,posStart, posEnd, deltaTime):
-    vel = [(posEnd[0]-posStart[0])/deltaTime,(posEnd[1]-posStart[1])/deltaTime,(posEnd[2]-posStart[2])/deltaTime]
+  def ComputeLinVel(self, posStart, posEnd, deltaTime):
+    vel = [(posEnd[0] - posStart[0]) / deltaTime, (posEnd[1] - posStart[1]) / deltaTime,
+           (posEnd[2] - posStart[2]) / deltaTime]
     return vel
-  
-  def ComputeAngVel(self,ornStart, ornEnd, deltaTime, bullet_client):
-    dorn = bullet_client.getDifferenceQuaternion(ornStart,ornEnd)
-    axis,angle = bullet_client.getAxisAngleFromQuaternion(dorn)
-    angVel = [(axis[0]*angle)/deltaTime,(axis[1]*angle)/deltaTime,(axis[2]*angle)/deltaTime]
+
+  def ComputeAngVel(self, ornStart, ornEnd, deltaTime, bullet_client):
+    dorn = bullet_client.getDifferenceQuaternion(ornStart, ornEnd)
+    axis, angle = bullet_client.getAxisAngleFromQuaternion(dorn)
+    angVel = [(axis[0] * angle) / deltaTime, (axis[1] * angle) / deltaTime,
+              (axis[2] * angle) / deltaTime]
     return angVel
-    
+
   def NormalizeQuaternion(self, orn):
-    length2 = orn[0]*orn[0]+orn[1]*orn[1]+orn[2]*orn[2]+orn[3]*orn[3]
-    if (length2>0):
+    length2 = orn[0] * orn[0] + orn[1] * orn[1] + orn[2] * orn[2] + orn[3] * orn[3]
+    if (length2 > 0):
       length = math.sqrt(length2)
     #print("Normalize? length=",length)
 
-    
   def PostProcessMotionData(self, frameData):
-    baseOrn1Start = [frameData[5],frameData[6], frameData[7],frameData[4]]
+    baseOrn1Start = [frameData[5], frameData[6], frameData[7], frameData[4]]
     self.NormalizeQuaternion(baseOrn1Start)
-    chestRotStart = [frameData[9],frameData[10],frameData[11],frameData[8]]
-    
-    neckRotStart = [frameData[13],frameData[14],frameData[15],frameData[12]]
-    rightHipRotStart = [frameData[17],frameData[18],frameData[19],frameData[16]]
-    rightAnkleRotStart = [frameData[22],frameData[23],frameData[24],frameData[21]]
-    rightShoulderRotStart = [frameData[26],frameData[27],frameData[28],frameData[25]]
-    leftHipRotStart = [frameData[31],frameData[32],frameData[33],frameData[30]]
-    leftAnkleRotStart = [frameData[36],frameData[37],frameData[38],frameData[35]]
-    leftShoulderRotStart = [frameData[40],frameData[41],frameData[42],frameData[39]]
-    
-    
-  def Slerp(self, frameFraction, frameData, frameDataNext,bullet_client ):
+    chestRotStart = [frameData[9], frameData[10], frameData[11], frameData[8]]
+
+    neckRotStart = [frameData[13], frameData[14], frameData[15], frameData[12]]
+    rightHipRotStart = [frameData[17], frameData[18], frameData[19], frameData[16]]
+    rightAnkleRotStart = [frameData[22], frameData[23], frameData[24], frameData[21]]
+    rightShoulderRotStart = [frameData[26], frameData[27], frameData[28], frameData[25]]
+    leftHipRotStart = [frameData[31], frameData[32], frameData[33], frameData[30]]
+    leftAnkleRotStart = [frameData[36], frameData[37], frameData[38], frameData[35]]
+    leftShoulderRotStart = [frameData[40], frameData[41], frameData[42], frameData[39]]
+
+  def Slerp(self, frameFraction, frameData, frameDataNext, bullet_client):
     keyFrameDuration = frameData[0]
-    basePos1Start = [frameData[1],frameData[2],frameData[3]]
-    basePos1End = [frameDataNext[1],frameDataNext[2],frameDataNext[3]]
-    self._basePos = [basePos1Start[0]+frameFraction*(basePos1End[0]-basePos1Start[0]), 
-      basePos1Start[1]+frameFraction*(basePos1End[1]-basePos1Start[1]), 
-      basePos1Start[2]+frameFraction*(basePos1End[2]-basePos1Start[2])]
-    self._baseLinVel = self.ComputeLinVel(basePos1Start,basePos1End, keyFrameDuration)
-    baseOrn1Start = [frameData[5],frameData[6], frameData[7],frameData[4]]
-    baseOrn1Next = [frameDataNext[5],frameDataNext[6], frameDataNext[7],frameDataNext[4]]
-    self._baseOrn = bullet_client.getQuaternionSlerp(baseOrn1Start,baseOrn1Next,frameFraction)
-    self._baseAngVel = self.ComputeAngVel(baseOrn1Start,baseOrn1Next, keyFrameDuration, bullet_client)
-    
+    basePos1Start = [frameData[1], frameData[2], frameData[3]]
+    basePos1End = [frameDataNext[1], frameDataNext[2], frameDataNext[3]]
+    self._basePos = [
+        basePos1Start[0] + frameFraction * (basePos1End[0] - basePos1Start[0]),
+        basePos1Start[1] + frameFraction * (basePos1End[1] - basePos1Start[1]),
+        basePos1Start[2] + frameFraction * (basePos1End[2] - basePos1Start[2])
+    ]
+    self._baseLinVel = self.ComputeLinVel(basePos1Start, basePos1End, keyFrameDuration)
+    baseOrn1Start = [frameData[5], frameData[6], frameData[7], frameData[4]]
+    baseOrn1Next = [frameDataNext[5], frameDataNext[6], frameDataNext[7], frameDataNext[4]]
+    self._baseOrn = bullet_client.getQuaternionSlerp(baseOrn1Start, baseOrn1Next, frameFraction)
+    self._baseAngVel = self.ComputeAngVel(baseOrn1Start, baseOrn1Next, keyFrameDuration,
+                                          bullet_client)
+
     ##pre-rotate to make z-up
     #y2zPos=[0,0,0.0]
     #y2zOrn = p.getQuaternionFromEuler([1.57,0,0])
     #basePos,baseOrn = p.multiplyTransforms(y2zPos, y2zOrn,basePos1,baseOrn1)
 
-    chestRotStart = [frameData[9],frameData[10],frameData[11],frameData[8]]
-    chestRotEnd = [frameDataNext[9],frameDataNext[10],frameDataNext[11],frameDataNext[8]]
-    self._chestRot = bullet_client.getQuaternionSlerp(chestRotStart,chestRotEnd,frameFraction)
-    self._chestVel = self.ComputeAngVel(chestRotStart,chestRotEnd,keyFrameDuration,bullet_client)
-    
-    neckRotStart = [frameData[13],frameData[14],frameData[15],frameData[12]]
-    neckRotEnd= [frameDataNext[13],frameDataNext[14],frameDataNext[15],frameDataNext[12]]
-    self._neckRot =  bullet_client.getQuaternionSlerp(neckRotStart,neckRotEnd,frameFraction)
-    self._neckVel = self.ComputeAngVel(neckRotStart,neckRotEnd,keyFrameDuration,bullet_client)
-    
-    rightHipRotStart = [frameData[17],frameData[18],frameData[19],frameData[16]]
-    rightHipRotEnd = [frameDataNext[17],frameDataNext[18],frameDataNext[19],frameDataNext[16]]
-    self._rightHipRot = bullet_client.getQuaternionSlerp(rightHipRotStart,rightHipRotEnd,frameFraction)
-    self._rightHipVel = self.ComputeAngVel(rightHipRotStart,rightHipRotEnd,keyFrameDuration,bullet_client)
-    
+    chestRotStart = [frameData[9], frameData[10], frameData[11], frameData[8]]
+    chestRotEnd = [frameDataNext[9], frameDataNext[10], frameDataNext[11], frameDataNext[8]]
+    self._chestRot = bullet_client.getQuaternionSlerp(chestRotStart, chestRotEnd, frameFraction)
+    self._chestVel = self.ComputeAngVel(chestRotStart, chestRotEnd, keyFrameDuration,
+                                        bullet_client)
+
+    neckRotStart = [frameData[13], frameData[14], frameData[15], frameData[12]]
+    neckRotEnd = [frameDataNext[13], frameDataNext[14], frameDataNext[15], frameDataNext[12]]
+    self._neckRot = bullet_client.getQuaternionSlerp(neckRotStart, neckRotEnd, frameFraction)
+    self._neckVel = self.ComputeAngVel(neckRotStart, neckRotEnd, keyFrameDuration, bullet_client)
+
+    rightHipRotStart = [frameData[17], frameData[18], frameData[19], frameData[16]]
+    rightHipRotEnd = [frameDataNext[17], frameDataNext[18], frameDataNext[19], frameDataNext[16]]
+    self._rightHipRot = bullet_client.getQuaternionSlerp(rightHipRotStart, rightHipRotEnd,
+                                                         frameFraction)
+    self._rightHipVel = self.ComputeAngVel(rightHipRotStart, rightHipRotEnd, keyFrameDuration,
+                                           bullet_client)
+
     rightKneeRotStart = [frameData[20]]
     rightKneeRotEnd = [frameDataNext[20]]
-    self._rightKneeRot = [rightKneeRotStart[0]+frameFraction*(rightKneeRotEnd[0]-rightKneeRotStart[0])]
-    self._rightKneeVel = [(rightKneeRotEnd[0]-rightKneeRotStart[0])/keyFrameDuration]
-    
-    rightAnkleRotStart = [frameData[22],frameData[23],frameData[24],frameData[21]]
-    rightAnkleRotEnd = [frameDataNext[22],frameDataNext[23],frameDataNext[24],frameDataNext[21]]
-    self._rightAnkleRot =  bullet_client.getQuaternionSlerp(rightAnkleRotStart,rightAnkleRotEnd,frameFraction)
-    self._rightAnkleVel = self.ComputeAngVel(rightAnkleRotStart,rightAnkleRotEnd,keyFrameDuration,bullet_client)
-      
-    rightShoulderRotStart = [frameData[26],frameData[27],frameData[28],frameData[25]]
-    rightShoulderRotEnd = [frameDataNext[26],frameDataNext[27],frameDataNext[28],frameDataNext[25]]
-    self._rightShoulderRot = bullet_client.getQuaternionSlerp(rightShoulderRotStart,rightShoulderRotEnd,frameFraction)
-    self._rightShoulderVel = self.ComputeAngVel(rightShoulderRotStart,rightShoulderRotEnd, keyFrameDuration,bullet_client)
-    
+    self._rightKneeRot = [
+        rightKneeRotStart[0] + frameFraction * (rightKneeRotEnd[0] - rightKneeRotStart[0])
+    ]
+    self._rightKneeVel = [(rightKneeRotEnd[0] - rightKneeRotStart[0]) / keyFrameDuration]
+
+    rightAnkleRotStart = [frameData[22], frameData[23], frameData[24], frameData[21]]
+    rightAnkleRotEnd = [frameDataNext[22], frameDataNext[23], frameDataNext[24], frameDataNext[21]]
+    self._rightAnkleRot = bullet_client.getQuaternionSlerp(rightAnkleRotStart, rightAnkleRotEnd,
+                                                           frameFraction)
+    self._rightAnkleVel = self.ComputeAngVel(rightAnkleRotStart, rightAnkleRotEnd,
+                                             keyFrameDuration, bullet_client)
+
+    rightShoulderRotStart = [frameData[26], frameData[27], frameData[28], frameData[25]]
+    rightShoulderRotEnd = [
+        frameDataNext[26], frameDataNext[27], frameDataNext[28], frameDataNext[25]
+    ]
+    self._rightShoulderRot = bullet_client.getQuaternionSlerp(rightShoulderRotStart,
+                                                              rightShoulderRotEnd, frameFraction)
+    self._rightShoulderVel = self.ComputeAngVel(rightShoulderRotStart, rightShoulderRotEnd,
+                                                keyFrameDuration, bullet_client)
+
     rightElbowRotStart = [frameData[29]]
     rightElbowRotEnd = [frameDataNext[29]]
-    self._rightElbowRot = [rightElbowRotStart[0]+frameFraction*(rightElbowRotEnd[0]-rightElbowRotStart[0])]
-    self._rightElbowVel = [(rightElbowRotEnd[0]-rightElbowRotStart[0])/keyFrameDuration]
-    
-    leftHipRotStart = [frameData[31],frameData[32],frameData[33],frameData[30]]
-    leftHipRotEnd = [frameDataNext[31],frameDataNext[32],frameDataNext[33],frameDataNext[30]]
-    self._leftHipRot = bullet_client.getQuaternionSlerp(leftHipRotStart,leftHipRotEnd,frameFraction)
-    self._leftHipVel = self.ComputeAngVel(leftHipRotStart, leftHipRotEnd,keyFrameDuration,bullet_client)
-    
+    self._rightElbowRot = [
+        rightElbowRotStart[0] + frameFraction * (rightElbowRotEnd[0] - rightElbowRotStart[0])
+    ]
+    self._rightElbowVel = [(rightElbowRotEnd[0] - rightElbowRotStart[0]) / keyFrameDuration]
+
+    leftHipRotStart = [frameData[31], frameData[32], frameData[33], frameData[30]]
+    leftHipRotEnd = [frameDataNext[31], frameDataNext[32], frameDataNext[33], frameDataNext[30]]
+    self._leftHipRot = bullet_client.getQuaternionSlerp(leftHipRotStart, leftHipRotEnd,
+                                                        frameFraction)
+    self._leftHipVel = self.ComputeAngVel(leftHipRotStart, leftHipRotEnd, keyFrameDuration,
+                                          bullet_client)
+
     leftKneeRotStart = [frameData[34]]
     leftKneeRotEnd = [frameDataNext[34]]
-    self._leftKneeRot = [leftKneeRotStart[0] +frameFraction*(leftKneeRotEnd[0]-leftKneeRotStart[0]) ]
-    self._leftKneeVel = [(leftKneeRotEnd[0]-leftKneeRotStart[0])/keyFrameDuration]
-    
-    leftAnkleRotStart = [frameData[36],frameData[37],frameData[38],frameData[35]]
-    leftAnkleRotEnd = [frameDataNext[36],frameDataNext[37],frameDataNext[38],frameDataNext[35]]
-    self._leftAnkleRot = bullet_client.getQuaternionSlerp(leftAnkleRotStart,leftAnkleRotEnd,frameFraction)
-    self._leftAnkleVel = self.ComputeAngVel(leftAnkleRotStart,leftAnkleRotEnd,keyFrameDuration,bullet_client)
-
-    leftShoulderRotStart = [frameData[40],frameData[41],frameData[42],frameData[39]]
-    leftShoulderRotEnd = [frameDataNext[40],frameDataNext[41],frameDataNext[42],frameDataNext[39]]
-    self._leftShoulderRot = bullet_client.getQuaternionSlerp(leftShoulderRotStart,leftShoulderRotEnd,frameFraction)
-    self._leftShoulderVel = self.ComputeAngVel(leftShoulderRotStart,leftShoulderRotEnd,keyFrameDuration,bullet_client)
+    self._leftKneeRot = [
+        leftKneeRotStart[0] + frameFraction * (leftKneeRotEnd[0] - leftKneeRotStart[0])
+    ]
+    self._leftKneeVel = [(leftKneeRotEnd[0] - leftKneeRotStart[0]) / keyFrameDuration]
+
+    leftAnkleRotStart = [frameData[36], frameData[37], frameData[38], frameData[35]]
+    leftAnkleRotEnd = [frameDataNext[36], frameDataNext[37], frameDataNext[38], frameDataNext[35]]
+    self._leftAnkleRot = bullet_client.getQuaternionSlerp(leftAnkleRotStart, leftAnkleRotEnd,
+                                                          frameFraction)
+    self._leftAnkleVel = self.ComputeAngVel(leftAnkleRotStart, leftAnkleRotEnd, keyFrameDuration,
+                                            bullet_client)
+
+    leftShoulderRotStart = [frameData[40], frameData[41], frameData[42], frameData[39]]
+    leftShoulderRotEnd = [
+        frameDataNext[40], frameDataNext[41], frameDataNext[42], frameDataNext[39]
+    ]
+    self._leftShoulderRot = bullet_client.getQuaternionSlerp(leftShoulderRotStart,
+                                                             leftShoulderRotEnd, frameFraction)
+    self._leftShoulderVel = self.ComputeAngVel(leftShoulderRotStart, leftShoulderRotEnd,
+                                               keyFrameDuration, bullet_client)
 
     leftElbowRotStart = [frameData[43]]
     leftElbowRotEnd = [frameDataNext[43]]
-    self._leftElbowRot = [leftElbowRotStart[0]+frameFraction*(leftElbowRotEnd[0]-leftElbowRotStart[0])]
-    self._leftElbowVel = [(leftElbowRotEnd[0]-leftElbowRotStart[0])/keyFrameDuration]
-        
+    self._leftElbowRot = [
+        leftElbowRotStart[0] + frameFraction * (leftElbowRotEnd[0] - leftElbowRotStart[0])
+    ]
+    self._leftElbowVel = [(leftElbowRotEnd[0] - leftElbowRotStart[0]) / keyFrameDuration]
+
 
 class Humanoid(object):
+
   def __init__(self, pybullet_client, motion_data, baseShift):
     """Constructs a humanoid and reset it to the initial states.
     Args:
@@ -169,42 +201,46 @@ class Humanoid(object):
     """
     self._baseShift = baseShift
     self._pybullet_client = pybullet_client
-    
-    self.kin_client = BulletClient(pybullet_client.DIRECT)# use SHARED_MEMORY for visual debugging, start a GUI physics server first
+
+    self.kin_client = BulletClient(
+        pybullet_client.DIRECT
+    )  # use SHARED_MEMORY for visual debugging, start a GUI physics server first
     self.kin_client.resetSimulation()
     self.kin_client.setAdditionalSearchPath(pybullet_data.getDataPath())
-    self.kin_client.configureDebugVisualizer(self.kin_client.COV_ENABLE_Y_AXIS_UP,1)
-    self.kin_client.setGravity(0,-9.8,0)
-    
+    self.kin_client.configureDebugVisualizer(self.kin_client.COV_ENABLE_Y_AXIS_UP, 1)
+    self.kin_client.setGravity(0, -9.8, 0)
+
     self._motion_data = motion_data
     print("LOADING humanoid!")
-    self._humanoid = self._pybullet_client.loadURDF(
-      "humanoid/humanoid.urdf", [0,0.9,0],globalScaling=0.25, useFixedBase=False)
-      
-    self._kinematicHumanoid = self.kin_client.loadURDF(
-      "humanoid/humanoid.urdf", [0,0.9,0],globalScaling=0.25, useFixedBase=False)
-      
-      
+    self._humanoid = self._pybullet_client.loadURDF("humanoid/humanoid.urdf", [0, 0.9, 0],
+                                                    globalScaling=0.25,
+                                                    useFixedBase=False)
+
+    self._kinematicHumanoid = self.kin_client.loadURDF("humanoid/humanoid.urdf", [0, 0.9, 0],
+                                                       globalScaling=0.25,
+                                                       useFixedBase=False)
+
     #print("human #joints=", self._pybullet_client.getNumJoints(self._humanoid))
     pose = HumanoidPose()
-    
-    for i in range (self._motion_data.NumFrames()-1):
+
+    for i in range(self._motion_data.NumFrames() - 1):
       frameData = self._motion_data._motion_data['Frames'][i]
       pose.PostProcessMotionData(frameData)
-    
-    self._pybullet_client.resetBasePositionAndOrientation(self._humanoid,self._baseShift,[0,0,0,1])
+
+    self._pybullet_client.resetBasePositionAndOrientation(self._humanoid, self._baseShift,
+                                                          [0, 0, 0, 1])
     self._pybullet_client.changeDynamics(self._humanoid, -1, linearDamping=0, angularDamping=0)
-    for j in range (self._pybullet_client.getNumJoints(self._humanoid)):
-      ji = self._pybullet_client.getJointInfo(self._humanoid,j)
+    for j in range(self._pybullet_client.getNumJoints(self._humanoid)):
+      ji = self._pybullet_client.getJointInfo(self._humanoid, j)
       self._pybullet_client.changeDynamics(self._humanoid, j, linearDamping=0, angularDamping=0)
-      self._pybullet_client.changeVisualShape(self._humanoid, j , rgbaColor=[1,1,1,1])
+      self._pybullet_client.changeVisualShape(self._humanoid, j, rgbaColor=[1, 1, 1, 1])
       #print("joint[",j,"].type=",jointTypes[ji[2]])
       #print("joint[",j,"].name=",ji[1])
-    
+
     self._initial_state = self._pybullet_client.saveState()
-    self._allowed_body_parts=[11,14]
+    self._allowed_body_parts = [11, 14]
     self.Reset()
-    
+
   def Reset(self):
     self._pybullet_client.restoreState(self._initial_state)
     self.SetSimTime(0)
@@ -220,7 +256,7 @@ class Humanoid(object):
     self.ApplyPose(pose, True, True, self._humanoid, self._pybullet_client)
 
   def CalcCycleCount(self, simTime, cycleTime):
-    phases = simTime / cycleTime;
+    phases = simTime / cycleTime
     count = math.floor(phases)
     loop = True
     #count = (loop) ? count : cMathUtil::Clamp(count, 0, 1);
@@ -230,67 +266,67 @@ class Humanoid(object):
     self._simTime = t
     #print("SetTimeTime time =",t)
     keyFrameDuration = self._motion_data.KeyFrameDuraction()
-    cycleTime = keyFrameDuration*(self._motion_data.NumFrames()-1)
+    cycleTime = keyFrameDuration * (self._motion_data.NumFrames() - 1)
     #print("self._motion_data.NumFrames()=",self._motion_data.NumFrames())
     #print("cycleTime=",cycleTime)
     cycles = self.CalcCycleCount(t, cycleTime)
     #print("cycles=",cycles)
-    frameTime = t - cycles*cycleTime
-    if (frameTime<0):
+    frameTime = t - cycles * cycleTime
+    if (frameTime < 0):
       frameTime += cycleTime
-    
-    #print("keyFrameDuration=",keyFrameDuration)  
+
+    #print("keyFrameDuration=",keyFrameDuration)
     #print("frameTime=",frameTime)
-    self._frame = int(frameTime/keyFrameDuration)
+    self._frame = int(frameTime / keyFrameDuration)
     #print("self._frame=",self._frame)
-    
-    self._frameNext = self._frame+1
-    if (self._frameNext >=  self._motion_data.NumFrames()):
+
+    self._frameNext = self._frame + 1
+    if (self._frameNext >= self._motion_data.NumFrames()):
       self._frameNext = self._frame
 
-    self._frameFraction = (frameTime - self._frame*keyFrameDuration)/(keyFrameDuration)
+    self._frameFraction = (frameTime - self._frame * keyFrameDuration) / (keyFrameDuration)
     #print("self._frameFraction=",self._frameFraction)
 
   def Terminates(self):
     #check if any non-allowed body part hits the ground
-    terminates=False
+    terminates = False
     pts = self._pybullet_client.getContactPoints()
     for p in pts:
       part = -1
-      if (p[1]==self._humanoid):
-        part=p[3]
-      if (p[2]==self._humanoid):
-        part=p[4]
-      if (part >=0 and part not in self._allowed_body_parts):
-        terminates=True
-      
+      if (p[1] == self._humanoid):
+        part = p[3]
+      if (p[2] == self._humanoid):
+        part = p[4]
+      if (part >= 0 and part not in self._allowed_body_parts):
+        terminates = True
+
     return terminates
-        
+
   def BuildHeadingTrans(self, rootOrn):
     #align root transform 'forward' with world-space x axis
     eul = self._pybullet_client.getEulerFromQuaternion(rootOrn)
-    refDir = [1,0,0]
+    refDir = [1, 0, 0]
     rotVec = self._pybullet_client.rotateVector(rootOrn, refDir)
     heading = math.atan2(-rotVec[2], rotVec[0])
-    heading2=eul[1]
+    heading2 = eul[1]
     #print("heading=",heading)
-    headingOrn = self._pybullet_client.getQuaternionFromAxisAngle([0,1,0],-heading)
+    headingOrn = self._pybullet_client.getQuaternionFromAxisAngle([0, 1, 0], -heading)
     return headingOrn
 
   def GetPhase(self):
     keyFrameDuration = self._motion_data.KeyFrameDuraction()
-    cycleTime = keyFrameDuration*(self._motion_data.NumFrames()-1)
+    cycleTime = keyFrameDuration * (self._motion_data.NumFrames() - 1)
     phase = self._simTime / cycleTime
-    phase = math.fmod(phase,1.0)
-    if (phase<0):
+    phase = math.fmod(phase, 1.0)
+    if (phase < 0):
       phase += 1
     return phase
 
   def BuildOriginTrans(self):
-    rootPos,rootOrn = self._pybullet_client.getBasePositionAndOrientation(self._humanoid)
-    
+    rootPos, rootOrn = self._pybullet_client.getBasePositionAndOrientation(self._humanoid)
+
     #print("rootPos=",rootPos, " rootOrn=",rootOrn)
-    invRootPos=[-rootPos[0], 0, -rootPos[2]]
+    invRootPos = [-rootPos[0], 0, -rootPos[2]]
     #invOrigTransPos, invOrigTransOrn = self._pybullet_client.invertTransform(rootPos,rootOrn)
     headingOrn = self.BuildHeadingTrans(rootOrn)
     #print("headingOrn=",headingOrn)
@@ -298,166 +334,226 @@ class Humanoid(object):
     #print("headingMat=",headingMat)
     #dummy, rootOrnWithoutHeading = self._pybullet_client.multiplyTransforms([0,0,0],headingOrn, [0,0,0], rootOrn)
     #dummy, invOrigTransOrn = self._pybullet_client.multiplyTransforms([0,0,0],rootOrnWithoutHeading, invOrigTransPos, invOrigTransOrn)
-    
-    invOrigTransPos, invOrigTransOrn = self._pybullet_client.multiplyTransforms( [0,0,0],headingOrn, invRootPos,[0,0,0,1])
+
+    invOrigTransPos, invOrigTransOrn = self._pybullet_client.multiplyTransforms([0, 0, 0],
+                                                                                headingOrn,
+                                                                                invRootPos,
+                                                                                [0, 0, 0, 1])
     #print("invOrigTransPos=",invOrigTransPos)
     #print("invOrigTransOrn=",invOrigTransOrn)
     invOrigTransMat = self._pybullet_client.getMatrixFromQuaternion(invOrigTransOrn)
     #print("invOrigTransMat =",invOrigTransMat )
     return invOrigTransPos, invOrigTransOrn
-    
+
   def InitializePoseFromMotionData(self):
     frameData = self._motion_data._motion_data['Frames'][self._frame]
     frameDataNext = self._motion_data._motion_data['Frames'][self._frameNext]
     pose = HumanoidPose()
     pose.Slerp(self._frameFraction, frameData, frameDataNext, self._pybullet_client)
     return pose
-    
 
-  
-    
   def ApplyAction(self, action):
     #turn action into pose
     pose = HumanoidPose()
     pose.Reset()
-    index=0
+    index = 0
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    pose._chestRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle)
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    pose._chestRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle)
     #print("pose._chestRot=",pose._chestRot)
 
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    pose._neckRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle)
-    
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    pose._neckRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle)
+
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    pose._rightHipRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle)
-    
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    pose._rightHipRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle)
+
     angle = action[index]
-    index+=1
+    index += 1
     pose._rightKneeRot = [angle]
-        
+
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    pose._rightAnkleRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle)
-    
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    pose._rightAnkleRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle)
+
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    pose._rightShoulderRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle)
-    
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    pose._rightShoulderRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle)
+
     angle = action[index]
-    index+=1
+    index += 1
     pose._rightElbowRot = [angle]
-    
+
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    pose._leftHipRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle)
-    
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    pose._leftHipRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle)
+
     angle = action[index]
-    index+=1
+    index += 1
     pose._leftKneeRot = [angle]
-    
-    
+
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    pose._leftAnkleRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle)
-    
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    pose._leftAnkleRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle)
+
     angle = action[index]
-    axis = [action[index+1],action[index+2],action[index+3]]
-    index+=4
-    pose._leftShoulderRot = self._pybullet_client.getQuaternionFromAxisAngle(axis,angle)
-        
+    axis = [action[index + 1], action[index + 2], action[index + 3]]
+    index += 4
+    pose._leftShoulderRot = self._pybullet_client.getQuaternionFromAxisAngle(axis, angle)
+
     angle = action[index]
-    index+=1
+    index += 1
     pose._leftElbowRot = [angle]
-    
-    
+
     #print("index=",index)
-    
+
     initializeBase = False
     initializeVelocities = False
-    self.ApplyPose(pose, initializeBase, initializeVelocities, self._humanoid, self._pybullet_client)
-    
-    
-  def ApplyPose(self, pose, initializeBase, initializeVelocities, humanoid,bc):
+    self.ApplyPose(pose, initializeBase, initializeVelocities, self._humanoid,
+                   self._pybullet_client)
+
+  def ApplyPose(self, pose, initializeBase, initializeVelocities, humanoid, bc):
     #todo: get tunable parametes from a json file or from URDF (kd, maxForce)
     if (initializeBase):
-      bc.changeVisualShape(humanoid, 2 , rgbaColor=[1,0,0,1])
-      basePos=[pose._basePos[0]+self._baseShift[0],pose._basePos[1]+self._baseShift[1],pose._basePos[2]+self._baseShift[2]]
-      
-      bc.resetBasePositionAndOrientation(humanoid,
-        basePos, pose._baseOrn)
+      bc.changeVisualShape(humanoid, 2, rgbaColor=[1, 0, 0, 1])
+      basePos = [
+          pose._basePos[0] + self._baseShift[0], pose._basePos[1] + self._baseShift[1],
+          pose._basePos[2] + self._baseShift[2]
+      ]
+
+      bc.resetBasePositionAndOrientation(humanoid, basePos, pose._baseOrn)
       if initializeVelocities:
         bc.resetBaseVelocity(humanoid, pose._baseLinVel, pose._baseAngVel)
         #print("resetBaseVelocity=",pose._baseLinVel)
     else:
-      bc.changeVisualShape(humanoid, 2 , rgbaColor=[1,1,1,1])
-    
-    
-    
-    kp=0.03
-    chest=1
-    neck=2
-    rightShoulder=3
-    rightElbow=4
-    leftShoulder=6
+      bc.changeVisualShape(humanoid, 2, rgbaColor=[1, 1, 1, 1])
+
+    kp = 0.03
+    chest = 1
+    neck = 2
+    rightShoulder = 3
+    rightElbow = 4
+    leftShoulder = 6
     leftElbow = 7
     rightHip = 9
-    rightKnee=10
-    rightAnkle=11
+    rightKnee = 10
+    rightAnkle = 11
     leftHip = 12
-    leftKnee=13
-    leftAnkle=14
+    leftKnee = 13
+    leftAnkle = 14
     controlMode = bc.POSITION_CONTROL
-    
+
     if (initializeBase):
       if initializeVelocities:
-        bc.resetJointStateMultiDof(humanoid,chest,pose._chestRot, pose._chestVel)
-        bc.resetJointStateMultiDof(humanoid,neck,pose._neckRot, pose._neckVel)
-        bc.resetJointStateMultiDof(humanoid,rightHip,pose._rightHipRot, pose._rightHipVel)
-        bc.resetJointStateMultiDof(humanoid,rightKnee,pose._rightKneeRot, pose._rightKneeVel)
-        bc.resetJointStateMultiDof(humanoid,rightAnkle,pose._rightAnkleRot, pose._rightAnkleVel)
-        bc.resetJointStateMultiDof(humanoid,rightShoulder,pose._rightShoulderRot, pose._rightShoulderVel)
-        bc.resetJointStateMultiDof(humanoid,rightElbow, pose._rightElbowRot, pose._rightElbowVel)
-        bc.resetJointStateMultiDof(humanoid,leftHip, pose._leftHipRot, pose._leftHipVel)
-        bc.resetJointStateMultiDof(humanoid,leftKnee, pose._leftKneeRot, pose._leftKneeVel)
-        bc.resetJointStateMultiDof(humanoid,leftAnkle, pose._leftAnkleRot, pose._leftAnkleVel)
-        bc.resetJointStateMultiDof(humanoid,leftShoulder, pose._leftShoulderRot, pose._leftShoulderVel)
-        bc.resetJointStateMultiDof(humanoid,leftElbow, pose._leftElbowRot, pose._leftElbowVel)
+        bc.resetJointStateMultiDof(humanoid, chest, pose._chestRot, pose._chestVel)
+        bc.resetJointStateMultiDof(humanoid, neck, pose._neckRot, pose._neckVel)
+        bc.resetJointStateMultiDof(humanoid, rightHip, pose._rightHipRot, pose._rightHipVel)
+        bc.resetJointStateMultiDof(humanoid, rightKnee, pose._rightKneeRot, pose._rightKneeVel)
+        bc.resetJointStateMultiDof(humanoid, rightAnkle, pose._rightAnkleRot, pose._rightAnkleVel)
+        bc.resetJointStateMultiDof(humanoid, rightShoulder, pose._rightShoulderRot,
+                                   pose._rightShoulderVel)
+        bc.resetJointStateMultiDof(humanoid, rightElbow, pose._rightElbowRot, pose._rightElbowVel)
+        bc.resetJointStateMultiDof(humanoid, leftHip, pose._leftHipRot, pose._leftHipVel)
+        bc.resetJointStateMultiDof(humanoid, leftKnee, pose._leftKneeRot, pose._leftKneeVel)
+        bc.resetJointStateMultiDof(humanoid, leftAnkle, pose._leftAnkleRot, pose._leftAnkleVel)
+        bc.resetJointStateMultiDof(humanoid, leftShoulder, pose._leftShoulderRot,
+                                   pose._leftShoulderVel)
+        bc.resetJointStateMultiDof(humanoid, leftElbow, pose._leftElbowRot, pose._leftElbowVel)
       else:
-        bc.resetJointStateMultiDof(humanoid,chest,pose._chestRot)
-        bc.resetJointStateMultiDof(humanoid,neck,pose._neckRot)
-        bc.resetJointStateMultiDof(humanoid,rightHip,pose._rightHipRot)
-        bc.resetJointStateMultiDof(humanoid,rightKnee,pose._rightKneeRot)
-        bc.resetJointStateMultiDof(humanoid,rightAnkle,pose._rightAnkleRot)
-        bc.resetJointStateMultiDof(humanoid,rightShoulder,pose._rightShoulderRot)
-        bc.resetJointStateMultiDof(humanoid,rightElbow, pose._rightElbowRot)
-        bc.resetJointStateMultiDof(humanoid,leftHip, pose._leftHipRot)
-        bc.resetJointStateMultiDof(humanoid,leftKnee, pose._leftKneeRot)
-        bc.resetJointStateMultiDof(humanoid,leftAnkle, pose._leftAnkleRot)
-        bc.resetJointStateMultiDof(humanoid,leftShoulder, pose._leftShoulderRot)
-        bc.resetJointStateMultiDof(humanoid,leftElbow, pose._leftElbowRot)
-    
-    bc.setJointMotorControlMultiDof(humanoid,chest,controlMode, targetPosition=pose._chestRot,positionGain=kp, force=[200])
-    bc.setJointMotorControlMultiDof(humanoid,neck,controlMode,targetPosition=pose._neckRot,positionGain=kp, force=[50])
-    bc.setJointMotorControlMultiDof(humanoid,rightHip,controlMode,targetPosition=pose._rightHipRot,positionGain=kp, force=[200])
-    bc.setJointMotorControlMultiDof(humanoid,rightKnee,controlMode,targetPosition=pose._rightKneeRot,positionGain=kp, force=[150])
-    bc.setJointMotorControlMultiDof(humanoid,rightAnkle,controlMode,targetPosition=pose._rightAnkleRot,positionGain=kp, force=[90])
-    bc.setJointMotorControlMultiDof(humanoid,rightShoulder,controlMode,targetPosition=pose._rightShoulderRot,positionGain=kp, force=[100])
-    bc.setJointMotorControlMultiDof(humanoid,rightElbow, controlMode,targetPosition=pose._rightElbowRot,positionGain=kp, force=[60])
-    bc.setJointMotorControlMultiDof(humanoid,leftHip, controlMode,targetPosition=pose._leftHipRot,positionGain=kp, force=[200])
-    bc.setJointMotorControlMultiDof(humanoid,leftKnee, controlMode,targetPosition=pose._leftKneeRot,positionGain=kp, force=[150])
-    bc.setJointMotorControlMultiDof(humanoid,leftAnkle, controlMode,targetPosition=pose._leftAnkleRot,positionGain=kp, force=[90])
-    bc.setJointMotorControlMultiDof(humanoid,leftShoulder, controlMode,targetPosition=pose._leftShoulderRot,positionGain=kp, force=[100])
-    bc.setJointMotorControlMultiDof(humanoid,leftElbow, controlMode,targetPosition=pose._leftElbowRot,positionGain=kp, force=[60])
+        bc.resetJointStateMultiDof(humanoid, chest, pose._chestRot)
+        bc.resetJointStateMultiDof(humanoid, neck, pose._neckRot)
+        bc.resetJointStateMultiDof(humanoid, rightHip, pose._rightHipRot)
+        bc.resetJointStateMultiDof(humanoid, rightKnee, pose._rightKneeRot)
+        bc.resetJointStateMultiDof(humanoid, rightAnkle, pose._rightAnkleRot)
+        bc.resetJointStateMultiDof(humanoid, rightShoulder, pose._rightShoulderRot)
+        bc.resetJointStateMultiDof(humanoid, rightElbow, pose._rightElbowRot)
+        bc.resetJointStateMultiDof(humanoid, leftHip, pose._leftHipRot)
+        bc.resetJointStateMultiDof(humanoid, leftKnee, pose._leftKneeRot)
+        bc.resetJointStateMultiDof(humanoid, leftAnkle, pose._leftAnkleRot)
+        bc.resetJointStateMultiDof(humanoid, leftShoulder, pose._leftShoulderRot)
+        bc.resetJointStateMultiDof(humanoid, leftElbow, pose._leftElbowRot)
+
+    bc.setJointMotorControlMultiDof(humanoid,
+                                    chest,
+                                    controlMode,
+                                    targetPosition=pose._chestRot,
+                                    positionGain=kp,
+                                    force=[200])
+    bc.setJointMotorControlMultiDof(humanoid,
+                                    neck,
+                                    controlMode,
+                                    targetPosition=pose._neckRot,
+                                    positionGain=kp,
+                                    force=[50])
+    bc.setJointMotorControlMultiDof(humanoid,
+                                    rightHip,
+                                    controlMode,
+                                    targetPosition=pose._rightHipRot,
+                                    positionGain=kp,
+                                    force=[200])
+    bc.setJointMotorControlMultiDof(humanoid,
+                                    rightKnee,
+                                    controlMode,
+                                    targetPosition=pose._rightKneeRot,
+                                    positionGain=kp,
+                                    force=[150])
+    bc.setJointMotorControlMultiDof(humanoid,
+                                    rightAnkle,
+                                    controlMode,
+                                    targetPosition=pose._rightAnkleRot,
+                                    positionGain=kp,
+                                    force=[90])
+    bc.setJointMotorControlMultiDof(humanoid,
+                                    rightShoulder,
+                                    controlMode,
+                                    targetPosition=pose._rightShoulderRot,
+                                    positionGain=kp,
+                                    force=[100])
+    bc.setJointMotorControlMultiDof(humanoid,
+                                    rightElbow,
+                                    controlMode,
+                                    targetPosition=pose._rightElbowRot,
+                                    positionGain=kp,
+                                    force=[60])
+    bc.setJointMotorControlMultiDof(humanoid,
+                                    leftHip,
+                                    controlMode,
+                                    targetPosition=pose._leftHipRot,
+                                    positionGain=kp,
+                                    force=[200])
+    bc.setJointMotorControlMultiDof(humanoid,
+                                    leftKnee,
+                                    controlMode,
+                                    targetPosition=pose._leftKneeRot,
+                                    positionGain=kp,
+                                    force=[150])
+    bc.setJointMotorControlMultiDof(humanoid,
+                                    leftAnkle,
+                                    controlMode,
+                                    targetPosition=pose._leftAnkleRot,
+                                    positionGain=kp,
+                                    force=[90])
+    bc.setJointMotorControlMultiDof(humanoid,
+                                    leftShoulder,
+                                    controlMode,
+                                    targetPosition=pose._leftShoulderRot,
+                                    positionGain=kp,
+                                    force=[100])
+    bc.setJointMotorControlMultiDof(humanoid,
+                                    leftElbow,
+                                    controlMode,
+                                    targetPosition=pose._leftElbowRot,
+                                    positionGain=kp,
+                                    force=[60])
 
     #debug space
     #if (False):
@@ -467,51 +563,58 @@ class Humanoid(object):
     #    jsm = bc.getJointStateMultiDof(self._humanoid, j)
     #    if (len(jsm[0])>0):
     #      bc.resetJointStateMultiDof(self._humanoidDebug,j,jsm[0])
-        
+
   def GetState(self):
 
     stateVector = []
     phase = self.GetPhase()
     #print("phase=",phase)
     stateVector.append(phase)
-    
-    rootTransPos, rootTransOrn=self.BuildOriginTrans()
-    basePos,baseOrn = self._pybullet_client.getBasePositionAndOrientation(self._humanoid)
-    
-    rootPosRel, dummy = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, basePos,[0,0,0,1])
+
+    rootTransPos, rootTransOrn = self.BuildOriginTrans()
+    basePos, baseOrn = self._pybullet_client.getBasePositionAndOrientation(self._humanoid)
+
+    rootPosRel, dummy = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn,
+                                                                 basePos, [0, 0, 0, 1])
     #print("!!!rootPosRel =",rootPosRel )
     #print("rootTransPos=",rootTransPos)
     #print("basePos=",basePos)
-    localPos,localOrn = self._pybullet_client.multiplyTransforms( rootTransPos, rootTransOrn , basePos,baseOrn )
-    
-    localPos=[localPos[0]-rootPosRel[0],localPos[1]-rootPosRel[1],localPos[2]-rootPosRel[2]]
+    localPos, localOrn = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn,
+                                                                  basePos, baseOrn)
+
+    localPos = [
+        localPos[0] - rootPosRel[0], localPos[1] - rootPosRel[1], localPos[2] - rootPosRel[2]
+    ]
     #print("localPos=",localPos)
-        
+
     stateVector.append(rootPosRel[1])
-    
-    self.pb2dmJoints=[0,1,2,9,10,11,3,4,5,12,13,14,6,7,8]
-    
-    for pbJoint in range (self._pybullet_client.getNumJoints(self._humanoid)):
+
+    self.pb2dmJoints = [0, 1, 2, 9, 10, 11, 3, 4, 5, 12, 13, 14, 6, 7, 8]
+
+    for pbJoint in range(self._pybullet_client.getNumJoints(self._humanoid)):
       j = self.pb2dmJoints[pbJoint]
       #print("joint order:",j)
       ls = self._pybullet_client.getLinkState(self._humanoid, j, computeForwardKinematics=True)
       linkPos = ls[0]
       linkOrn = ls[1]
-      linkPosLocal, linkOrnLocal = self._pybullet_client.multiplyTransforms(rootTransPos, rootTransOrn, linkPos,linkOrn)
-      if (linkOrnLocal[3]<0):
-        linkOrnLocal=[-linkOrnLocal[0],-linkOrnLocal[1],-linkOrnLocal[2],-linkOrnLocal[3]]
-      linkPosLocal=[linkPosLocal[0]-rootPosRel[0],linkPosLocal[1]-rootPosRel[1],linkPosLocal[2]-rootPosRel[2]]
+      linkPosLocal, linkOrnLocal = self._pybullet_client.multiplyTransforms(
+          rootTransPos, rootTransOrn, linkPos, linkOrn)
+      if (linkOrnLocal[3] < 0):
+        linkOrnLocal = [-linkOrnLocal[0], -linkOrnLocal[1], -linkOrnLocal[2], -linkOrnLocal[3]]
+      linkPosLocal = [
+          linkPosLocal[0] - rootPosRel[0], linkPosLocal[1] - rootPosRel[1],
+          linkPosLocal[2] - rootPosRel[2]
+      ]
       for l in linkPosLocal:
         stateVector.append(l)
-      
+
       #re-order the quaternion, DeepMimic uses w,x,y,z
       stateVector.append(linkOrnLocal[3])
       stateVector.append(linkOrnLocal[0])
       stateVector.append(linkOrnLocal[1])
       stateVector.append(linkOrnLocal[2])
-    
-    
-    for pbJoint in range (self._pybullet_client.getNumJoints(self._humanoid)):
+
+    for pbJoint in range(self._pybullet_client.getNumJoints(self._humanoid)):
       j = self.pb2dmJoints[pbJoint]
       ls = self._pybullet_client.getLinkState(self._humanoid, j, computeLinkVelocity=True)
       linkLinVel = ls[6]
@@ -520,19 +623,18 @@ class Humanoid(object):
         stateVector.append(l)
       for l in linkAngVel:
         stateVector.append(l)
-    
-    #print("stateVector len=",len(stateVector))  
+
+    #print("stateVector len=",len(stateVector))
     #for st in range (len(stateVector)):
     #  print("state[",st,"]=",stateVector[st])
     return stateVector
-  
-  
+
   def GetReward(self):
     #from DeepMimic double cSceneImitate::CalcRewardImitate
     pose_w = 0.5
     vel_w = 0.05
-    end_eff_w = 0 #0.15
-    root_w = 0 #0.2
+    end_eff_w = 0  #0.15
+    root_w = 0  #0.2
     com_w = 0.1
 
     total_w = pose_w + vel_w + end_eff_w + root_w + com_w
@@ -557,9 +659,9 @@ class Humanoid(object):
     root_err = 0
     com_err = 0
     heading_err = 0
-    
+
     #create a mimic reward, comparing the dynamics humanoid with a kinematic one
-    
+
     pose = self.InitializePoseFromMotionData()
     #print("self._kinematicHumanoid=",self._kinematicHumanoid)
     #print("kinematicHumanoid #joints=",self.kin_client.getNumJoints(self._kinematicHumanoid))
@@ -588,46 +690,49 @@ class Humanoid(object):
     #tVector root_ang_vel0 = cKinTree::GetRootAngVel(joint_mat, vel0);
     #tVector root_ang_vel1 = cKinTree::GetRootAngVel(joint_mat, vel1);
 
-    mJointWeights = [0.20833,0.10416, 0.0625, 0.10416,
-      0.0625, 0.041666666666666671, 0.0625, 0.0416,
-      0.00, 0.10416,  0.0625, 0.0416, 0.0625, 0.0416, 0.0000]
-      
+    mJointWeights = [
+        0.20833, 0.10416, 0.0625, 0.10416, 0.0625, 0.041666666666666671, 0.0625, 0.0416, 0.00,
+        0.10416, 0.0625, 0.0416, 0.0625, 0.0416, 0.0000
+    ]
+
     num_end_effs = 0
     num_joints = 15
-    
+
     root_rot_w = mJointWeights[root_id]
     #pose_err += root_rot_w * cKinTree::CalcRootRotErr(joint_mat, pose0, pose1)
     #vel_err += root_rot_w * cKinTree::CalcRootAngVelErr(joint_mat, vel0, vel1)
 
-    for j in range (num_joints):
+    for j in range(num_joints):
       curr_pose_err = 0
       curr_vel_err = 0
-      w = mJointWeights[j];
-      
+      w = mJointWeights[j]
+
       simJointInfo = self._pybullet_client.getJointStateMultiDof(self._humanoid, j)
-      
+
       #print("simJointInfo.pos=",simJointInfo[0])
       #print("simJointInfo.vel=",simJointInfo[1])
-      kinJointInfo = self.kin_client.getJointStateMultiDof(self._kinematicHumanoid,j)
+      kinJointInfo = self.kin_client.getJointStateMultiDof(self._kinematicHumanoid, j)
       #print("kinJointInfo.pos=",kinJointInfo[0])
       #print("kinJointInfo.vel=",kinJointInfo[1])
-      if (len(simJointInfo[0])==1):
-        angle = simJointInfo[0][0]-kinJointInfo[0][0]
-        curr_pose_err = angle*angle
-        velDiff = simJointInfo[1][0]-kinJointInfo[1][0]
-        curr_vel_err = velDiff*velDiff
-      if (len(simJointInfo[0])==4):
+      if (len(simJointInfo[0]) == 1):
+        angle = simJointInfo[0][0] - kinJointInfo[0][0]
+        curr_pose_err = angle * angle
+        velDiff = simJointInfo[1][0] - kinJointInfo[1][0]
+        curr_vel_err = velDiff * velDiff
+      if (len(simJointInfo[0]) == 4):
         #print("quaternion diff")
-        diffQuat = self._pybullet_client.getDifferenceQuaternion(simJointInfo[0],kinJointInfo[0])
-        axis,angle = self._pybullet_client.getAxisAngleFromQuaternion(diffQuat)
-        curr_pose_err = angle*angle
-        diffVel = [simJointInfo[1][0]-kinJointInfo[1][0],simJointInfo[1][1]-kinJointInfo[1][1],simJointInfo[1][2]-kinJointInfo[1][2]]
-        curr_vel_err = diffVel[0]*diffVel[0]+diffVel[1]*diffVel[1]+diffVel[2]*diffVel[2]
-      
-      
+        diffQuat = self._pybullet_client.getDifferenceQuaternion(simJointInfo[0], kinJointInfo[0])
+        axis, angle = self._pybullet_client.getAxisAngleFromQuaternion(diffQuat)
+        curr_pose_err = angle * angle
+        diffVel = [
+            simJointInfo[1][0] - kinJointInfo[1][0], simJointInfo[1][1] - kinJointInfo[1][1],
+            simJointInfo[1][2] - kinJointInfo[1][2]
+        ]
+        curr_vel_err = diffVel[0] * diffVel[0] + diffVel[1] * diffVel[1] + diffVel[2] * diffVel[2]
+
       pose_err += w * curr_pose_err
       vel_err += w * curr_vel_err
-    
+
     #  bool is_end_eff = sim_char.IsEndEffector(j)
     #  if (is_end_eff)
     #  {
@@ -657,7 +762,7 @@ class Humanoid(object):
     #root_pos0[1] -= root_ground_h0
     #root_pos1[1] -= root_ground_h1
     #root_pos_err = (root_pos0 - root_pos1).squaredNorm()
-    #  
+    #
     #root_rot_err = cMathUtil::QuatDiffTheta(root_rot0, root_rot1)
     #root_rot_err *= root_rot_err
 
@@ -669,7 +774,7 @@ class Humanoid(object):
     #    + 0.01 * root_vel_err
     #    + 0.001 * root_ang_vel_err
     #com_err = 0.1 * (com_vel1_world - com_vel0_world).squaredNorm()
-    
+
     #print("pose_err=",pose_err)
     #print("vel_err=",vel_err)
     pose_reward = math.exp(-err_scale * pose_scale * pose_err)
@@ -682,10 +787,9 @@ class Humanoid(object):
 
     #print("reward = %f (pose_reward=%f, vel_reward=%f, end_eff_reward=%f, root_reward=%f, com_reward=%f)\n", reward,
     # pose_reward,vel_reward,end_eff_reward, root_reward, com_reward);
-    
+
     return reward
 
   def GetBasePosition(self):
-    pos,orn = self._pybullet_client.getBasePositionAndOrientation(self._humanoid)
+    pos, orn = self._pybullet_client.getBasePositionAndOrientation(self._humanoid)
     return pos
-
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/inverse_kinematics.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/inverse_kinematics.py
index 74f7686b7..5d00c5a4d 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/inverse_kinematics.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/inverse_kinematics.py
@@ -9,142 +9,138 @@ from transformation import *
 
 from pyquaternion import Quaternion
 
+
 def get_angle(vec1, vec2):
-    cos_theta = np.dot(vec1, vec2)/(np.linalg.norm(vec1) * np.linalg.norm(vec2))
-    return acos(cos_theta) 
+  cos_theta = np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
+  return acos(cos_theta)
 
 
 def get_quaternion(ox, oy, oz, x, y, z):
-    # given transformed axis in x-y-z order return a quaternion
-    ox /= np.linalg.norm(ox)
-    oy /= np.linalg.norm(oy)
-    oz /= np.linalg.norm(oz)
+  # given transformed axis in x-y-z order return a quaternion
+  ox /= np.linalg.norm(ox)
+  oy /= np.linalg.norm(oy)
+  oz /= np.linalg.norm(oz)
 
-    set1 = np.vstack((ox,oy,oz))
+  set1 = np.vstack((ox, oy, oz))
 
-    x /= np.linalg.norm(x)
-    y /= np.linalg.norm(y)
-    z /= np.linalg.norm(z)
+  x /= np.linalg.norm(x)
+  y /= np.linalg.norm(y)
+  z /= np.linalg.norm(z)
 
-    set2 = np.vstack((x,y,z))
-    rot_mat = superimposition_matrix(set1, set2, scale=False, usesvd=True)
-    rot_qua = quaternion_from_matrix(rot_mat)
+  set2 = np.vstack((x, y, z))
+  rot_mat = superimposition_matrix(set1, set2, scale=False, usesvd=True)
+  rot_qua = quaternion_from_matrix(rot_mat)
 
-    return rot_qua
+  return rot_qua
 
 
 # 3D coord to deepmimic rotations
 def coord_to_rot(frameNum, frame, frame_duration):
-    eps = 0.001
-    axis_rotate_rate = 0.3
-
-    frame = np.array(frame)
-    tmp = [[] for i in range(15)]
-    # duration of frame in seconds (1D),
-    tmp[0] = [frame_duration]
-    # root position (3D),
-    tmp[1] = frame[0]
-    # root rotation (4D),
-    root_y = (frame[7] - frame[0])
-    root_z = (frame[1] - frame[0]) 
-    root_x = np.cross(root_y, root_z)
-
-    x = np.array([1.0,0,0])
-    y = np.array([0,1.0,0])
-    z = np.array([0,0,1.0])
-    
-    rot_qua = get_quaternion(root_x, root_y, root_z, x, y, z)
-    tmp[2] = list(rot_qua)
-
-    # chest rotation (4D),
-    chest_y = (frame[8] - frame[7])
-    chest_z = (frame[14] - frame[8]) 
-    chest_x = np.cross(chest_y, chest_z)
-    rot_qua = get_quaternion(chest_x, chest_y, chest_z, root_x, root_y, root_z)
-    tmp[3] = list(rot_qua)
-
-    # neck rotation (4D),
-    neck_y = (frame[10] - frame[8])
-    neck_z = np.cross(frame[10]-frame[9], frame[8]-frame[9]) 
-    neck_x = np.cross(neck_y, neck_z)
-    rot_qua = get_quaternion(neck_x, neck_y, neck_z, chest_x, chest_y, chest_z)
-    tmp[4] = list(rot_qua)
-
-    # right hip rotation (4D),
-    r_hip_y = (frame[1] - frame[2])
-    r_hip_z = np.cross(frame[1]-frame[2], frame[3]-frame[2]) 
-    r_hip_x = np.cross(r_hip_y, r_hip_z)
-    rot_qua = get_quaternion(r_hip_x, r_hip_y, r_hip_z, root_x, root_y, root_z)
-    tmp[5] = list(rot_qua)
-
-    # right knee rotation (1D),
-    vec1 = frame[1] - frame[2]
-    vec2 = frame[3] - frame[2]
-    angle1 = get_angle(vec1, vec2)
-    tmp[6] = [angle1-pi]
-
-    # right ankle rotation (4D),
-    tmp[7] = [1,0,0,0]
-
-    #  right shoulder rotation (4D),
-    r_shou_y = (frame[14] - frame[15])
-    r_shou_z = np.cross(frame[16]-frame[15], frame[14]-frame[15]) 
-    r_shou_x = np.cross(r_shou_y, r_shou_z)
-    rot_qua = get_quaternion(r_shou_x, r_shou_y, r_shou_z, chest_x, chest_y, chest_z)
-    tmp[8] = list(rot_qua)
-
-    # right elbow rotation (1D),
-    vec1 = frame[14] - frame[15]
-    vec2 = frame[16] - frame[15]
-    angle1 = get_angle(vec1, vec2)
-    tmp[9] = [pi-angle1]
-
-    # left hip rotation (4D),
-    l_hip_y = (frame[4] - frame[5])
-    l_hip_z = np.cross(frame[4]-frame[5], frame[6]-frame[5]) 
-    l_hip_x = np.cross(l_hip_y, l_hip_z)
-    rot_qua = get_quaternion(l_hip_x, l_hip_y, l_hip_z, root_x, root_y, root_z)    
-    tmp[10] = list(rot_qua)
-    
-    # left knee rotation (1D),
-    vec1 = frame[4] - frame[5]
-    vec2 = frame[6] - frame[5]
-    angle1 = get_angle(vec1, vec2)
-    tmp[11] = [angle1-pi]
-    
-    # left ankle rotation (4D),
-    tmp[12] = [1,0,0,0]
-
-    # left shoulder rotation (4D),
-    l_shou_y = (frame[11] - frame[12])
-    l_shou_z = np.cross(frame[13]-frame[12], frame[11]-frame[12]) 
-    l_shou_x = np.cross(l_shou_y, l_shou_z)
-    rot_qua = get_quaternion(l_shou_x, l_shou_y, l_shou_z, chest_x, chest_y, chest_z)
-    tmp[13] = list(rot_qua)
-
-    # left elbow rotation (1D)
-    vec1 = frame[11] - frame[12]
-    vec2 = frame[13] - frame[12]
-    angle1 = get_angle(vec1, vec2)
-    tmp[14] = [pi-angle1]
-
-    ret = []
-    for i in tmp:
-        ret += list(i)
-    return np.array(ret)
+  eps = 0.001
+  axis_rotate_rate = 0.3
+
+  frame = np.array(frame)
+  tmp = [[] for i in range(15)]
+  # duration of frame in seconds (1D),
+  tmp[0] = [frame_duration]
+  # root position (3D),
+  tmp[1] = frame[0]
+  # root rotation (4D),
+  root_y = (frame[7] - frame[0])
+  root_z = (frame[1] - frame[0])
+  root_x = np.cross(root_y, root_z)
+
+  x = np.array([1.0, 0, 0])
+  y = np.array([0, 1.0, 0])
+  z = np.array([0, 0, 1.0])
+
+  rot_qua = get_quaternion(root_x, root_y, root_z, x, y, z)
+  tmp[2] = list(rot_qua)
+
+  # chest rotation (4D),
+  chest_y = (frame[8] - frame[7])
+  chest_z = (frame[14] - frame[8])
+  chest_x = np.cross(chest_y, chest_z)
+  rot_qua = get_quaternion(chest_x, chest_y, chest_z, root_x, root_y, root_z)
+  tmp[3] = list(rot_qua)
+
+  # neck rotation (4D),
+  neck_y = (frame[10] - frame[8])
+  neck_z = np.cross(frame[10] - frame[9], frame[8] - frame[9])
+  neck_x = np.cross(neck_y, neck_z)
+  rot_qua = get_quaternion(neck_x, neck_y, neck_z, chest_x, chest_y, chest_z)
+  tmp[4] = list(rot_qua)
+
+  # right hip rotation (4D),
+  r_hip_y = (frame[1] - frame[2])
+  r_hip_z = np.cross(frame[1] - frame[2], frame[3] - frame[2])
+  r_hip_x = np.cross(r_hip_y, r_hip_z)
+  rot_qua = get_quaternion(r_hip_x, r_hip_y, r_hip_z, root_x, root_y, root_z)
+  tmp[5] = list(rot_qua)
+
+  # right knee rotation (1D),
+  vec1 = frame[1] - frame[2]
+  vec2 = frame[3] - frame[2]
+  angle1 = get_angle(vec1, vec2)
+  tmp[6] = [angle1 - pi]
+
+  # right ankle rotation (4D),
+  tmp[7] = [1, 0, 0, 0]
+
+  #  right shoulder rotation (4D),
+  r_shou_y = (frame[14] - frame[15])
+  r_shou_z = np.cross(frame[16] - frame[15], frame[14] - frame[15])
+  r_shou_x = np.cross(r_shou_y, r_shou_z)
+  rot_qua = get_quaternion(r_shou_x, r_shou_y, r_shou_z, chest_x, chest_y, chest_z)
+  tmp[8] = list(rot_qua)
+
+  # right elbow rotation (1D),
+  vec1 = frame[14] - frame[15]
+  vec2 = frame[16] - frame[15]
+  angle1 = get_angle(vec1, vec2)
+  tmp[9] = [pi - angle1]
+
+  # left hip rotation (4D),
+  l_hip_y = (frame[4] - frame[5])
+  l_hip_z = np.cross(frame[4] - frame[5], frame[6] - frame[5])
+  l_hip_x = np.cross(l_hip_y, l_hip_z)
+  rot_qua = get_quaternion(l_hip_x, l_hip_y, l_hip_z, root_x, root_y, root_z)
+  tmp[10] = list(rot_qua)
+
+  # left knee rotation (1D),
+  vec1 = frame[4] - frame[5]
+  vec2 = frame[6] - frame[5]
+  angle1 = get_angle(vec1, vec2)
+  tmp[11] = [angle1 - pi]
+
+  # left ankle rotation (4D),
+  tmp[12] = [1, 0, 0, 0]
+
+  # left shoulder rotation (4D),
+  l_shou_y = (frame[11] - frame[12])
+  l_shou_z = np.cross(frame[13] - frame[12], frame[11] - frame[12])
+  l_shou_x = np.cross(l_shou_y, l_shou_z)
+  rot_qua = get_quaternion(l_shou_x, l_shou_y, l_shou_z, chest_x, chest_y, chest_z)
+  tmp[13] = list(rot_qua)
+
+  # left elbow rotation (1D)
+  vec1 = frame[11] - frame[12]
+  vec2 = frame[13] - frame[12]
+  angle1 = get_angle(vec1, vec2)
+  tmp[14] = [pi - angle1]
+
+  ret = []
+  for i in tmp:
+    ret += list(i)
+  return np.array(ret)
+
 
 # In[6]:
 
 
 def coord_seq_to_rot_seq(coord_seq, frame_duration):
-    ret = []
-    for i in range(len(coord_seq)):
-        tmp = coord_to_rot( i, coord_seq[i], frame_duration)
-        ret.append(list(tmp))
-    return ret
-
-
-
-
-
-
+  ret = []
+  for i in range(len(coord_seq)):
+    tmp = coord_to_rot(i, coord_seq[i], frame_duration)
+    ret.append(list(tmp))
+  return ret
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/mocap_dataset.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/mocap_dataset.py
index 18bc27ae4..c297220db 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/mocap_dataset.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/mocap_dataset.py
@@ -1,36 +1,37 @@
 import numpy as np
 from skeleton import Skeleton
 
+
 class MocapDataset:
-    def __init__(self, fps, skeleton):
-        self._skeleton = skeleton
-        self._fps = fps
-        self._data = None # Must be filled by subclass
-        self._cameras = None # Must be filled by subclass
-    
-    def remove_joints(self, joints_to_remove):
-        kept_joints = self._skeleton.remove_joints(joints_to_remove)
-        for subject in self._data.keys():
-            for action in self._data[subject].keys():
-                s = self._data[subject][action]
-                s['positions'] = s['positions'][:, kept_joints]
-                
-        
-    def __getitem__(self, key):
-        return self._data[key]
-        
-    def subjects(self):
-        return self._data.keys()
-    
-    def fps(self):
-        return self._fps
-    
-    def skeleton(self):
-        return self._skeleton
-        
-    def cameras(self):
-        return self._cameras
-    
-    def supports_semi_supervised(self):
-        # This method can be overridden
-        return False 
+
+  def __init__(self, fps, skeleton):
+    self._skeleton = skeleton
+    self._fps = fps
+    self._data = None  # Must be filled by subclass
+    self._cameras = None  # Must be filled by subclass
+
+  def remove_joints(self, joints_to_remove):
+    kept_joints = self._skeleton.remove_joints(joints_to_remove)
+    for subject in self._data.keys():
+      for action in self._data[subject].keys():
+        s = self._data[subject][action]
+        s['positions'] = s['positions'][:, kept_joints]
+
+  def __getitem__(self, key):
+    return self._data[key]
+
+  def subjects(self):
+    return self._data.keys()
+
+  def fps(self):
+    return self._fps
+
+  def skeleton(self):
+    return self._skeleton
+
+  def cameras(self):
+    return self._cameras
+
+  def supports_semi_supervised(self):
+    # This method can be overridden
+    return False
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/quaternion.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/quaternion.py
index e4e9a7753..c972846ce 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/quaternion.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/quaternion.py
@@ -1,30 +1,31 @@
 import numpy as np
 
+
 def qrot(q, v):
-    """
+  """
     Rotate vector(s) v about the rotation described by quaternion(s) q.
     Expects a tensor of shape (*, 4) for q and a tensor of shape (*, 3) for v,
     where * denotes any number of dimensions.
     Returns a tensor of shape (*, 3).
     """
-    assert q.shape[-1] == 4
-    assert v.shape[-1] == 3
-    assert q.shape[:-1] == v.shape[:-1]
+  assert q.shape[-1] == 4
+  assert v.shape[-1] == 3
+  assert q.shape[:-1] == v.shape[:-1]
+
+  qvec = q[..., 1:]
+
+  uv = np.cross(qvec, v)
+  uuv = np.cross(qvec, uv)
+
+  return (v + 2 * (q[..., :1] * uv + uuv))
 
-    qvec = q[..., 1:]
 
-    uv = np.cross(qvec, v)
-    uuv = np.cross(qvec, uv)
-    
-    return (v + 2 * (q[..., :1] * uv + uuv))
-    
 def qinverse(q, inplace=False):
-    # We assume the quaternion to be normalized
-    if inplace:
-        q[..., 1:] *= -1
-        return q
-    else:
-        w = q[..., :1]
-        xyz = q[..., 1:]
-        return np.hstack((w, -xyz))
- 
\ No newline at end of file
+  # We assume the quaternion to be normalized
+  if inplace:
+    q[..., 1:] *= -1
+    return q
+  else:
+    w = q[..., :1]
+    xyz = q[..., 1:]
+    return np.hstack((w, -xyz))
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/render_reference.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/render_reference.py
index c3882bf07..61fe8878d 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/render_reference.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/render_reference.py
@@ -1,7 +1,7 @@
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 print('parent:', parentdir)
 
@@ -10,7 +10,6 @@ import pybullet
 import time
 import random
 
-
 from pybullet_utils.bullet_client import BulletClient
 from deep_mimic.env.motion_capture_data import MotionCaptureData
 
@@ -26,21 +25,28 @@ import pybullet as p
 import numpy as np
 import argparse
 
-
-
 parser = argparse.ArgumentParser(description='Arguments for loading reference for learning.')
 
 # General arguments
-parser.add_argument('--dataset_path', default='data/data_3d_h36m.npz', type=str, help='target dataset') # h36m or humaneva
-parser.add_argument('--json_path', default='data/Walking.json', type=str, help='json file path for storing the deepmimic-format json created by inverse-kinect.')
+parser.add_argument('--dataset_path',
+                    default='data/data_3d_h36m.npz',
+                    type=str,
+                    help='target dataset')  # h36m or humaneva
+parser.add_argument(
+    '--json_path',
+    default='data/Walking.json',
+    type=str,
+    help='json file path for storing the deepmimic-format json created by inverse-kinect.')
 parser.add_argument('--fps', default=24, type=int, help='frame per second')
 parser.add_argument('--subject', default='S11', type=str, help='camera subject.')
-parser.add_argument('--action', default='Walking', type=str,  help='name of the action.')
-parser.add_argument('--loop', default='wrap', type=str, help='loop information in deepmimic, wrap or none.')
+parser.add_argument('--action', default='Walking', type=str, help='name of the action.')
+parser.add_argument('--loop',
+                    default='wrap',
+                    type=str,
+                    help='loop information in deepmimic, wrap or none.')
 parser.add_argument('--draw_gt', action='store_true', help='draw ground truth or not.')
 
-args = parser.parse_args() 
-
+args = parser.parse_args()
 
 dataset_path = args.dataset_path
 json_path = args.json_path
@@ -52,93 +58,75 @@ draw_gt = args.draw_gt
 
 
 def draw_ground_truth(coord_seq, frame, duration, shift):
-    global joint_info
-    joint = coord_seq[frame]
-    shift = np.array(shift)
-    for i in range(1, 17):
-        # print(x[11], x[14])
-        joint_fa = joint_info['father'][i]
-        if joint_info['side'][i] == 'right':
-            p.addUserDebugLine(lineFromXYZ=joint[i]+shift,
-                               lineToXYZ=joint[joint_fa]+shift,
-                               lineColorRGB=(255,0,0),
-                               lineWidth=1,
-                               lifeTime=duration)
-        else:
-            p.addUserDebugLine(lineFromXYZ=joint[i]+shift,
-                               lineToXYZ=joint[joint_fa]+shift,
-                               lineColorRGB=(0,0,0),
-                               lineWidth=1,
-                               lifeTime=duration)
+  global joint_info
+  joint = coord_seq[frame]
+  shift = np.array(shift)
+  for i in range(1, 17):
+    # print(x[11], x[14])
+    joint_fa = joint_info['father'][i]
+    if joint_info['side'][i] == 'right':
+      p.addUserDebugLine(lineFromXYZ=joint[i] + shift,
+                         lineToXYZ=joint[joint_fa] + shift,
+                         lineColorRGB=(255, 0, 0),
+                         lineWidth=1,
+                         lifeTime=duration)
+    else:
+      p.addUserDebugLine(lineFromXYZ=joint[i] + shift,
+                         lineToXYZ=joint[joint_fa] + shift,
+                         lineColorRGB=(0, 0, 0),
+                         lineWidth=1,
+                         lifeTime=duration)
 
 
 dataset = init_fb_h36m_dataset(dataset_path)
-ground_truth = pose3D_from_fb_h36m(dataset, 
-                                   subject = subject, 
-                                   action = action,
-                                   shift = [1.0,0.0,0.0])
-
-rot_seq =  coord_seq_to_rot_seq(coord_seq = ground_truth, 
-                                frame_duration = 1/fps)
-
-
-rot_seq_to_deepmimic_json(  rot_seq = rot_seq, 
-                            loop = loop,
-                            json_path = json_path)
+ground_truth = pose3D_from_fb_h36m(dataset, subject=subject, action=action, shift=[1.0, 0.0, 0.0])
 
+rot_seq = coord_seq_to_rot_seq(coord_seq=ground_truth, frame_duration=1 / fps)
 
+rot_seq_to_deepmimic_json(rot_seq=rot_seq, loop=loop, json_path=json_path)
 
 bc = BulletClient(connection_mode=pybullet.GUI)
 bc.setAdditionalSearchPath(pybullet_data.getDataPath())
-bc.configureDebugVisualizer(bc.COV_ENABLE_Y_AXIS_UP,1)
-bc.setGravity(0,-9.8,0)
-motion=MotionCaptureData()
+bc.configureDebugVisualizer(bc.COV_ENABLE_Y_AXIS_UP, 1)
+bc.setGravity(0, -9.8, 0)
+motion = MotionCaptureData()
 
 motionPath = json_path
 motion.Load(motionPath)
 print("numFrames = ", motion.NumFrames())
 
+simTimeId = bc.addUserDebugParameter("simTime", 0, motion.NumFrames() - 1.1, 0)
 
+y2zOrn = bc.getQuaternionFromEuler([-1.57, 0, 0])
+bc.loadURDF("plane.urdf", [0, -0.04, 0], y2zOrn)
 
-simTimeId= bc.addUserDebugParameter("simTime",0,motion.NumFrames()-1.1,0)
-
-y2zOrn = bc.getQuaternionFromEuler([-1.57,0,0])
-bc.loadURDF("plane.urdf",[0,-0.04,0], y2zOrn)
-
-humanoid = Humanoid(bc, motion, [0,0,0]) #这是初始位置的坐标
-
-
+humanoid = Humanoid(bc, motion, [0, 0, 0])  #这是初始位置的坐标
 
 print(p.getBasePositionAndOrientation(humanoid._humanoid))
 
-
-
 simTime = 0
 keyFrameDuration = motion.KeyFrameDuraction()
-print("keyFrameDuration=",keyFrameDuration)
+print("keyFrameDuration=", keyFrameDuration)
 for utNum in range(motion.NumFrames()):
-    bc.stepSimulation()
-    humanoid.RenderReference(utNum * keyFrameDuration)
-    if draw_gt:
-        draw_ground_truth(coord_seq = ground_truth, 
-                        frame = utNum, 
-                        duration = keyFrameDuration,
-                        shift = [-1.0, 0.0, 1.0])
-    time.sleep(0.001)
+  bc.stepSimulation()
+  humanoid.RenderReference(utNum * keyFrameDuration)
+  if draw_gt:
+    draw_ground_truth(coord_seq=ground_truth,
+                      frame=utNum,
+                      duration=keyFrameDuration,
+                      shift=[-1.0, 0.0, 1.0])
+  time.sleep(0.001)
 stage = 0
 
 
-
 def Reset(humanoid):
-	global simTime
-	humanoid.Reset()
-	simTime = 0 
-	humanoid.SetSimTime(simTime)
-	pose = humanoid.InitializePoseFromMotionData()
-	humanoid.ApplyPose(pose, True, True, humanoid._humanoid,bc)
+  global simTime
+  humanoid.Reset()
+  simTime = 0
+  humanoid.SetSimTime(simTime)
+  pose = humanoid.InitializePoseFromMotionData()
+  humanoid.ApplyPose(pose, True, True, humanoid._humanoid, bc)
 
 
 Reset(humanoid)
 p.disconnect()
-
-
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/skeleton.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/skeleton.py
index fa6d1e622..6e8fe8ba6 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/skeleton.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/skeleton.py
@@ -1,81 +1,82 @@
 import numpy as np
 
+
 class Skeleton:
-    def __init__(self, parents, joints_left, joints_right):
-        assert len(joints_left) == len(joints_right)
-        
-        self._parents = np.array(parents)
-        self._joints_left = joints_left
-        self._joints_right = joints_right
-        self._compute_metadata()
-    
-    def num_joints(self):
-        return len(self._parents)
-    
-    def parents(self):
-        return self._parents
-    
-    def has_children(self):
-        return self._has_children
-    
-    def children(self):
-        return self._children
-    
-    def remove_joints(self, joints_to_remove):
-        """
+
+  def __init__(self, parents, joints_left, joints_right):
+    assert len(joints_left) == len(joints_right)
+
+    self._parents = np.array(parents)
+    self._joints_left = joints_left
+    self._joints_right = joints_right
+    self._compute_metadata()
+
+  def num_joints(self):
+    return len(self._parents)
+
+  def parents(self):
+    return self._parents
+
+  def has_children(self):
+    return self._has_children
+
+  def children(self):
+    return self._children
+
+  def remove_joints(self, joints_to_remove):
+    """
         Remove the joints specified in 'joints_to_remove'.
         """
-        valid_joints = []
-        for joint in range(len(self._parents)):
-            if joint not in joints_to_remove:
-                valid_joints.append(joint)
-
-        for i in range(len(self._parents)):
-            while self._parents[i] in joints_to_remove:
-                self._parents[i] = self._parents[self._parents[i]]
-                
-        index_offsets = np.zeros(len(self._parents), dtype=int)
-        new_parents = []
-        for i, parent in enumerate(self._parents):
-            if i not in joints_to_remove:
-                new_parents.append(parent - index_offsets[parent])
-            else:
-                index_offsets[i:] += 1
-        self._parents = np.array(new_parents)
-        
-        
-        if self._joints_left is not None:
-            new_joints_left = []
-            for joint in self._joints_left:
-                if joint in valid_joints:
-                    new_joints_left.append(joint - index_offsets[joint])
-            self._joints_left = new_joints_left
-        if self._joints_right is not None:
-            new_joints_right = []
-            for joint in self._joints_right:
-                if joint in valid_joints:
-                    new_joints_right.append(joint - index_offsets[joint])
-            self._joints_right = new_joints_right
-
-        self._compute_metadata()
-        
-        return valid_joints
-    
-    def joints_left(self):
-        return self._joints_left
-    
-    def joints_right(self):
-        return self._joints_right
-        
-    def _compute_metadata(self):
-        self._has_children = np.zeros(len(self._parents)).astype(bool)
-        for i, parent in enumerate(self._parents):
-            if parent != -1:
-                self._has_children[parent] = True
-
-        self._children = []
-        for i, parent in enumerate(self._parents):
-            self._children.append([])
-        for i, parent in enumerate(self._parents):
-            if parent != -1:
-                self._children[parent].append(i) 
+    valid_joints = []
+    for joint in range(len(self._parents)):
+      if joint not in joints_to_remove:
+        valid_joints.append(joint)
+
+    for i in range(len(self._parents)):
+      while self._parents[i] in joints_to_remove:
+        self._parents[i] = self._parents[self._parents[i]]
+
+    index_offsets = np.zeros(len(self._parents), dtype=int)
+    new_parents = []
+    for i, parent in enumerate(self._parents):
+      if i not in joints_to_remove:
+        new_parents.append(parent - index_offsets[parent])
+      else:
+        index_offsets[i:] += 1
+    self._parents = np.array(new_parents)
+
+    if self._joints_left is not None:
+      new_joints_left = []
+      for joint in self._joints_left:
+        if joint in valid_joints:
+          new_joints_left.append(joint - index_offsets[joint])
+      self._joints_left = new_joints_left
+    if self._joints_right is not None:
+      new_joints_right = []
+      for joint in self._joints_right:
+        if joint in valid_joints:
+          new_joints_right.append(joint - index_offsets[joint])
+      self._joints_right = new_joints_right
+
+    self._compute_metadata()
+
+    return valid_joints
+
+  def joints_left(self):
+    return self._joints_left
+
+  def joints_right(self):
+    return self._joints_right
+
+  def _compute_metadata(self):
+    self._has_children = np.zeros(len(self._parents)).astype(bool)
+    for i, parent in enumerate(self._parents):
+      if parent != -1:
+        self._has_children[parent] = True
+
+    self._children = []
+    for i, parent in enumerate(self._parents):
+      self._children.append([])
+    for i, parent in enumerate(self._parents):
+      if parent != -1:
+        self._children[parent].append(i)
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/transformation.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/transformation.py
index f9745fe6e..e4807b538 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/transformation.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/transformation.py
@@ -1,13 +1,12 @@
 from __future__ import division, print_function
 
-
 import math
 
 import numpy
 
 
 def identity_matrix():
-    """Return 4x4 identity/unit matrix.
+  """Return 4x4 identity/unit matrix.
 
     >>> I = identity_matrix()
     >>> numpy.allclose(I, numpy.dot(I, I))
@@ -18,24 +17,24 @@ def identity_matrix():
     True
 
     """
-    return numpy.identity(4)
+  return numpy.identity(4)
 
 
 def translation_matrix(direction):
-    """Return matrix to translate by direction vector.
+  """Return matrix to translate by direction vector.
 
     >>> v = numpy.random.random(3) - 0.5
     >>> numpy.allclose(v, translation_matrix(v)[:3, 3])
     True
 
     """
-    M = numpy.identity(4)
-    M[:3, 3] = direction[:3]
-    return M
+  M = numpy.identity(4)
+  M[:3, 3] = direction[:3]
+  return M
 
 
 def translation_from_matrix(matrix):
-    """Return translation vector from translation matrix.
+  """Return translation vector from translation matrix.
 
     >>> v0 = numpy.random.random(3) - 0.5
     >>> v1 = translation_from_matrix(translation_matrix(v0))
@@ -43,11 +42,11 @@ def translation_from_matrix(matrix):
     True
 
     """
-    return numpy.array(matrix, copy=False)[:3, 3].copy()
+  return numpy.array(matrix, copy=False)[:3, 3].copy()
 
 
 def reflection_matrix(point, normal):
-    """Return matrix to mirror at plane defined by point and normal vector.
+  """Return matrix to mirror at plane defined by point and normal vector.
 
     >>> v0 = numpy.random.random(4) - 0.5
     >>> v0[3] = 1.
@@ -65,15 +64,15 @@ def reflection_matrix(point, normal):
     True
 
     """
-    normal = unit_vector(normal[:3])
-    M = numpy.identity(4)
-    M[:3, :3] -= 2.0 * numpy.outer(normal, normal)
-    M[:3, 3] = (2.0 * numpy.dot(point[:3], normal)) * normal
-    return M
+  normal = unit_vector(normal[:3])
+  M = numpy.identity(4)
+  M[:3, :3] -= 2.0 * numpy.outer(normal, normal)
+  M[:3, 3] = (2.0 * numpy.dot(point[:3], normal)) * normal
+  return M
 
 
 def reflection_from_matrix(matrix):
-    """Return mirror plane point and normal vector from reflection matrix.
+  """Return mirror plane point and normal vector from reflection matrix.
 
     >>> v0 = numpy.random.random(3) - 0.5
     >>> v1 = numpy.random.random(3) - 0.5
@@ -84,25 +83,25 @@ def reflection_from_matrix(matrix):
     True
 
     """
-    M = numpy.array(matrix, dtype=numpy.float64, copy=False)
-    # normal: unit eigenvector corresponding to eigenvalue -1
-    w, V = numpy.linalg.eig(M[:3, :3])
-    i = numpy.where(abs(numpy.real(w) + 1.0) < 1e-8)[0]
-    if not len(i):
-        raise ValueError('no unit eigenvector corresponding to eigenvalue -1')
-    normal = numpy.real(V[:, i[0]]).squeeze()
-    # point: any unit eigenvector corresponding to eigenvalue 1
-    w, V = numpy.linalg.eig(M)
-    i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0]
-    if not len(i):
-        raise ValueError('no unit eigenvector corresponding to eigenvalue 1')
-    point = numpy.real(V[:, i[-1]]).squeeze()
-    point /= point[3]
-    return point, normal
+  M = numpy.array(matrix, dtype=numpy.float64, copy=False)
+  # normal: unit eigenvector corresponding to eigenvalue -1
+  w, V = numpy.linalg.eig(M[:3, :3])
+  i = numpy.where(abs(numpy.real(w) + 1.0) < 1e-8)[0]
+  if not len(i):
+    raise ValueError('no unit eigenvector corresponding to eigenvalue -1')
+  normal = numpy.real(V[:, i[0]]).squeeze()
+  # point: any unit eigenvector corresponding to eigenvalue 1
+  w, V = numpy.linalg.eig(M)
+  i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0]
+  if not len(i):
+    raise ValueError('no unit eigenvector corresponding to eigenvalue 1')
+  point = numpy.real(V[:, i[-1]]).squeeze()
+  point /= point[3]
+  return point, normal
 
 
 def rotation_matrix(angle, direction, point=None):
-    """Return matrix to rotate about axis defined by point and direction.
+  """Return matrix to rotate about axis defined by point and direction.
 
     >>> R = rotation_matrix(math.pi/2, [0, 0, 1], [1, 0, 0])
     >>> numpy.allclose(numpy.dot(R, [0, 0, 0, 1]), [1, -1, 0, 1])
@@ -126,27 +125,26 @@ def rotation_matrix(angle, direction, point=None):
     True
 
     """
-    sina = math.sin(angle)
-    cosa = math.cos(angle)
-    direction = unit_vector(direction[:3])
-    # rotation matrix around unit vector
-    R = numpy.diag([cosa, cosa, cosa])
-    R += numpy.outer(direction, direction) * (1.0 - cosa)
-    direction *= sina
-    R += numpy.array([[ 0.0,         -direction[2],  direction[1]],
-                      [ direction[2], 0.0,          -direction[0]],
-                      [-direction[1], direction[0],  0.0]])
-    M = numpy.identity(4)
-    M[:3, :3] = R
-    if point is not None:
-        # rotation not around origin
-        point = numpy.array(point[:3], dtype=numpy.float64, copy=False)
-        M[:3, 3] = point - numpy.dot(R, point)
-    return M
+  sina = math.sin(angle)
+  cosa = math.cos(angle)
+  direction = unit_vector(direction[:3])
+  # rotation matrix around unit vector
+  R = numpy.diag([cosa, cosa, cosa])
+  R += numpy.outer(direction, direction) * (1.0 - cosa)
+  direction *= sina
+  R += numpy.array([[0.0, -direction[2], direction[1]], [direction[2], 0.0, -direction[0]],
+                    [-direction[1], direction[0], 0.0]])
+  M = numpy.identity(4)
+  M[:3, :3] = R
+  if point is not None:
+    # rotation not around origin
+    point = numpy.array(point[:3], dtype=numpy.float64, copy=False)
+    M[:3, 3] = point - numpy.dot(R, point)
+  return M
 
 
 def rotation_from_matrix(matrix):
-    """Return rotation angle and axis from rotation matrix.
+  """Return rotation angle and axis from rotation matrix.
 
     >>> angle = (random.random() - 0.5) * (2*math.pi)
     >>> direc = numpy.random.random(3) - 0.5
@@ -158,35 +156,35 @@ def rotation_from_matrix(matrix):
     True
 
     """
-    R = numpy.array(matrix, dtype=numpy.float64, copy=False)
-    R33 = R[:3, :3]
-    # direction: unit eigenvector of R33 corresponding to eigenvalue of 1
-    w, W = numpy.linalg.eig(R33.T)
-    i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0]
-    if not len(i):
-        raise ValueError('no unit eigenvector corresponding to eigenvalue 1')
-    direction = numpy.real(W[:, i[-1]]).squeeze()
-    # point: unit eigenvector of R33 corresponding to eigenvalue of 1
-    w, Q = numpy.linalg.eig(R)
-    i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0]
-    if not len(i):
-        raise ValueError('no unit eigenvector corresponding to eigenvalue 1')
-    point = numpy.real(Q[:, i[-1]]).squeeze()
-    point /= point[3]
-    # rotation angle depending on direction
-    cosa = (numpy.trace(R33) - 1.0) / 2.0
-    if abs(direction[2]) > 1e-8:
-        sina = (R[1, 0] + (cosa-1.0)*direction[0]*direction[1]) / direction[2]
-    elif abs(direction[1]) > 1e-8:
-        sina = (R[0, 2] + (cosa-1.0)*direction[0]*direction[2]) / direction[1]
-    else:
-        sina = (R[2, 1] + (cosa-1.0)*direction[1]*direction[2]) / direction[0]
-    angle = math.atan2(sina, cosa)
-    return angle, direction, point
+  R = numpy.array(matrix, dtype=numpy.float64, copy=False)
+  R33 = R[:3, :3]
+  # direction: unit eigenvector of R33 corresponding to eigenvalue of 1
+  w, W = numpy.linalg.eig(R33.T)
+  i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0]
+  if not len(i):
+    raise ValueError('no unit eigenvector corresponding to eigenvalue 1')
+  direction = numpy.real(W[:, i[-1]]).squeeze()
+  # point: unit eigenvector of R33 corresponding to eigenvalue of 1
+  w, Q = numpy.linalg.eig(R)
+  i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0]
+  if not len(i):
+    raise ValueError('no unit eigenvector corresponding to eigenvalue 1')
+  point = numpy.real(Q[:, i[-1]]).squeeze()
+  point /= point[3]
+  # rotation angle depending on direction
+  cosa = (numpy.trace(R33) - 1.0) / 2.0
+  if abs(direction[2]) > 1e-8:
+    sina = (R[1, 0] + (cosa - 1.0) * direction[0] * direction[1]) / direction[2]
+  elif abs(direction[1]) > 1e-8:
+    sina = (R[0, 2] + (cosa - 1.0) * direction[0] * direction[2]) / direction[1]
+  else:
+    sina = (R[2, 1] + (cosa - 1.0) * direction[1] * direction[2]) / direction[0]
+  angle = math.atan2(sina, cosa)
+  return angle, direction, point
 
 
 def scale_matrix(factor, origin=None, direction=None):
-    """Return matrix to scale by factor around origin in direction.
+  """Return matrix to scale by factor around origin in direction.
 
     Use factor -1 for point symmetry.
 
@@ -202,25 +200,25 @@ def scale_matrix(factor, origin=None, direction=None):
     >>> S = scale_matrix(factor, origin, direct)
 
     """
-    if direction is None:
-        # uniform scaling
-        M = numpy.diag([factor, factor, factor, 1.0])
-        if origin is not None:
-            M[:3, 3] = origin[:3]
-            M[:3, 3] *= 1.0 - factor
-    else:
-        # nonuniform scaling
-        direction = unit_vector(direction[:3])
-        factor = 1.0 - factor
-        M = numpy.identity(4)
-        M[:3, :3] -= factor * numpy.outer(direction, direction)
-        if origin is not None:
-            M[:3, 3] = (factor * numpy.dot(origin[:3], direction)) * direction
-    return M
+  if direction is None:
+    # uniform scaling
+    M = numpy.diag([factor, factor, factor, 1.0])
+    if origin is not None:
+      M[:3, 3] = origin[:3]
+      M[:3, 3] *= 1.0 - factor
+  else:
+    # nonuniform scaling
+    direction = unit_vector(direction[:3])
+    factor = 1.0 - factor
+    M = numpy.identity(4)
+    M[:3, :3] -= factor * numpy.outer(direction, direction)
+    if origin is not None:
+      M[:3, 3] = (factor * numpy.dot(origin[:3], direction)) * direction
+  return M
 
 
 def scale_from_matrix(matrix):
-    """Return scaling factor, origin and direction from scaling matrix.
+  """Return scaling factor, origin and direction from scaling matrix.
 
     >>> factor = random.random() * 10 - 5
     >>> origin = numpy.random.random(3) - 0.5
@@ -237,32 +235,31 @@ def scale_from_matrix(matrix):
     True
 
     """
-    M = numpy.array(matrix, dtype=numpy.float64, copy=False)
-    M33 = M[:3, :3]
-    factor = numpy.trace(M33) - 2.0
-    try:
-        # direction: unit eigenvector corresponding to eigenvalue factor
-        w, V = numpy.linalg.eig(M33)
-        i = numpy.where(abs(numpy.real(w) - factor) < 1e-8)[0][0]
-        direction = numpy.real(V[:, i]).squeeze()
-        direction /= vector_norm(direction)
-    except IndexError:
-        # uniform scaling
-        factor = (factor + 2.0) / 3.0
-        direction = None
-    # origin: any eigenvector corresponding to eigenvalue 1
-    w, V = numpy.linalg.eig(M)
-    i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0]
-    if not len(i):
-        raise ValueError('no eigenvector corresponding to eigenvalue 1')
-    origin = numpy.real(V[:, i[-1]]).squeeze()
-    origin /= origin[3]
-    return factor, origin, direction
-
-
-def projection_matrix(point, normal, direction=None,
-                      perspective=None, pseudo=False):
-    """Return matrix to project onto plane defined by point and normal.
+  M = numpy.array(matrix, dtype=numpy.float64, copy=False)
+  M33 = M[:3, :3]
+  factor = numpy.trace(M33) - 2.0
+  try:
+    # direction: unit eigenvector corresponding to eigenvalue factor
+    w, V = numpy.linalg.eig(M33)
+    i = numpy.where(abs(numpy.real(w) - factor) < 1e-8)[0][0]
+    direction = numpy.real(V[:, i]).squeeze()
+    direction /= vector_norm(direction)
+  except IndexError:
+    # uniform scaling
+    factor = (factor + 2.0) / 3.0
+    direction = None
+  # origin: any eigenvector corresponding to eigenvalue 1
+  w, V = numpy.linalg.eig(M)
+  i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0]
+  if not len(i):
+    raise ValueError('no eigenvector corresponding to eigenvalue 1')
+  origin = numpy.real(V[:, i[-1]]).squeeze()
+  origin /= origin[3]
+  return factor, origin, direction
+
+
+def projection_matrix(point, normal, direction=None, perspective=None, pseudo=False):
+  """Return matrix to project onto plane defined by point and normal.
 
     Using either perspective point, projection direction, or none of both.
 
@@ -292,38 +289,37 @@ def projection_matrix(point, normal, direction=None,
     True
 
     """
-    M = numpy.identity(4)
-    point = numpy.array(point[:3], dtype=numpy.float64, copy=False)
-    normal = unit_vector(normal[:3])
-    if perspective is not None:
-        # perspective projection
-        perspective = numpy.array(perspective[:3], dtype=numpy.float64,
-                                  copy=False)
-        M[0, 0] = M[1, 1] = M[2, 2] = numpy.dot(perspective-point, normal)
-        M[:3, :3] -= numpy.outer(perspective, normal)
-        if pseudo:
-            # preserve relative depth
-            M[:3, :3] -= numpy.outer(normal, normal)
-            M[:3, 3] = numpy.dot(point, normal) * (perspective+normal)
-        else:
-            M[:3, 3] = numpy.dot(point, normal) * perspective
-        M[3, :3] = -normal
-        M[3, 3] = numpy.dot(perspective, normal)
-    elif direction is not None:
-        # parallel projection
-        direction = numpy.array(direction[:3], dtype=numpy.float64, copy=False)
-        scale = numpy.dot(direction, normal)
-        M[:3, :3] -= numpy.outer(direction, normal) / scale
-        M[:3, 3] = direction * (numpy.dot(point, normal) / scale)
+  M = numpy.identity(4)
+  point = numpy.array(point[:3], dtype=numpy.float64, copy=False)
+  normal = unit_vector(normal[:3])
+  if perspective is not None:
+    # perspective projection
+    perspective = numpy.array(perspective[:3], dtype=numpy.float64, copy=False)
+    M[0, 0] = M[1, 1] = M[2, 2] = numpy.dot(perspective - point, normal)
+    M[:3, :3] -= numpy.outer(perspective, normal)
+    if pseudo:
+      # preserve relative depth
+      M[:3, :3] -= numpy.outer(normal, normal)
+      M[:3, 3] = numpy.dot(point, normal) * (perspective + normal)
     else:
-        # orthogonal projection
-        M[:3, :3] -= numpy.outer(normal, normal)
-        M[:3, 3] = numpy.dot(point, normal) * normal
-    return M
+      M[:3, 3] = numpy.dot(point, normal) * perspective
+    M[3, :3] = -normal
+    M[3, 3] = numpy.dot(perspective, normal)
+  elif direction is not None:
+    # parallel projection
+    direction = numpy.array(direction[:3], dtype=numpy.float64, copy=False)
+    scale = numpy.dot(direction, normal)
+    M[:3, :3] -= numpy.outer(direction, normal) / scale
+    M[:3, 3] = direction * (numpy.dot(point, normal) / scale)
+  else:
+    # orthogonal projection
+    M[:3, :3] -= numpy.outer(normal, normal)
+    M[:3, 3] = numpy.dot(point, normal) * normal
+  return M
 
 
 def projection_from_matrix(matrix, pseudo=False):
-    """Return projection plane and perspective point from projection matrix.
+  """Return projection plane and perspective point from projection matrix.
 
     Return values are same as arguments for projection_matrix function:
     point, normal, direction, perspective, and pseudo.
@@ -354,49 +350,48 @@ def projection_from_matrix(matrix, pseudo=False):
     True
 
     """
-    M = numpy.array(matrix, dtype=numpy.float64, copy=False)
-    M33 = M[:3, :3]
-    w, V = numpy.linalg.eig(M)
-    i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0]
-    if not pseudo and len(i):
-        # point: any eigenvector corresponding to eigenvalue 1
-        point = numpy.real(V[:, i[-1]]).squeeze()
-        point /= point[3]
-        # direction: unit eigenvector corresponding to eigenvalue 0
-        w, V = numpy.linalg.eig(M33)
-        i = numpy.where(abs(numpy.real(w)) < 1e-8)[0]
-        if not len(i):
-            raise ValueError('no eigenvector corresponding to eigenvalue 0')
-        direction = numpy.real(V[:, i[0]]).squeeze()
-        direction /= vector_norm(direction)
-        # normal: unit eigenvector of M33.T corresponding to eigenvalue 0
-        w, V = numpy.linalg.eig(M33.T)
-        i = numpy.where(abs(numpy.real(w)) < 1e-8)[0]
-        if len(i):
-            # parallel projection
-            normal = numpy.real(V[:, i[0]]).squeeze()
-            normal /= vector_norm(normal)
-            return point, normal, direction, None, False
-        else:
-            # orthogonal projection, where normal equals direction vector
-            return point, direction, None, None, False
+  M = numpy.array(matrix, dtype=numpy.float64, copy=False)
+  M33 = M[:3, :3]
+  w, V = numpy.linalg.eig(M)
+  i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0]
+  if not pseudo and len(i):
+    # point: any eigenvector corresponding to eigenvalue 1
+    point = numpy.real(V[:, i[-1]]).squeeze()
+    point /= point[3]
+    # direction: unit eigenvector corresponding to eigenvalue 0
+    w, V = numpy.linalg.eig(M33)
+    i = numpy.where(abs(numpy.real(w)) < 1e-8)[0]
+    if not len(i):
+      raise ValueError('no eigenvector corresponding to eigenvalue 0')
+    direction = numpy.real(V[:, i[0]]).squeeze()
+    direction /= vector_norm(direction)
+    # normal: unit eigenvector of M33.T corresponding to eigenvalue 0
+    w, V = numpy.linalg.eig(M33.T)
+    i = numpy.where(abs(numpy.real(w)) < 1e-8)[0]
+    if len(i):
+      # parallel projection
+      normal = numpy.real(V[:, i[0]]).squeeze()
+      normal /= vector_norm(normal)
+      return point, normal, direction, None, False
     else:
-        # perspective projection
-        i = numpy.where(abs(numpy.real(w)) > 1e-8)[0]
-        if not len(i):
-            raise ValueError(
-                'no eigenvector not corresponding to eigenvalue 0')
-        point = numpy.real(V[:, i[-1]]).squeeze()
-        point /= point[3]
-        normal = - M[3, :3]
-        perspective = M[:3, 3] / numpy.dot(point[:3], normal)
-        if pseudo:
-            perspective -= normal
-        return point, normal, None, perspective, pseudo
+      # orthogonal projection, where normal equals direction vector
+      return point, direction, None, None, False
+  else:
+    # perspective projection
+    i = numpy.where(abs(numpy.real(w)) > 1e-8)[0]
+    if not len(i):
+      raise ValueError('no eigenvector not corresponding to eigenvalue 0')
+    point = numpy.real(V[:, i[-1]]).squeeze()
+    point /= point[3]
+    normal = -M[3, :3]
+    perspective = M[:3, 3] / numpy.dot(point[:3], normal)
+    if pseudo:
+      perspective -= normal
+    return point, normal, None, perspective, pseudo
 
 
 def clip_matrix(left, right, bottom, top, near, far, perspective=False):
-    """Return matrix to obtain normalized device coordinates from frustum.
+  """Return matrix to obtain normalized device coordinates from frustum.
 
     The frustum bounds are axis-aligned along x (left, right),
     y (bottom, top) and z (near, far).
@@ -429,26 +424,24 @@ def clip_matrix(left, right, bottom, top, near, far, perspective=False):
     array([ 1.,  1., -1.,  1.])
 
     """
-    if left >= right or bottom >= top or near >= far:
-        raise ValueError('invalid frustum')
-    if perspective:
-        if near <= _EPS:
-            raise ValueError('invalid frustum: near <= 0')
-        t = 2.0 * near
-        M = [[t/(left-right), 0.0, (right+left)/(right-left), 0.0],
-             [0.0, t/(bottom-top), (top+bottom)/(top-bottom), 0.0],
-             [0.0, 0.0, (far+near)/(near-far), t*far/(far-near)],
-             [0.0, 0.0, -1.0, 0.0]]
-    else:
-        M = [[2.0/(right-left), 0.0, 0.0, (right+left)/(left-right)],
-             [0.0, 2.0/(top-bottom), 0.0, (top+bottom)/(bottom-top)],
-             [0.0, 0.0, 2.0/(far-near), (far+near)/(near-far)],
-             [0.0, 0.0, 0.0, 1.0]]
-    return numpy.array(M)
+  if left >= right or bottom >= top or near >= far:
+    raise ValueError('invalid frustum')
+  if perspective:
+    if near <= _EPS:
+      raise ValueError('invalid frustum: near <= 0')
+    t = 2.0 * near
+    M = [[t / (left - right), 0.0, (right + left) / (right - left), 0.0],
+         [0.0, t / (bottom - top), (top + bottom) / (top - bottom), 0.0],
+         [0.0, 0.0, (far + near) / (near - far), t * far / (far - near)], [0.0, 0.0, -1.0, 0.0]]
+  else:
+    M = [[2.0 / (right - left), 0.0, 0.0, (right + left) / (left - right)],
+         [0.0, 2.0 / (top - bottom), 0.0, (top + bottom) / (bottom - top)],
+         [0.0, 0.0, 2.0 / (far - near), (far + near) / (near - far)], [0.0, 0.0, 0.0, 1.0]]
+  return numpy.array(M)
 
 
 def shear_matrix(angle, direction, point, normal):
-    """Return matrix to shear by angle along direction vector on shear plane.
+  """Return matrix to shear by angle along direction vector on shear plane.
 
     The shear plane is defined by a point and normal vector. The direction
     vector must be orthogonal to the plane's normal vector.
@@ -467,19 +460,19 @@ def shear_matrix(angle, direction, point, normal):
     True
 
     """
-    normal = unit_vector(normal[:3])
-    direction = unit_vector(direction[:3])
-    if abs(numpy.dot(normal, direction)) > 1e-6:
-        raise ValueError('direction and normal vectors are not orthogonal')
-    angle = math.tan(angle)
-    M = numpy.identity(4)
-    M[:3, :3] += angle * numpy.outer(direction, normal)
-    M[:3, 3] = -angle * numpy.dot(point[:3], normal) * direction
-    return M
+  normal = unit_vector(normal[:3])
+  direction = unit_vector(direction[:3])
+  if abs(numpy.dot(normal, direction)) > 1e-6:
+    raise ValueError('direction and normal vectors are not orthogonal')
+  angle = math.tan(angle)
+  M = numpy.identity(4)
+  M[:3, :3] += angle * numpy.outer(direction, normal)
+  M[:3, 3] = -angle * numpy.dot(point[:3], normal) * direction
+  return M
 
 
 def shear_from_matrix(matrix):
-    """Return shear angle, direction and plane from shear matrix.
+  """Return shear angle, direction and plane from shear matrix.
 
     >>> angle = (random.random() - 0.5) * 4*math.pi
     >>> direct = numpy.random.random(3) - 0.5
@@ -492,39 +485,39 @@ def shear_from_matrix(matrix):
     True
 
     """
-    M = numpy.array(matrix, dtype=numpy.float64, copy=False)
-    M33 = M[:3, :3]
-    # normal: cross independent eigenvectors corresponding to the eigenvalue 1
-    w, V = numpy.linalg.eig(M33)
-    i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-4)[0]
-    if len(i) < 2:
-        raise ValueError('no two linear independent eigenvectors found %s' % w)
-    V = numpy.real(V[:, i]).squeeze().T
-    lenorm = -1.0
-    for i0, i1 in ((0, 1), (0, 2), (1, 2)):
-        n = numpy.cross(V[i0], V[i1])
-        w = vector_norm(n)
-        if w > lenorm:
-            lenorm = w
-            normal = n
-    normal /= lenorm
-    # direction and angle
-    direction = numpy.dot(M33 - numpy.identity(3), normal)
-    angle = vector_norm(direction)
-    direction /= angle
-    angle = math.atan(angle)
-    # point: eigenvector corresponding to eigenvalue 1
-    w, V = numpy.linalg.eig(M)
-    i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0]
-    if not len(i):
-        raise ValueError('no eigenvector corresponding to eigenvalue 1')
-    point = numpy.real(V[:, i[-1]]).squeeze()
-    point /= point[3]
-    return angle, direction, point, normal
+  M = numpy.array(matrix, dtype=numpy.float64, copy=False)
+  M33 = M[:3, :3]
+  # normal: cross independent eigenvectors corresponding to the eigenvalue 1
+  w, V = numpy.linalg.eig(M33)
+  i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-4)[0]
+  if len(i) < 2:
+    raise ValueError('no two linear independent eigenvectors found %s' % w)
+  V = numpy.real(V[:, i]).squeeze().T
+  lenorm = -1.0
+  for i0, i1 in ((0, 1), (0, 2), (1, 2)):
+    n = numpy.cross(V[i0], V[i1])
+    w = vector_norm(n)
+    if w > lenorm:
+      lenorm = w
+      normal = n
+  normal /= lenorm
+  # direction and angle
+  direction = numpy.dot(M33 - numpy.identity(3), normal)
+  angle = vector_norm(direction)
+  direction /= angle
+  angle = math.atan(angle)
+  # point: eigenvector corresponding to eigenvalue 1
+  w, V = numpy.linalg.eig(M)
+  i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0]
+  if not len(i):
+    raise ValueError('no eigenvector corresponding to eigenvalue 1')
+  point = numpy.real(V[:, i[-1]]).squeeze()
+  point /= point[3]
+  return angle, direction, point, normal
 
 
 def decompose_matrix(matrix):
-    """Return sequence of transformations from transformation matrix.
+  """Return sequence of transformations from transformation matrix.
 
     matrix : array_like
         Non-degenerative homogeneous transformation matrix
@@ -554,63 +547,62 @@ def decompose_matrix(matrix):
     True
 
     """
-    M = numpy.array(matrix, dtype=numpy.float64, copy=True).T
-    if abs(M[3, 3]) < _EPS:
-        raise ValueError('M[3, 3] is zero')
-    M /= M[3, 3]
-    P = M.copy()
-    P[:, 3] = 0.0, 0.0, 0.0, 1.0
-    if not numpy.linalg.det(P):
-        raise ValueError('matrix is singular')
-
-    scale = numpy.zeros((3, ))
-    shear = [0.0, 0.0, 0.0]
-    angles = [0.0, 0.0, 0.0]
-
-    if any(abs(M[:3, 3]) > _EPS):
-        perspective = numpy.dot(M[:, 3], numpy.linalg.inv(P.T))
-        M[:, 3] = 0.0, 0.0, 0.0, 1.0
-    else:
-        perspective = numpy.array([0.0, 0.0, 0.0, 1.0])
-
-    translate = M[3, :3].copy()
-    M[3, :3] = 0.0
-
-    row = M[:3, :3].copy()
-    scale[0] = vector_norm(row[0])
-    row[0] /= scale[0]
-    shear[0] = numpy.dot(row[0], row[1])
-    row[1] -= row[0] * shear[0]
-    scale[1] = vector_norm(row[1])
-    row[1] /= scale[1]
-    shear[0] /= scale[1]
-    shear[1] = numpy.dot(row[0], row[2])
-    row[2] -= row[0] * shear[1]
-    shear[2] = numpy.dot(row[1], row[2])
-    row[2] -= row[1] * shear[2]
-    scale[2] = vector_norm(row[2])
-    row[2] /= scale[2]
-    shear[1:] /= scale[2]
-
-    if numpy.dot(row[0], numpy.cross(row[1], row[2])) < 0:
-        numpy.negative(scale, scale)
-        numpy.negative(row, row)
-
-    angles[1] = math.asin(-row[0, 2])
-    if math.cos(angles[1]):
-        angles[0] = math.atan2(row[1, 2], row[2, 2])
-        angles[2] = math.atan2(row[0, 1], row[0, 0])
-    else:
-        # angles[0] = math.atan2(row[1, 0], row[1, 1])
-        angles[0] = math.atan2(-row[2, 1], row[1, 1])
-        angles[2] = 0.0
-
-    return scale, shear, angles, translate, perspective
-
-
-def compose_matrix(scale=None, shear=None, angles=None, translate=None,
-                   perspective=None):
-    """Return transformation matrix from sequence of transformations.
+  M = numpy.array(matrix, dtype=numpy.float64, copy=True).T
+  if abs(M[3, 3]) < _EPS:
+    raise ValueError('M[3, 3] is zero')
+  M /= M[3, 3]
+  P = M.copy()
+  P[:, 3] = 0.0, 0.0, 0.0, 1.0
+  if not numpy.linalg.det(P):
+    raise ValueError('matrix is singular')
+
+  scale = numpy.zeros((3,))
+  shear = [0.0, 0.0, 0.0]
+  angles = [0.0, 0.0, 0.0]
+
+  if any(abs(M[:3, 3]) > _EPS):
+    perspective = numpy.dot(M[:, 3], numpy.linalg.inv(P.T))
+    M[:, 3] = 0.0, 0.0, 0.0, 1.0
+  else:
+    perspective = numpy.array([0.0, 0.0, 0.0, 1.0])
+
+  translate = M[3, :3].copy()
+  M[3, :3] = 0.0
+
+  row = M[:3, :3].copy()
+  scale[0] = vector_norm(row[0])
+  row[0] /= scale[0]
+  shear[0] = numpy.dot(row[0], row[1])
+  row[1] -= row[0] * shear[0]
+  scale[1] = vector_norm(row[1])
+  row[1] /= scale[1]
+  shear[0] /= scale[1]
+  shear[1] = numpy.dot(row[0], row[2])
+  row[2] -= row[0] * shear[1]
+  shear[2] = numpy.dot(row[1], row[2])
+  row[2] -= row[1] * shear[2]
+  scale[2] = vector_norm(row[2])
+  row[2] /= scale[2]
+  shear[1:] /= scale[2]
+
+  if numpy.dot(row[0], numpy.cross(row[1], row[2])) < 0:
+    numpy.negative(scale, scale)
+    numpy.negative(row, row)
+
+  angles[1] = math.asin(-row[0, 2])
+  if math.cos(angles[1]):
+    angles[0] = math.atan2(row[1, 2], row[2, 2])
+    angles[2] = math.atan2(row[0, 1], row[0, 0])
+  else:
+    # angles[0] = math.atan2(row[1, 0], row[1, 1])
+    angles[0] = math.atan2(-row[2, 1], row[1, 1])
+    angles[2] = 0.0
+
+  return scale, shear, angles, translate, perspective
+
+
+def compose_matrix(scale=None, shear=None, angles=None, translate=None, perspective=None):
+  """Return transformation matrix from sequence of transformations.
 
     This is the inverse of the decompose_matrix function.
 
@@ -633,36 +625,36 @@ def compose_matrix(scale=None, shear=None, angles=None, translate=None,
     True
 
     """
-    M = numpy.identity(4)
-    if perspective is not None:
-        P = numpy.identity(4)
-        P[3, :] = perspective[:4]
-        M = numpy.dot(M, P)
-    if translate is not None:
-        T = numpy.identity(4)
-        T[:3, 3] = translate[:3]
-        M = numpy.dot(M, T)
-    if angles is not None:
-        R = euler_matrix(angles[0], angles[1], angles[2], 'sxyz')
-        M = numpy.dot(M, R)
-    if shear is not None:
-        Z = numpy.identity(4)
-        Z[1, 2] = shear[2]
-        Z[0, 2] = shear[1]
-        Z[0, 1] = shear[0]
-        M = numpy.dot(M, Z)
-    if scale is not None:
-        S = numpy.identity(4)
-        S[0, 0] = scale[0]
-        S[1, 1] = scale[1]
-        S[2, 2] = scale[2]
-        M = numpy.dot(M, S)
-    M /= M[3, 3]
-    return M
+  M = numpy.identity(4)
+  if perspective is not None:
+    P = numpy.identity(4)
+    P[3, :] = perspective[:4]
+    M = numpy.dot(M, P)
+  if translate is not None:
+    T = numpy.identity(4)
+    T[:3, 3] = translate[:3]
+    M = numpy.dot(M, T)
+  if angles is not None:
+    R = euler_matrix(angles[0], angles[1], angles[2], 'sxyz')
+    M = numpy.dot(M, R)
+  if shear is not None:
+    Z = numpy.identity(4)
+    Z[1, 2] = shear[2]
+    Z[0, 2] = shear[1]
+    Z[0, 1] = shear[0]
+    M = numpy.dot(M, Z)
+  if scale is not None:
+    S = numpy.identity(4)
+    S[0, 0] = scale[0]
+    S[1, 1] = scale[1]
+    S[2, 2] = scale[2]
+    M = numpy.dot(M, S)
+  M /= M[3, 3]
+  return M
 
 
 def orthogonalization_matrix(lengths, angles):
-    """Return orthogonalization matrix for crystallographic cell coordinates.
+  """Return orthogonalization matrix for crystallographic cell coordinates.
 
     Angles are expected in degrees.
 
@@ -676,20 +668,18 @@ def orthogonalization_matrix(lengths, angles):
     True
 
     """
-    a, b, c = lengths
-    angles = numpy.radians(angles)
-    sina, sinb, _ = numpy.sin(angles)
-    cosa, cosb, cosg = numpy.cos(angles)
-    co = (cosa * cosb - cosg) / (sina * sinb)
-    return numpy.array([
-        [ a*sinb*math.sqrt(1.0-co*co),  0.0,    0.0, 0.0],
-        [-a*sinb*co,                    b*sina, 0.0, 0.0],
-        [ a*cosb,                       b*cosa, c,   0.0],
-        [ 0.0,                          0.0,    0.0, 1.0]])
+  a, b, c = lengths
+  angles = numpy.radians(angles)
+  sina, sinb, _ = numpy.sin(angles)
+  cosa, cosb, cosg = numpy.cos(angles)
+  co = (cosa * cosb - cosg) / (sina * sinb)
+  return numpy.array([[a * sinb * math.sqrt(1.0 - co * co), 0.0, 0.0, 0.0],
+                      [-a * sinb * co, b * sina, 0.0, 0.0], [a * cosb, b * cosa, c, 0.0],
+                      [0.0, 0.0, 0.0, 1.0]])
 
 
 def affine_matrix_from_points(v0, v1, shear=True, scale=True, usesvd=True):
-    """Return affine transform matrix to register two point sets.
+  """Return affine transform matrix to register two point sets.
 
     v0 and v1 are shape (ndims, \*) arrays of at least ndims non-homogeneous
     coordinates, where ndims is the dimensionality of the coordinate space.
@@ -729,76 +719,74 @@ def affine_matrix_from_points(v0, v1, shear=True, scale=True, usesvd=True):
     More examples in superimposition_matrix()
 
     """
-    v0 = numpy.array(v0, dtype=numpy.float64, copy=True)
-    v1 = numpy.array(v1, dtype=numpy.float64, copy=True)
-
-    ndims = v0.shape[0]
-    if ndims < 2 or v0.shape[1] < ndims or v0.shape != v1.shape:
-        raise ValueError('input arrays are of wrong shape or type')
-
-    # move centroids to origin
-    t0 = -numpy.mean(v0, axis=1)
-    M0 = numpy.identity(ndims+1)
-    M0[:ndims, ndims] = t0
-    v0 += t0.reshape(ndims, 1)
-    t1 = -numpy.mean(v1, axis=1)
-    M1 = numpy.identity(ndims+1)
-    M1[:ndims, ndims] = t1
-    v1 += t1.reshape(ndims, 1)
-
-    if shear:
-        # Affine transformation
-        A = numpy.concatenate((v0, v1), axis=0)
-        u, s, vh = numpy.linalg.svd(A.T)
-        vh = vh[:ndims].T
-        B = vh[:ndims]
-        C = vh[ndims:2*ndims]
-        t = numpy.dot(C, numpy.linalg.pinv(B))
-        t = numpy.concatenate((t, numpy.zeros((ndims, 1))), axis=1)
-        M = numpy.vstack((t, ((0.0,)*ndims) + (1.0,)))
-    elif usesvd or ndims != 3:
-        # Rigid transformation via SVD of covariance matrix
-        u, s, vh = numpy.linalg.svd(numpy.dot(v1, v0.T))
-        # rotation matrix from SVD orthonormal bases
-        R = numpy.dot(u, vh)
-        if numpy.linalg.det(R) < 0.0:
-            # R does not constitute right handed system
-            R -= numpy.outer(u[:, ndims-1], vh[ndims-1, :]*2.0)
-            s[-1] *= -1.0
-        # homogeneous transformation matrix
-        M = numpy.identity(ndims+1)
-        M[:ndims, :ndims] = R
-    else:
-        # Rigid transformation matrix via quaternion
-        # compute symmetric matrix N
-        xx, yy, zz = numpy.sum(v0 * v1, axis=1)
-        xy, yz, zx = numpy.sum(v0 * numpy.roll(v1, -1, axis=0), axis=1)
-        xz, yx, zy = numpy.sum(v0 * numpy.roll(v1, -2, axis=0), axis=1)
-        N = [[xx+yy+zz, 0.0,      0.0,      0.0],
-             [yz-zy,    xx-yy-zz, 0.0,      0.0],
-             [zx-xz,    xy+yx,    yy-xx-zz, 0.0],
-             [xy-yx,    zx+xz,    yz+zy,    zz-xx-yy]]
-        # quaternion: eigenvector corresponding to most positive eigenvalue
-        w, V = numpy.linalg.eigh(N)
-        q = V[:, numpy.argmax(w)]
-        q /= vector_norm(q)  # unit quaternion
-        # homogeneous transformation matrix
-        M = quaternion_matrix(q)
-
-    if scale and not shear:
-        # Affine transformation; scale is ratio of RMS deviations from centroid
-        v0 *= v0
-        v1 *= v1
-        M[:ndims, :ndims] *= math.sqrt(numpy.sum(v1) / numpy.sum(v0))
-
-    # move centroids back
-    M = numpy.dot(numpy.linalg.inv(M1), numpy.dot(M, M0))
-    M /= M[ndims, ndims]
-    return M
+  v0 = numpy.array(v0, dtype=numpy.float64, copy=True)
+  v1 = numpy.array(v1, dtype=numpy.float64, copy=True)
+
+  ndims = v0.shape[0]
+  if ndims < 2 or v0.shape[1] < ndims or v0.shape != v1.shape:
+    raise ValueError('input arrays are of wrong shape or type')
+
+  # move centroids to origin
+  t0 = -numpy.mean(v0, axis=1)
+  M0 = numpy.identity(ndims + 1)
+  M0[:ndims, ndims] = t0
+  v0 += t0.reshape(ndims, 1)
+  t1 = -numpy.mean(v1, axis=1)
+  M1 = numpy.identity(ndims + 1)
+  M1[:ndims, ndims] = t1
+  v1 += t1.reshape(ndims, 1)
+
+  if shear:
+    # Affine transformation
+    A = numpy.concatenate((v0, v1), axis=0)
+    u, s, vh = numpy.linalg.svd(A.T)
+    vh = vh[:ndims].T
+    B = vh[:ndims]
+    C = vh[ndims:2 * ndims]
+    t = numpy.dot(C, numpy.linalg.pinv(B))
+    t = numpy.concatenate((t, numpy.zeros((ndims, 1))), axis=1)
+    M = numpy.vstack((t, ((0.0,) * ndims) + (1.0,)))
+  elif usesvd or ndims != 3:
+    # Rigid transformation via SVD of covariance matrix
+    u, s, vh = numpy.linalg.svd(numpy.dot(v1, v0.T))
+    # rotation matrix from SVD orthonormal bases
+    R = numpy.dot(u, vh)
+    if numpy.linalg.det(R) < 0.0:
+      # R does not constitute right handed system
+      R -= numpy.outer(u[:, ndims - 1], vh[ndims - 1, :] * 2.0)
+      s[-1] *= -1.0
+    # homogeneous transformation matrix
+    M = numpy.identity(ndims + 1)
+    M[:ndims, :ndims] = R
+  else:
+    # Rigid transformation matrix via quaternion
+    # compute symmetric matrix N
+    xx, yy, zz = numpy.sum(v0 * v1, axis=1)
+    xy, yz, zx = numpy.sum(v0 * numpy.roll(v1, -1, axis=0), axis=1)
+    xz, yx, zy = numpy.sum(v0 * numpy.roll(v1, -2, axis=0), axis=1)
+    N = [[xx + yy + zz, 0.0, 0.0, 0.0], [yz - zy, xx - yy - zz, 0.0, 0.0],
+         [zx - xz, xy + yx, yy - xx - zz, 0.0], [xy - yx, zx + xz, yz + zy, zz - xx - yy]]
+    # quaternion: eigenvector corresponding to most positive eigenvalue
+    w, V = numpy.linalg.eigh(N)
+    q = V[:, numpy.argmax(w)]
+    q /= vector_norm(q)  # unit quaternion
+    # homogeneous transformation matrix
+    M = quaternion_matrix(q)
+
+  if scale and not shear:
+    # Affine transformation; scale is ratio of RMS deviations from centroid
+    v0 *= v0
+    v1 *= v1
+    M[:ndims, :ndims] *= math.sqrt(numpy.sum(v1) / numpy.sum(v0))
+
+  # move centroids back
+  M = numpy.dot(numpy.linalg.inv(M1), numpy.dot(M, M0))
+  M /= M[ndims, ndims]
+  return M
 
 
 def superimposition_matrix(v0, v1, scale=False, usesvd=True):
-    """Return matrix to transform given 3D point set into second point set.
+  """Return matrix to transform given 3D point set into second point set.
 
     v0 and v1 are shape (3, \*) or (4, \*) arrays of at least 3 points.
 
@@ -842,14 +830,13 @@ def superimposition_matrix(v0, v1, scale=False, usesvd=True):
     True
 
     """
-    v0 = numpy.array(v0, dtype=numpy.float64, copy=False)[:3]
-    v1 = numpy.array(v1, dtype=numpy.float64, copy=False)[:3]
-    return affine_matrix_from_points(v0, v1, shear=False,
-                                     scale=scale, usesvd=usesvd)
+  v0 = numpy.array(v0, dtype=numpy.float64, copy=False)[:3]
+  v1 = numpy.array(v1, dtype=numpy.float64, copy=False)[:3]
+  return affine_matrix_from_points(v0, v1, shear=False, scale=scale, usesvd=usesvd)
 
 
 def euler_matrix(ai, aj, ak, axes='sxyz'):
-    """Return homogeneous rotation matrix from Euler angles and axis sequence.
+  """Return homogeneous rotation matrix from Euler angles and axis sequence.
 
     ai, aj, ak : Euler's roll, pitch and yaw angles
     axes : One of 24 axis sequences as string or encoded tuple
@@ -867,52 +854,52 @@ def euler_matrix(ai, aj, ak, axes='sxyz'):
     ...    R = euler_matrix(ai, aj, ak, axes)
 
     """
-    try:
-        firstaxis, parity, repetition, frame = _AXES2TUPLE[axes]
-    except (AttributeError, KeyError):
-        _TUPLE2AXES[axes]  # noqa: validation
-        firstaxis, parity, repetition, frame = axes
-
-    i = firstaxis
-    j = _NEXT_AXIS[i+parity]
-    k = _NEXT_AXIS[i-parity+1]
-
-    if frame:
-        ai, ak = ak, ai
-    if parity:
-        ai, aj, ak = -ai, -aj, -ak
-
-    si, sj, sk = math.sin(ai), math.sin(aj), math.sin(ak)
-    ci, cj, ck = math.cos(ai), math.cos(aj), math.cos(ak)
-    cc, cs = ci*ck, ci*sk
-    sc, ss = si*ck, si*sk
-
-    M = numpy.identity(4)
-    if repetition:
-        M[i, i] = cj
-        M[i, j] = sj*si
-        M[i, k] = sj*ci
-        M[j, i] = sj*sk
-        M[j, j] = -cj*ss+cc
-        M[j, k] = -cj*cs-sc
-        M[k, i] = -sj*ck
-        M[k, j] = cj*sc+cs
-        M[k, k] = cj*cc-ss
-    else:
-        M[i, i] = cj*ck
-        M[i, j] = sj*sc-cs
-        M[i, k] = sj*cc+ss
-        M[j, i] = cj*sk
-        M[j, j] = sj*ss+cc
-        M[j, k] = sj*cs-sc
-        M[k, i] = -sj
-        M[k, j] = cj*si
-        M[k, k] = cj*ci
-    return M
+  try:
+    firstaxis, parity, repetition, frame = _AXES2TUPLE[axes]
+  except (AttributeError, KeyError):
+    _TUPLE2AXES[axes]  # noqa: validation
+    firstaxis, parity, repetition, frame = axes
+
+  i = firstaxis
+  j = _NEXT_AXIS[i + parity]
+  k = _NEXT_AXIS[i - parity + 1]
+
+  if frame:
+    ai, ak = ak, ai
+  if parity:
+    ai, aj, ak = -ai, -aj, -ak
+
+  si, sj, sk = math.sin(ai), math.sin(aj), math.sin(ak)
+  ci, cj, ck = math.cos(ai), math.cos(aj), math.cos(ak)
+  cc, cs = ci * ck, ci * sk
+  sc, ss = si * ck, si * sk
+
+  M = numpy.identity(4)
+  if repetition:
+    M[i, i] = cj
+    M[i, j] = sj * si
+    M[i, k] = sj * ci
+    M[j, i] = sj * sk
+    M[j, j] = -cj * ss + cc
+    M[j, k] = -cj * cs - sc
+    M[k, i] = -sj * ck
+    M[k, j] = cj * sc + cs
+    M[k, k] = cj * cc - ss
+  else:
+    M[i, i] = cj * ck
+    M[i, j] = sj * sc - cs
+    M[i, k] = sj * cc + ss
+    M[j, i] = cj * sk
+    M[j, j] = sj * ss + cc
+    M[j, k] = sj * cs - sc
+    M[k, i] = -sj
+    M[k, j] = cj * si
+    M[k, k] = cj * ci
+  return M
 
 
 def euler_from_matrix(matrix, axes='sxyz'):
-    """Return Euler angles from rotation matrix for specified axis sequence.
+  """Return Euler angles from rotation matrix for specified axis sequence.
 
     axes : One of 24 axis sequences as string or encoded tuple
 
@@ -930,58 +917,58 @@ def euler_from_matrix(matrix, axes='sxyz'):
     ...    if not numpy.allclose(R0, R1): print(axes, "failed")
 
     """
-    try:
-        firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()]
-    except (AttributeError, KeyError):
-        _TUPLE2AXES[axes]  # noqa: validation
-        firstaxis, parity, repetition, frame = axes
-
-    i = firstaxis
-    j = _NEXT_AXIS[i+parity]
-    k = _NEXT_AXIS[i-parity+1]
-
-    M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:3, :3]
-    if repetition:
-        sy = math.sqrt(M[i, j]*M[i, j] + M[i, k]*M[i, k])
-        if sy > _EPS:
-            ax = math.atan2( M[i, j],  M[i, k])
-            ay = math.atan2( sy,       M[i, i])
-            az = math.atan2( M[j, i], -M[k, i])
-        else:
-            ax = math.atan2(-M[j, k],  M[j, j])
-            ay = math.atan2( sy,       M[i, i])
-            az = 0.0
+  try:
+    firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()]
+  except (AttributeError, KeyError):
+    _TUPLE2AXES[axes]  # noqa: validation
+    firstaxis, parity, repetition, frame = axes
+
+  i = firstaxis
+  j = _NEXT_AXIS[i + parity]
+  k = _NEXT_AXIS[i - parity + 1]
+
+  M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:3, :3]
+  if repetition:
+    sy = math.sqrt(M[i, j] * M[i, j] + M[i, k] * M[i, k])
+    if sy > _EPS:
+      ax = math.atan2(M[i, j], M[i, k])
+      ay = math.atan2(sy, M[i, i])
+      az = math.atan2(M[j, i], -M[k, i])
+    else:
+      ax = math.atan2(-M[j, k], M[j, j])
+      ay = math.atan2(sy, M[i, i])
+      az = 0.0
+  else:
+    cy = math.sqrt(M[i, i] * M[i, i] + M[j, i] * M[j, i])
+    if cy > _EPS:
+      ax = math.atan2(M[k, j], M[k, k])
+      ay = math.atan2(-M[k, i], cy)
+      az = math.atan2(M[j, i], M[i, i])
     else:
-        cy = math.sqrt(M[i, i]*M[i, i] + M[j, i]*M[j, i])
-        if cy > _EPS:
-            ax = math.atan2( M[k, j],  M[k, k])
-            ay = math.atan2(-M[k, i],  cy)
-            az = math.atan2( M[j, i],  M[i, i])
-        else:
-            ax = math.atan2(-M[j, k],  M[j, j])
-            ay = math.atan2(-M[k, i],  cy)
-            az = 0.0
-
-    if parity:
-        ax, ay, az = -ax, -ay, -az
-    if frame:
-        ax, az = az, ax
-    return ax, ay, az
+      ax = math.atan2(-M[j, k], M[j, j])
+      ay = math.atan2(-M[k, i], cy)
+      az = 0.0
+
+  if parity:
+    ax, ay, az = -ax, -ay, -az
+  if frame:
+    ax, az = az, ax
+  return ax, ay, az
 
 
 def euler_from_quaternion(quaternion, axes='sxyz'):
-    """Return Euler angles from quaternion for specified axis sequence.
+  """Return Euler angles from quaternion for specified axis sequence.
 
     >>> angles = euler_from_quaternion([0.99810947, 0.06146124, 0, 0])
     >>> numpy.allclose(angles, [0.123, 0, 0])
     True
 
     """
-    return euler_from_matrix(quaternion_matrix(quaternion), axes)
+  return euler_from_matrix(quaternion_matrix(quaternion), axes)
 
 
 def quaternion_from_euler(ai, aj, ak, axes='sxyz'):
-    """Return quaternion from Euler angles and axis sequence.
+  """Return quaternion from Euler angles and axis sequence.
 
     ai, aj, ak : Euler's roll, pitch and yaw angles
     axes : One of 24 axis sequences as string or encoded tuple
@@ -991,70 +978,70 @@ def quaternion_from_euler(ai, aj, ak, axes='sxyz'):
     True
 
     """
-    try:
-        firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()]
-    except (AttributeError, KeyError):
-        _TUPLE2AXES[axes]  # noqa: validation
-        firstaxis, parity, repetition, frame = axes
-
-    i = firstaxis + 1
-    j = _NEXT_AXIS[i+parity-1] + 1
-    k = _NEXT_AXIS[i-parity] + 1
-
-    if frame:
-        ai, ak = ak, ai
-    if parity:
-        aj = -aj
-
-    ai /= 2.0
-    aj /= 2.0
-    ak /= 2.0
-    ci = math.cos(ai)
-    si = math.sin(ai)
-    cj = math.cos(aj)
-    sj = math.sin(aj)
-    ck = math.cos(ak)
-    sk = math.sin(ak)
-    cc = ci*ck
-    cs = ci*sk
-    sc = si*ck
-    ss = si*sk
-
-    q = numpy.empty((4, ))
-    if repetition:
-        q[0] = cj*(cc - ss)
-        q[i] = cj*(cs + sc)
-        q[j] = sj*(cc + ss)
-        q[k] = sj*(cs - sc)
-    else:
-        q[0] = cj*cc + sj*ss
-        q[i] = cj*sc - sj*cs
-        q[j] = cj*ss + sj*cc
-        q[k] = cj*cs - sj*sc
-    if parity:
-        q[j] *= -1.0
-
-    return q
+  try:
+    firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()]
+  except (AttributeError, KeyError):
+    _TUPLE2AXES[axes]  # noqa: validation
+    firstaxis, parity, repetition, frame = axes
+
+  i = firstaxis + 1
+  j = _NEXT_AXIS[i + parity - 1] + 1
+  k = _NEXT_AXIS[i - parity] + 1
+
+  if frame:
+    ai, ak = ak, ai
+  if parity:
+    aj = -aj
+
+  ai /= 2.0
+  aj /= 2.0
+  ak /= 2.0
+  ci = math.cos(ai)
+  si = math.sin(ai)
+  cj = math.cos(aj)
+  sj = math.sin(aj)
+  ck = math.cos(ak)
+  sk = math.sin(ak)
+  cc = ci * ck
+  cs = ci * sk
+  sc = si * ck
+  ss = si * sk
+
+  q = numpy.empty((4,))
+  if repetition:
+    q[0] = cj * (cc - ss)
+    q[i] = cj * (cs + sc)
+    q[j] = sj * (cc + ss)
+    q[k] = sj * (cs - sc)
+  else:
+    q[0] = cj * cc + sj * ss
+    q[i] = cj * sc - sj * cs
+    q[j] = cj * ss + sj * cc
+    q[k] = cj * cs - sj * sc
+  if parity:
+    q[j] *= -1.0
+
+  return q
 
 
 def quaternion_about_axis(angle, axis):
-    """Return quaternion for rotation about axis.
+  """Return quaternion for rotation about axis.
 
     >>> q = quaternion_about_axis(0.123, [1, 0, 0])
     >>> numpy.allclose(q, [0.99810947, 0.06146124, 0, 0])
     True
 
     """
-    q = numpy.array([0.0, axis[0], axis[1], axis[2]])
-    qlen = vector_norm(q)
-    if qlen > _EPS:
-        q *= math.sin(angle/2.0) / qlen
-    q[0] = math.cos(angle/2.0)
-    return q
+  q = numpy.array([0.0, axis[0], axis[1], axis[2]])
+  qlen = vector_norm(q)
+  if qlen > _EPS:
+    q *= math.sin(angle / 2.0) / qlen
+  q[0] = math.cos(angle / 2.0)
+  return q
 
 
 def quaternion_matrix(quaternion):
-    """Return homogeneous rotation matrix from quaternion.
+  """Return homogeneous rotation matrix from quaternion.
 
     >>> M = quaternion_matrix([0.99810947, 0.06146124, 0, 0])
     >>> numpy.allclose(M, rotation_matrix(0.123, [1, 0, 0]))
@@ -1067,21 +1054,20 @@ def quaternion_matrix(quaternion):
     True
 
     """
-    q = numpy.array(quaternion, dtype=numpy.float64, copy=True)
-    n = numpy.dot(q, q)
-    if n < _EPS:
-        return numpy.identity(4)
-    q *= math.sqrt(2.0 / n)
-    q = numpy.outer(q, q)
-    return numpy.array([
-        [1.0-q[2, 2]-q[3, 3],     q[1, 2]-q[3, 0],     q[1, 3]+q[2, 0], 0.0],
-        [    q[1, 2]+q[3, 0], 1.0-q[1, 1]-q[3, 3],     q[2, 3]-q[1, 0], 0.0],
-        [    q[1, 3]-q[2, 0],     q[2, 3]+q[1, 0], 1.0-q[1, 1]-q[2, 2], 0.0],
-        [                0.0,                 0.0,                 0.0, 1.0]])
+  q = numpy.array(quaternion, dtype=numpy.float64, copy=True)
+  n = numpy.dot(q, q)
+  if n < _EPS:
+    return numpy.identity(4)
+  q *= math.sqrt(2.0 / n)
+  q = numpy.outer(q, q)
+  return numpy.array([[1.0 - q[2, 2] - q[3, 3], q[1, 2] - q[3, 0], q[1, 3] + q[2, 0], 0.0],
+                      [q[1, 2] + q[3, 0], 1.0 - q[1, 1] - q[3, 3], q[2, 3] - q[1, 0], 0.0],
+                      [q[1, 3] - q[2, 0], q[2, 3] + q[1, 0], 1.0 - q[1, 1] - q[2, 2], 0.0],
+                      [0.0, 0.0, 0.0, 1.0]])
 
 
 def quaternion_from_matrix(matrix, isprecise=False):
-    """Return quaternion from rotation matrix.
+  """Return quaternion from rotation matrix.
 
     If isprecise is True, the input matrix is assumed to be a precise rotation
     matrix and a faster algorithm is used.
@@ -1119,71 +1105,70 @@ def quaternion_from_matrix(matrix, isprecise=False):
     True
 
     """
-    M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:4, :4]
-    if isprecise:
-        q = numpy.empty((4, ))
-        t = numpy.trace(M)
-        if t > M[3, 3]:
-            q[0] = t
-            q[3] = M[1, 0] - M[0, 1]
-            q[2] = M[0, 2] - M[2, 0]
-            q[1] = M[2, 1] - M[1, 2]
-        else:
-            i, j, k = 0, 1, 2
-            if M[1, 1] > M[0, 0]:
-                i, j, k = 1, 2, 0
-            if M[2, 2] > M[i, i]:
-                i, j, k = 2, 0, 1
-            t = M[i, i] - (M[j, j] + M[k, k]) + M[3, 3]
-            q[i] = t
-            q[j] = M[i, j] + M[j, i]
-            q[k] = M[k, i] + M[i, k]
-            q[3] = M[k, j] - M[j, k]
-            q = q[[3, 0, 1, 2]]
-        q *= 0.5 / math.sqrt(t * M[3, 3])
+  M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:4, :4]
+  if isprecise:
+    q = numpy.empty((4,))
+    t = numpy.trace(M)
+    if t > M[3, 3]:
+      q[0] = t
+      q[3] = M[1, 0] - M[0, 1]
+      q[2] = M[0, 2] - M[2, 0]
+      q[1] = M[2, 1] - M[1, 2]
     else:
-        m00 = M[0, 0]
-        m01 = M[0, 1]
-        m02 = M[0, 2]
-        m10 = M[1, 0]
-        m11 = M[1, 1]
-        m12 = M[1, 2]
-        m20 = M[2, 0]
-        m21 = M[2, 1]
-        m22 = M[2, 2]
-        # symmetric matrix K
-        K = numpy.array([[m00-m11-m22, 0.0,         0.0,         0.0],
-                         [m01+m10,     m11-m00-m22, 0.0,         0.0],
-                         [m02+m20,     m12+m21,     m22-m00-m11, 0.0],
-                         [m21-m12,     m02-m20,     m10-m01,     m00+m11+m22]])
-        K /= 3.0
-        # quaternion is eigenvector of K that corresponds to largest eigenvalue
-        w, V = numpy.linalg.eigh(K)
-        q = V[[3, 0, 1, 2], numpy.argmax(w)]
-    if q[0] < 0.0:
-        numpy.negative(q, q)
-    return q
+      i, j, k = 0, 1, 2
+      if M[1, 1] > M[0, 0]:
+        i, j, k = 1, 2, 0
+      if M[2, 2] > M[i, i]:
+        i, j, k = 2, 0, 1
+      t = M[i, i] - (M[j, j] + M[k, k]) + M[3, 3]
+      q[i] = t
+      q[j] = M[i, j] + M[j, i]
+      q[k] = M[k, i] + M[i, k]
+      q[3] = M[k, j] - M[j, k]
+      q = q[[3, 0, 1, 2]]
+    q *= 0.5 / math.sqrt(t * M[3, 3])
+  else:
+    m00 = M[0, 0]
+    m01 = M[0, 1]
+    m02 = M[0, 2]
+    m10 = M[1, 0]
+    m11 = M[1, 1]
+    m12 = M[1, 2]
+    m20 = M[2, 0]
+    m21 = M[2, 1]
+    m22 = M[2, 2]
+    # symmetric matrix K
+    K = numpy.array([[m00 - m11 - m22, 0.0, 0.0, 0.0], [m01 + m10, m11 - m00 - m22, 0.0, 0.0],
+                     [m02 + m20, m12 + m21, m22 - m00 - m11, 0.0],
+                     [m21 - m12, m02 - m20, m10 - m01, m00 + m11 + m22]])
+    K /= 3.0
+    # quaternion is eigenvector of K that corresponds to largest eigenvalue
+    w, V = numpy.linalg.eigh(K)
+    q = V[[3, 0, 1, 2], numpy.argmax(w)]
+  if q[0] < 0.0:
+    numpy.negative(q, q)
+  return q
 
 
 def quaternion_multiply(quaternion1, quaternion0):
-    """Return multiplication of two quaternions.
+  """Return multiplication of two quaternions.
 
     >>> q = quaternion_multiply([4, 1, -2, 3], [8, -5, 6, 7])
     >>> numpy.allclose(q, [28, -44, -14, 48])
     True
 
     """
-    w0, x0, y0, z0 = quaternion0
-    w1, x1, y1, z1 = quaternion1
-    return numpy.array([
-        -x1*x0 - y1*y0 - z1*z0 + w1*w0,
-        x1*w0 + y1*z0 - z1*y0 + w1*x0,
-        -x1*z0 + y1*w0 + z1*x0 + w1*y0,
-        x1*y0 - y1*x0 + z1*w0 + w1*z0], dtype=numpy.float64)
+  w0, x0, y0, z0 = quaternion0
+  w1, x1, y1, z1 = quaternion1
+  return numpy.array([
+      -x1 * x0 - y1 * y0 - z1 * z0 + w1 * w0, x1 * w0 + y1 * z0 - z1 * y0 + w1 * x0,
+      -x1 * z0 + y1 * w0 + z1 * x0 + w1 * y0, x1 * y0 - y1 * x0 + z1 * w0 + w1 * z0
+  ],
+                     dtype=numpy.float64)
 
 
 def quaternion_conjugate(quaternion):
-    """Return conjugate of quaternion.
+  """Return conjugate of quaternion.
 
     >>> q0 = random_quaternion()
     >>> q1 = quaternion_conjugate(q0)
@@ -1191,13 +1176,13 @@ def quaternion_conjugate(quaternion):
     True
 
     """
-    q = numpy.array(quaternion, dtype=numpy.float64, copy=True)
-    numpy.negative(q[1:], q[1:])
-    return q
+  q = numpy.array(quaternion, dtype=numpy.float64, copy=True)
+  numpy.negative(q[1:], q[1:])
+  return q
 
 
 def quaternion_inverse(quaternion):
-    """Return inverse of quaternion.
+  """Return inverse of quaternion.
 
     >>> q0 = random_quaternion()
     >>> q1 = quaternion_inverse(q0)
@@ -1205,33 +1190,33 @@ def quaternion_inverse(quaternion):
     True
 
     """
-    q = numpy.array(quaternion, dtype=numpy.float64, copy=True)
-    numpy.negative(q[1:], q[1:])
-    return q / numpy.dot(q, q)
+  q = numpy.array(quaternion, dtype=numpy.float64, copy=True)
+  numpy.negative(q[1:], q[1:])
+  return q / numpy.dot(q, q)
 
 
 def quaternion_real(quaternion):
-    """Return real part of quaternion.
+  """Return real part of quaternion.
 
     >>> quaternion_real([3, 0, 1, 2])
     3.0
 
     """
-    return float(quaternion[0])
+  return float(quaternion[0])
 
 
 def quaternion_imag(quaternion):
-    """Return imaginary part of quaternion.
+  """Return imaginary part of quaternion.
 
     >>> quaternion_imag([3, 0, 1, 2])
     array([ 0.,  1.,  2.])
 
     """
-    return numpy.array(quaternion[1:4], dtype=numpy.float64, copy=True)
+  return numpy.array(quaternion[1:4], dtype=numpy.float64, copy=True)
 
 
 def quaternion_slerp(quat0, quat1, fraction, spin=0, shortestpath=True):
-    """Return spherical linear interpolation between two quaternions.
+  """Return spherical linear interpolation between two quaternions.
 
     >>> q0 = random_quaternion()
     >>> q1 = random_quaternion()
@@ -1248,31 +1233,31 @@ def quaternion_slerp(quat0, quat1, fraction, spin=0, shortestpath=True):
     True
 
     """
-    q0 = unit_vector(quat0[:4])
-    q1 = unit_vector(quat1[:4])
-    if fraction == 0.0:
-        return q0
-    elif fraction == 1.0:
-        return q1
-    d = numpy.dot(q0, q1)
-    if abs(abs(d) - 1.0) < _EPS:
-        return q0
-    if shortestpath and d < 0.0:
-        # invert rotation
-        d = -d
-        numpy.negative(q1, q1)
-    angle = math.acos(d) + spin * math.pi
-    if abs(angle) < _EPS:
-        return q0
-    isin = 1.0 / math.sin(angle)
-    q0 *= math.sin((1.0 - fraction) * angle) * isin
-    q1 *= math.sin(fraction * angle) * isin
-    q0 += q1
+  q0 = unit_vector(quat0[:4])
+  q1 = unit_vector(quat1[:4])
+  if fraction == 0.0:
+    return q0
+  elif fraction == 1.0:
+    return q1
+  d = numpy.dot(q0, q1)
+  if abs(abs(d) - 1.0) < _EPS:
+    return q0
+  if shortestpath and d < 0.0:
+    # invert rotation
+    d = -d
+    numpy.negative(q1, q1)
+  angle = math.acos(d) + spin * math.pi
+  if abs(angle) < _EPS:
     return q0
+  isin = 1.0 / math.sin(angle)
+  q0 *= math.sin((1.0 - fraction) * angle) * isin
+  q1 *= math.sin(fraction * angle) * isin
+  q0 += q1
+  return q0
 
 
 def random_quaternion(rand=None):
-    """Return uniform random unit quaternion.
+  """Return uniform random unit quaternion.
 
     rand: array like or None
         Three independent random variables that are uniformly distributed
@@ -1286,21 +1271,24 @@ def random_quaternion(rand=None):
     (1, True)
 
     """
-    if rand is None:
-        rand = numpy.random.rand(3)
-    else:
-        assert len(rand) == 3
-    r1 = numpy.sqrt(1.0 - rand[0])
-    r2 = numpy.sqrt(rand[0])
-    pi2 = math.pi * 2.0
-    t1 = pi2 * rand[1]
-    t2 = pi2 * rand[2]
-    return numpy.array([numpy.cos(t2)*r2, numpy.sin(t1)*r1,
-                        numpy.cos(t1)*r1, numpy.sin(t2)*r2])
+  if rand is None:
+    rand = numpy.random.rand(3)
+  else:
+    assert len(rand) == 3
+  r1 = numpy.sqrt(1.0 - rand[0])
+  r2 = numpy.sqrt(rand[0])
+  pi2 = math.pi * 2.0
+  t1 = pi2 * rand[1]
+  t2 = pi2 * rand[2]
+  return numpy.array(
+      [numpy.cos(t2) * r2,
+       numpy.sin(t1) * r1,
+       numpy.cos(t1) * r1,
+       numpy.sin(t2) * r2])
 
 
 def random_rotation_matrix(rand=None):
-    """Return uniform random rotation matrix.
+  """Return uniform random rotation matrix.
 
     rand: array like
         Three independent random variables that are uniformly distributed
@@ -1311,7 +1299,7 @@ def random_rotation_matrix(rand=None):
     True
 
     """
-    return quaternion_matrix(random_quaternion(rand))
+  return quaternion_matrix(random_quaternion(rand))
 
 
 # epsilon for testing whether a number is close to zero
@@ -1322,20 +1310,37 @@ _NEXT_AXIS = [1, 2, 0, 1]
 
 # map axes strings to/from tuples of inner axis, parity, repetition, frame
 _AXES2TUPLE = {
-    'sxyz': (0, 0, 0, 0), 'sxyx': (0, 0, 1, 0), 'sxzy': (0, 1, 0, 0),
-    'sxzx': (0, 1, 1, 0), 'syzx': (1, 0, 0, 0), 'syzy': (1, 0, 1, 0),
-    'syxz': (1, 1, 0, 0), 'syxy': (1, 1, 1, 0), 'szxy': (2, 0, 0, 0),
-    'szxz': (2, 0, 1, 0), 'szyx': (2, 1, 0, 0), 'szyz': (2, 1, 1, 0),
-    'rzyx': (0, 0, 0, 1), 'rxyx': (0, 0, 1, 1), 'ryzx': (0, 1, 0, 1),
-    'rxzx': (0, 1, 1, 1), 'rxzy': (1, 0, 0, 1), 'ryzy': (1, 0, 1, 1),
-    'rzxy': (1, 1, 0, 1), 'ryxy': (1, 1, 1, 1), 'ryxz': (2, 0, 0, 1),
-    'rzxz': (2, 0, 1, 1), 'rxyz': (2, 1, 0, 1), 'rzyz': (2, 1, 1, 1)}
+    'sxyz': (0, 0, 0, 0),
+    'sxyx': (0, 0, 1, 0),
+    'sxzy': (0, 1, 0, 0),
+    'sxzx': (0, 1, 1, 0),
+    'syzx': (1, 0, 0, 0),
+    'syzy': (1, 0, 1, 0),
+    'syxz': (1, 1, 0, 0),
+    'syxy': (1, 1, 1, 0),
+    'szxy': (2, 0, 0, 0),
+    'szxz': (2, 0, 1, 0),
+    'szyx': (2, 1, 0, 0),
+    'szyz': (2, 1, 1, 0),
+    'rzyx': (0, 0, 0, 1),
+    'rxyx': (0, 0, 1, 1),
+    'ryzx': (0, 1, 0, 1),
+    'rxzx': (0, 1, 1, 1),
+    'rxzy': (1, 0, 0, 1),
+    'ryzy': (1, 0, 1, 1),
+    'rzxy': (1, 1, 0, 1),
+    'ryxy': (1, 1, 1, 1),
+    'ryxz': (2, 0, 0, 1),
+    'rzxz': (2, 0, 1, 1),
+    'rxyz': (2, 1, 0, 1),
+    'rzyz': (2, 1, 1, 1)
+}
 
 _TUPLE2AXES = dict((v, k) for k, v in _AXES2TUPLE.items())
 
 
 def vector_norm(data, axis=None, out=None):
-    """Return length, i.e. Euclidean norm, of ndarray along axis.
+  """Return length, i.e. Euclidean norm, of ndarray along axis.
 
     >>> v = numpy.random.random(3)
     >>> n = vector_norm(v)
@@ -1359,22 +1364,22 @@ def vector_norm(data, axis=None, out=None):
     1.0
 
     """
-    data = numpy.array(data, dtype=numpy.float64, copy=True)
-    if out is None:
-        if data.ndim == 1:
-            return math.sqrt(numpy.dot(data, data))
-        data *= data
-        out = numpy.atleast_1d(numpy.sum(data, axis=axis))
-        numpy.sqrt(out, out)
-        return out
-    else:
-        data *= data
-        numpy.sum(data, axis=axis, out=out)
-        numpy.sqrt(out, out)
+  data = numpy.array(data, dtype=numpy.float64, copy=True)
+  if out is None:
+    if data.ndim == 1:
+      return math.sqrt(numpy.dot(data, data))
+    data *= data
+    out = numpy.atleast_1d(numpy.sum(data, axis=axis))
+    numpy.sqrt(out, out)
+    return out
+  else:
+    data *= data
+    numpy.sum(data, axis=axis, out=out)
+    numpy.sqrt(out, out)
 
 
 def unit_vector(data, axis=None, out=None):
-    """Return ndarray normalized by length, i.e. Euclidean norm, along axis.
+  """Return ndarray normalized by length, i.e. Euclidean norm, along axis.
 
     >>> v0 = numpy.random.random(3)
     >>> v1 = unit_vector(v0)
@@ -1399,26 +1404,26 @@ def unit_vector(data, axis=None, out=None):
     [1.0]
 
     """
-    if out is None:
-        data = numpy.array(data, dtype=numpy.float64, copy=True)
-        if data.ndim == 1:
-            data /= math.sqrt(numpy.dot(data, data))
-            return data
-    else:
-        if out is not data:
-            out[:] = numpy.array(data, copy=False)
-        data = out
-    length = numpy.atleast_1d(numpy.sum(data*data, axis))
-    numpy.sqrt(length, length)
-    if axis is not None:
-        length = numpy.expand_dims(length, axis)
-    data /= length
-    if out is None:
-        return data
+  if out is None:
+    data = numpy.array(data, dtype=numpy.float64, copy=True)
+    if data.ndim == 1:
+      data /= math.sqrt(numpy.dot(data, data))
+      return data
+  else:
+    if out is not data:
+      out[:] = numpy.array(data, copy=False)
+    data = out
+  length = numpy.atleast_1d(numpy.sum(data * data, axis))
+  numpy.sqrt(length, length)
+  if axis is not None:
+    length = numpy.expand_dims(length, axis)
+  data /= length
+  if out is None:
+    return data
 
 
 def random_vector(size):
-    """Return array of random doubles in the half-open interval [0.0, 1.0).
+  """Return array of random doubles in the half-open interval [0.0, 1.0).
 
     >>> v = random_vector(10000)
     >>> numpy.all(v >= 0) and numpy.all(v < 1)
@@ -1429,11 +1434,11 @@ def random_vector(size):
     False
 
     """
-    return numpy.random.random(size)
+  return numpy.random.random(size)
 
 
 def vector_product(v0, v1, axis=0):
-    """Return vector perpendicular to vectors.
+  """Return vector perpendicular to vectors.
 
     >>> v = vector_product([2, 0, 0], [0, 3, 0])
     >>> numpy.allclose(v, [0, 0, 6])
@@ -1450,11 +1455,11 @@ def vector_product(v0, v1, axis=0):
     True
 
     """
-    return numpy.cross(v0, v1, axis=axis)
+  return numpy.cross(v0, v1, axis=axis)
 
 
 def angle_between_vectors(v0, v1, directed=True, axis=0):
-    """Return angle between vectors.
+  """Return angle between vectors.
 
     If directed is False, the input vectors are interpreted as undirected axes,
     i.e. the maximum angle is pi/2.
@@ -1477,16 +1482,16 @@ def angle_between_vectors(v0, v1, directed=True, axis=0):
     True
 
     """
-    v0 = numpy.array(v0, dtype=numpy.float64, copy=False)
-    v1 = numpy.array(v1, dtype=numpy.float64, copy=False)
-    dot = numpy.sum(v0 * v1, axis=axis)
-    dot /= vector_norm(v0, axis=axis) * vector_norm(v1, axis=axis)
-    dot = numpy.clip(dot, -1.0, 1.0)
-    return numpy.arccos(dot if directed else numpy.fabs(dot))
+  v0 = numpy.array(v0, dtype=numpy.float64, copy=False)
+  v1 = numpy.array(v1, dtype=numpy.float64, copy=False)
+  dot = numpy.sum(v0 * v1, axis=axis)
+  dot /= vector_norm(v0, axis=axis) * vector_norm(v1, axis=axis)
+  dot = numpy.clip(dot, -1.0, 1.0)
+  return numpy.arccos(dot if directed else numpy.fabs(dot))
 
 
 def inverse_matrix(matrix):
-    """Return inverse of square transformation matrix.
+  """Return inverse of square transformation matrix.
 
     >>> M0 = random_rotation_matrix()
     >>> M1 = inverse_matrix(M0.T)
@@ -1498,11 +1503,11 @@ def inverse_matrix(matrix):
     ...     if not numpy.allclose(M1, numpy.linalg.inv(M0)): print(size)
 
     """
-    return numpy.linalg.inv(matrix)
+  return numpy.linalg.inv(matrix)
 
 
 def concatenate_matrices(*matrices):
-    """Return concatenation of series of transformation matrices.
+  """Return concatenation of series of transformation matrices.
 
     >>> M = numpy.random.rand(16).reshape((4, 4)) - 0.5
     >>> numpy.allclose(M, concatenate_matrices(M))
@@ -1511,14 +1516,14 @@ def concatenate_matrices(*matrices):
     True
 
     """
-    M = numpy.identity(4)
-    for i in matrices:
-        M = numpy.dot(M, i)
-    return M
+  M = numpy.identity(4)
+  for i in matrices:
+    M = numpy.dot(M, i)
+  return M
 
 
 def is_same_transform(matrix0, matrix1):
-    """Return True if two matrices perform same transformation.
+  """Return True if two matrices perform same transformation.
 
     >>> is_same_transform(numpy.identity(4), numpy.identity(4))
     True
@@ -1526,22 +1531,22 @@ def is_same_transform(matrix0, matrix1):
     False
 
     """
-    matrix0 = numpy.array(matrix0, dtype=numpy.float64, copy=True)
-    matrix0 /= matrix0[3, 3]
-    matrix1 = numpy.array(matrix1, dtype=numpy.float64, copy=True)
-    matrix1 /= matrix1[3, 3]
-    return numpy.allclose(matrix0, matrix1)
+  matrix0 = numpy.array(matrix0, dtype=numpy.float64, copy=True)
+  matrix0 /= matrix0[3, 3]
+  matrix1 = numpy.array(matrix1, dtype=numpy.float64, copy=True)
+  matrix1 /= matrix1[3, 3]
+  return numpy.allclose(matrix0, matrix1)
 
 
 def is_same_quaternion(q0, q1):
-    """Return True if two quaternions are equal."""
-    q0 = numpy.array(q0)
-    q1 = numpy.array(q1)
-    return numpy.allclose(q0, q1) or numpy.allclose(q0, -q1)
+  """Return True if two quaternions are equal."""
+  q0 = numpy.array(q0)
+  q1 = numpy.array(q1)
+  return numpy.allclose(q0, q1) or numpy.allclose(q0, -q1)
 
 
 def _import_module(name, package=None, warn=True, postfix='_py', ignore='_'):
-    """Try import all public attributes from module into global namespace.
+  """Try import all public attributes from module into global namespace.
 
     Existing attributes with name clashes are renamed with prefix.
     Attributes starting with underscore are ignored by default.
@@ -1549,32 +1554,31 @@ def _import_module(name, package=None, warn=True, postfix='_py', ignore='_'):
     Return True on successful import.
 
     """
-    import warnings
-    from importlib import import_module
-    try:
-        if not package:
-            module = import_module(name)
-        else:
-            module = import_module('.' + name, package=package)
-    except ImportError as err:
-        if warn:
-            warnings.warn(str(err))
+  import warnings
+  from importlib import import_module
+  try:
+    if not package:
+      module = import_module(name)
     else:
-        for attr in dir(module):
-            if ignore and attr.startswith(ignore):
-                continue
-            if postfix:
-                if attr in globals():
-                    globals()[attr + postfix] = globals()[attr]
-                elif warn:
-                    warnings.warn('no Python implementation of ' + attr)
-            globals()[attr] = getattr(module, attr)
-        return True
+      module = import_module('.' + name, package=package)
+  except ImportError as err:
+    if warn:
+      warnings.warn(str(err))
+  else:
+    for attr in dir(module):
+      if ignore and attr.startswith(ignore):
+        continue
+      if postfix:
+        if attr in globals():
+          globals()[attr + postfix] = globals()[attr]
+        elif warn:
+          warnings.warn('no Python implementation of ' + attr)
+      globals()[attr] = getattr(module, attr)
+    return True
 
 
 # _import_module('_transformations', __package__)
 
-
 # if __name__ == '__main__':
 #     import doctest
 #     import random  # noqa: used in doctests
@@ -1582,4 +1586,4 @@ def _import_module(name, package=None, warn=True, postfix='_py', ignore='_'):
 #         numpy.set_printoptions(suppress=True, precision=5, legacy='1.13')
 #     except TypeError:
 #         numpy.set_printoptions(suppress=True, precision=5)
-#     doctest.testmod()
\ No newline at end of file
+#     doctest.testmod()
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/mpi_run.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/mpi_run.py
index 85d70abb1..0d30f5d5e 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/mpi_run.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/mpi_run.py
@@ -3,21 +3,23 @@ import subprocess
 from pybullet_utils.arg_parser import ArgParser
 from pybullet_utils.logger import Logger
 
+
 def main():
-    # Command line arguments
-    args = sys.argv[1:]
-    arg_parser = ArgParser()
-    arg_parser.load_args(args)
+  # Command line arguments
+  args = sys.argv[1:]
+  arg_parser = ArgParser()
+  arg_parser.load_args(args)
+
+  num_workers = arg_parser.parse_int('num_workers', 1)
+  assert (num_workers > 0)
 
-    num_workers = arg_parser.parse_int('num_workers', 1)
-    assert(num_workers > 0)
+  Logger.print2('Running with {:d} workers'.format(num_workers))
+  cmd = 'mpiexec -n {:d} python3 DeepMimic_Optimizer.py '.format(num_workers)
+  cmd += ' '.join(args)
+  Logger.print2('cmd: ' + cmd)
+  subprocess.call(cmd, shell=True)
+  return
 
-    Logger.print2('Running with {:d} workers'.format(num_workers))
-    cmd = 'mpiexec -n {:d} python3 DeepMimic_Optimizer.py '.format(num_workers)
-    cmd += ' '.join(args)
-    Logger.print2('cmd: ' + cmd)
-    subprocess.call(cmd, shell=True)
-    return
 
 if __name__ == '__main__':
-    main()
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/testrl.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/testrl.py
index 38a9469e9..1693fe7d4 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/testrl.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/testrl.py
@@ -2,8 +2,8 @@ import os
 import inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
-print("parentdir=",parentdir)
+os.sys.path.insert(0, parentdir)
+print("parentdir=", parentdir)
 import json
 from pybullet_envs.deep_mimic.learning.rl_world import RLWorld
 from pybullet_envs.deep_mimic.learning.ppo_agent import PPOAgent
@@ -15,81 +15,78 @@ from pybullet_envs.deep_mimic.env.pybullet_deep_mimic_env import PyBulletDeepMim
 import sys
 import random
 
-update_timestep = 1./240.
+update_timestep = 1. / 240.
 animating = True
 
+
 def update_world(world, time_elapsed):
-    timeStep = update_timestep
-    world.update(timeStep)
-    reward = world.env.calc_reward(agent_id=0)
-    #print("reward=",reward)
-    end_episode = world.env.is_episode_end()
-    if (end_episode):
-      world.end_episode()
-      world.reset()
-    return
+  timeStep = update_timestep
+  world.update(timeStep)
+  reward = world.env.calc_reward(agent_id=0)
+  #print("reward=",reward)
+  end_episode = world.env.is_episode_end()
+  if (end_episode):
+    world.end_episode()
+    world.reset()
+  return
+
 
 def build_arg_parser(args):
-    arg_parser = ArgParser()
-    arg_parser.load_args(args)
+  arg_parser = ArgParser()
+  arg_parser.load_args(args)
 
-    arg_file = arg_parser.parse_string('arg_file', '')
-    if (arg_file != ''):
-        path = pybullet_data.getDataPath()+"/args/"+arg_file
-        succ = arg_parser.load_file(path)
-        Logger.print2(arg_file)
-        assert succ, Logger.print2('Failed to load args from: ' + arg_file)
-    return arg_parser
+  arg_file = arg_parser.parse_string('arg_file', '')
+  if (arg_file != ''):
+    path = pybullet_data.getDataPath() + "/args/" + arg_file
+    succ = arg_parser.load_file(path)
+    Logger.print2(arg_file)
+    assert succ, Logger.print2('Failed to load args from: ' + arg_file)
+  return arg_parser
 
-args = sys.argv[1:]
 
+args = sys.argv[1:]
 
 
 def build_world(args, enable_draw):
-    arg_parser = build_arg_parser(args)
-    print("enable_draw=",enable_draw)
-    env = PyBulletDeepMimicEnv(arg_parser, enable_draw)
-    world = RLWorld(env, arg_parser)
-    #world.env.set_playback_speed(playback_speed)
-
-    motion_file = arg_parser.parse_string("motion_file")
-    print("motion_file=",motion_file)
-    bodies = arg_parser.parse_ints("fall_contact_bodies")
-    print("bodies=",bodies)
-    int_output_path = arg_parser.parse_string("int_output_path")
-    print("int_output_path=",int_output_path)
-    agent_files = pybullet_data.getDataPath()+"/"+arg_parser.parse_string("agent_files")
-
-    AGENT_TYPE_KEY = "AgentType"
-
-    print("agent_file=",agent_files)
-    with open(agent_files) as data_file:
-        json_data = json.load(data_file)
-        print("json_data=",json_data)
-        assert AGENT_TYPE_KEY in json_data
-        agent_type = json_data[AGENT_TYPE_KEY]
-        print("agent_type=",agent_type)
-        agent = PPOAgent(world, id, json_data)
-    
-        agent.set_enable_training(False)
-        world.reset()
-    return world    
-
-
-
-  
-    
+  arg_parser = build_arg_parser(args)
+  print("enable_draw=", enable_draw)
+  env = PyBulletDeepMimicEnv(arg_parser, enable_draw)
+  world = RLWorld(env, arg_parser)
+  #world.env.set_playback_speed(playback_speed)
+
+  motion_file = arg_parser.parse_string("motion_file")
+  print("motion_file=", motion_file)
+  bodies = arg_parser.parse_ints("fall_contact_bodies")
+  print("bodies=", bodies)
+  int_output_path = arg_parser.parse_string("int_output_path")
+  print("int_output_path=", int_output_path)
+  agent_files = pybullet_data.getDataPath() + "/" + arg_parser.parse_string("agent_files")
+
+  AGENT_TYPE_KEY = "AgentType"
+
+  print("agent_file=", agent_files)
+  with open(agent_files) as data_file:
+    json_data = json.load(data_file)
+    print("json_data=", json_data)
+    assert AGENT_TYPE_KEY in json_data
+    agent_type = json_data[AGENT_TYPE_KEY]
+    print("agent_type=", agent_type)
+    agent = PPOAgent(world, id, json_data)
+
+    agent.set_enable_training(False)
+    world.reset()
+  return world
+
+
 if __name__ == '__main__':
-    
-    world = build_world(args, True)
-    while (world.env._pybullet_client.isConnected()):
-      timeStep = update_timestep
-      keys = world.env.getKeyboardEvents()
-      
-        
-      if world.env.isKeyTriggered(keys, ' '):
-        animating = not animating
-      if (animating):
-        update_world(world, timeStep)
-        #animating=False
 
+  world = build_world(args, True)
+  while (world.env._pybullet_client.isConnected()):
+    timeStep = update_timestep
+    keys = world.env.getKeyboardEvents()
+
+    if world.env.isKeyTriggered(keys, ' '):
+      animating = not animating
+    if (animating):
+      update_world(world, timeStep)
+      #animating=False
diff --git a/examples/pybullet/gym/pybullet_envs/env_bases.py b/examples/pybullet/gym/pybullet_envs/env_bases.py
index aeef7fda5..a352ebe4c 100644
--- a/examples/pybullet/gym/pybullet_envs/env_bases.py
+++ b/examples/pybullet/gym/pybullet_envs/env_bases.py
@@ -5,134 +5,134 @@ from pybullet_utils import bullet_client
 
 from pkg_resources import parse_version
 
+
 class MJCFBaseBulletEnv(gym.Env):
-	"""
+  """
 	Base class for Bullet physics simulation loading MJCF (MuJoCo .xml) environments in a Scene.
 	These environments create single-player scenes and behave like normal Gym environments, if
 	you don't use multiplayer.
 	"""
 
-	metadata = {
-		'render.modes': ['human', 'rgb_array'],
-		'video.frames_per_second': 60
-		}
-
-	def __init__(self, robot, render=False):
-		self.scene = None
-		self.physicsClientId = -1
-		self.ownsPhysicsClient = 0
-		self.camera = Camera()
-		self.isRender = render
-		self.robot = robot
-		self.seed()
-		self._cam_dist = 3
-		self._cam_yaw = 0
-		self._cam_pitch = -30
-		self._render_width =320
-		self._render_height = 240
-
-		self.action_space = robot.action_space
-		self.observation_space = robot.observation_space
-
-	def configure(self, args):
-		self.robot.args = args
-
-	def seed(self, seed=None):
-		self.np_random, seed = gym.utils.seeding.np_random(seed)
-		self.robot.np_random = self.np_random # use the same np_randomizer for robot as for env
-		return [seed]
-
-	def reset(self):
-		if (self.physicsClientId<0):
-			self.ownsPhysicsClient = True
-
-			if self.isRender:
-				self._p = bullet_client.BulletClient(connection_mode=pybullet.GUI)
-			else:
-				self._p = bullet_client.BulletClient()
-
-			self.physicsClientId = self._p._client
-			self._p.configureDebugVisualizer(pybullet.COV_ENABLE_GUI,0)
-
-		if self.scene is None:
-			self.scene = self.create_single_player_scene(self._p)
-		if not self.scene.multiplayer and self.ownsPhysicsClient:
-			self.scene.episode_restart(self._p)
-
-		self.robot.scene = self.scene
-
-		self.frame = 0
-		self.done = 0
-		self.reward = 0
-		dump = 0
-		s = self.robot.reset(self._p)
-		self.potential = self.robot.calc_potential()
-		return s
-
-	def render(self, mode='human', close=False):
-		if mode == "human":
-			self.isRender = True
-		if mode != "rgb_array":
-			return np.array([])
-
-		base_pos=[0,0,0]
-		if (hasattr(self,'robot')):
-			if (hasattr(self.robot,'body_xyz')):
-				base_pos = self.robot.body_xyz
-
-		view_matrix = self._p.computeViewMatrixFromYawPitchRoll(
-			cameraTargetPosition=base_pos,
-			distance=self._cam_dist,
-			yaw=self._cam_yaw,
-			pitch=self._cam_pitch,
-			roll=0,
-			upAxisIndex=2)
-		proj_matrix = self._p.computeProjectionMatrixFOV(
-			fov=60, aspect=float(self._render_width)/self._render_height,
-			nearVal=0.1, farVal=100.0)
-		(_, _, px, _, _) = self._p.getCameraImage(
-		width=self._render_width, height=self._render_height, viewMatrix=view_matrix,
-			projectionMatrix=proj_matrix,
-			renderer=pybullet.ER_BULLET_HARDWARE_OPENGL
-			)
-		rgb_array = np.array(px)
-		rgb_array = np.reshape(np.array(px), (self._render_height, self._render_width, -1))
-		rgb_array = rgb_array[:, :, :3]
-		return rgb_array
-
-
-	def close(self):
-		if (self.ownsPhysicsClient):
-			if (self.physicsClientId>=0):
-				self._p.disconnect()
-		self.physicsClientId = -1
-
-	def HUD(self, state, a, done):
-		pass
-
-	# def step(self, *args, **kwargs):
-	# 	if self.isRender:
-	# 		base_pos=[0,0,0]
-	# 		if (hasattr(self,'robot')):
-	# 			if (hasattr(self.robot,'body_xyz')):
-	# 				base_pos = self.robot.body_xyz
-	# 				# Keep the previous orientation of the camera set by the user.
-	# 				#[yaw, pitch, dist] = self._p.getDebugVisualizerCamera()[8:11]
-	# 				self._p.resetDebugVisualizerCamera(3,0,0, base_pos)
-	#
-	#
-	# 	return self.step(*args, **kwargs)
-	if parse_version(gym.__version__) < parse_version('0.9.6'):
-		_render = render
-		_reset = reset
-		_seed = seed
+  metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 60}
+
+  def __init__(self, robot, render=False):
+    self.scene = None
+    self.physicsClientId = -1
+    self.ownsPhysicsClient = 0
+    self.camera = Camera()
+    self.isRender = render
+    self.robot = robot
+    self.seed()
+    self._cam_dist = 3
+    self._cam_yaw = 0
+    self._cam_pitch = -30
+    self._render_width = 320
+    self._render_height = 240
+
+    self.action_space = robot.action_space
+    self.observation_space = robot.observation_space
+
+  def configure(self, args):
+    self.robot.args = args
+
+  def seed(self, seed=None):
+    self.np_random, seed = gym.utils.seeding.np_random(seed)
+    self.robot.np_random = self.np_random  # use the same np_randomizer for robot as for env
+    return [seed]
+
+  def reset(self):
+    if (self.physicsClientId < 0):
+      self.ownsPhysicsClient = True
+
+      if self.isRender:
+        self._p = bullet_client.BulletClient(connection_mode=pybullet.GUI)
+      else:
+        self._p = bullet_client.BulletClient()
+
+      self.physicsClientId = self._p._client
+      self._p.configureDebugVisualizer(pybullet.COV_ENABLE_GUI, 0)
+
+    if self.scene is None:
+      self.scene = self.create_single_player_scene(self._p)
+    if not self.scene.multiplayer and self.ownsPhysicsClient:
+      self.scene.episode_restart(self._p)
+
+    self.robot.scene = self.scene
+
+    self.frame = 0
+    self.done = 0
+    self.reward = 0
+    dump = 0
+    s = self.robot.reset(self._p)
+    self.potential = self.robot.calc_potential()
+    return s
+
+  def render(self, mode='human', close=False):
+    if mode == "human":
+      self.isRender = True
+    if mode != "rgb_array":
+      return np.array([])
+
+    base_pos = [0, 0, 0]
+    if (hasattr(self, 'robot')):
+      if (hasattr(self.robot, 'body_xyz')):
+        base_pos = self.robot.body_xyz
+
+    view_matrix = self._p.computeViewMatrixFromYawPitchRoll(cameraTargetPosition=base_pos,
+                                                            distance=self._cam_dist,
+                                                            yaw=self._cam_yaw,
+                                                            pitch=self._cam_pitch,
+                                                            roll=0,
+                                                            upAxisIndex=2)
+    proj_matrix = self._p.computeProjectionMatrixFOV(fov=60,
+                                                     aspect=float(self._render_width) /
+                                                     self._render_height,
+                                                     nearVal=0.1,
+                                                     farVal=100.0)
+    (_, _, px, _, _) = self._p.getCameraImage(width=self._render_width,
+                                              height=self._render_height,
+                                              viewMatrix=view_matrix,
+                                              projectionMatrix=proj_matrix,
+                                              renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
+    rgb_array = np.array(px)
+    rgb_array = np.reshape(np.array(px), (self._render_height, self._render_width, -1))
+    rgb_array = rgb_array[:, :, :3]
+    return rgb_array
+
+  def close(self):
+    if (self.ownsPhysicsClient):
+      if (self.physicsClientId >= 0):
+        self._p.disconnect()
+    self.physicsClientId = -1
+
+  def HUD(self, state, a, done):
+    pass
+
+  # def step(self, *args, **kwargs):
+  # 	if self.isRender:
+  # 		base_pos=[0,0,0]
+  # 		if (hasattr(self,'robot')):
+  # 			if (hasattr(self.robot,'body_xyz')):
+  # 				base_pos = self.robot.body_xyz
+  # 				# Keep the previous orientation of the camera set by the user.
+  # 				#[yaw, pitch, dist] = self._p.getDebugVisualizerCamera()[8:11]
+  # 				self._p.resetDebugVisualizerCamera(3,0,0, base_pos)
+  #
+  #
+  # 	return self.step(*args, **kwargs)
+  if parse_version(gym.__version__) < parse_version('0.9.6'):
+    _render = render
+    _reset = reset
+    _seed = seed
+
 
 class Camera:
-	def __init__(self):
-		pass
-
-	def move_and_look_at(self,i,j,k,x,y,z):
-		lookat = [x,y,z]
-		distance = 10
-		yaw = 10
-		self._p.resetDebugVisualizerCamera(distance, yaw, -20, lookat)
+
+  def __init__(self):
+    pass
+
+  def move_and_look_at(self, i, j, k, x, y, z):
+    lookat = [x, y, z]
+    distance = 10
+    yaw = 10
+    self._p.resetDebugVisualizerCamera(distance, yaw, -20, lookat)
diff --git a/examples/pybullet/gym/pybullet_envs/examples/dominoes.py b/examples/pybullet/gym/pybullet_envs/examples/dominoes.py
index c96c07ebd..e27f6aec9 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/dominoes.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/dominoes.py
@@ -7,31 +7,38 @@ import time
 p = bc.BulletClient(connection_mode=pybullet.GUI)
 p.setAdditionalSearchPath(pd.getDataPath())
 p.loadURDF("plane_transparent.urdf", useMaximalCoordinates=True)
-p#.setPhysicsEngineParameter(numSolverIterations=10, fixedTimeStep=0.01)
-
-
-p.configureDebugVisualizer(p.COV_ENABLE_PLANAR_REFLECTION,1)
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,0)
-
-y2z = p.getQuaternionFromEuler([0,0,1.57])
-meshScale = [1,1,1]
-visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,fileName="domino/domino.obj", rgbaColor=[1,1,1,1], specularColor=[0.4,.4,0], visualFrameOrientation=y2z, meshScale=meshScale)
-
-boxDimensions = [0.5*0.00635, 0.5*0.0254, 0.5*0.0508]
-collisionShapeId = p.createCollisionShape(p.GEOM_BOX,halfExtents=boxDimensions)
-
-
-for j in range (12):
-  print("j=",j)
-  for i in range (35):
+p  #.setPhysicsEngineParameter(numSolverIterations=10, fixedTimeStep=0.01)
+
+p.configureDebugVisualizer(p.COV_ENABLE_PLANAR_REFLECTION, 1)
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)
+
+y2z = p.getQuaternionFromEuler([0, 0, 1.57])
+meshScale = [1, 1, 1]
+visualShapeId = p.createVisualShape(shapeType=p.GEOM_MESH,
+                                    fileName="domino/domino.obj",
+                                    rgbaColor=[1, 1, 1, 1],
+                                    specularColor=[0.4, .4, 0],
+                                    visualFrameOrientation=y2z,
+                                    meshScale=meshScale)
+
+boxDimensions = [0.5 * 0.00635, 0.5 * 0.0254, 0.5 * 0.0508]
+collisionShapeId = p.createCollisionShape(p.GEOM_BOX, halfExtents=boxDimensions)
+
+for j in range(12):
+  print("j=", j)
+  for i in range(35):
     #p.loadURDF("domino/domino.urdf",[i*0.04,0, 0.06])
-    p.createMultiBody(baseMass=0.025,baseCollisionShapeIndex = collisionShapeId,baseVisualShapeIndex = visualShapeId, basePosition = [i*0.04,j*0.05, 0.06], useMaximalCoordinates=True)
-  
-p.configureDebugVisualizer(p.COV_ENABLE_RENDERING,1)
+    p.createMultiBody(baseMass=0.025,
+                      baseCollisionShapeIndex=collisionShapeId,
+                      baseVisualShapeIndex=visualShapeId,
+                      basePosition=[i * 0.04, j * 0.05, 0.06],
+                      useMaximalCoordinates=True)
+
+p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
 
-p.setGravity(0,0,-9.8)
+p.setGravity(0, 0, -9.8)
 p.setRealTimeSimulation(1)
 while (1):
-  p.setGravity(0,0,-9.8)
+  p.setGravity(0, 0, -9.8)
   #p.stepSimulation(1./100.)
-  time.sleep(1./240.)
\ No newline at end of file
+  time.sleep(1. / 240.)
diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_AntBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_AntBulletEnv_v0_2017may.py
index 6b1379c16..9fc5d7962 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_AntBulletEnv_v0_2017may.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_AntBulletEnv_v0_2017may.py
@@ -3,300 +3,2167 @@ import os
 import inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 import pybullet
 import gym
 import numpy as np
 import pybullet_envs
 import time
 
+
 def relu(x):
-    return np.maximum(x, 0)
+  return np.maximum(x, 0)
+
 
 class SmallReactivePolicy:
-    "Simple multi-layer perceptron policy, no internal state"
-    def __init__(self, observation_space, action_space):
-        assert weights_dense1_w.shape == (observation_space.shape[0], 128)
-        assert weights_dense2_w.shape == (128, 64)
-        assert weights_final_w.shape  == (64, action_space.shape[0])
+  "Simple multi-layer perceptron policy, no internal state"
+
+  def __init__(self, observation_space, action_space):
+    assert weights_dense1_w.shape == (observation_space.shape[0], 128)
+    assert weights_dense2_w.shape == (128, 64)
+    assert weights_final_w.shape == (64, action_space.shape[0])
+
+  def act(self, ob):
+    x = ob
+    x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
+    x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
+    x = np.dot(x, weights_final_w) + weights_final_b
+    return x
 
-    def act(self, ob):
-        x = ob
-        x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
-        x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
-        x = np.dot(x, weights_final_w) + weights_final_b
-        return x
 
 def main():
-    pybullet.connect(pybullet.DIRECT)
-    env = gym.make("AntBulletEnv-v0")
-    env.render(mode="human")
-    
-    pi = SmallReactivePolicy(env.observation_space, env.action_space)
-    env.reset()
+  pybullet.connect(pybullet.DIRECT)
+  env = gym.make("AntBulletEnv-v0")
+  env.render(mode="human")
+
+  pi = SmallReactivePolicy(env.observation_space, env.action_space)
+  env.reset()
+
+  while 1:
+    frame = 0
+    score = 0
+    restart_delay = 0
+    obs = env.reset()
 
     while 1:
-        frame = 0
-        score = 0
-        restart_delay = 0
-        obs = env.reset()
-        
-        while 1:
-            time.sleep(1./60.)
-            a = pi.act(obs)
-            obs, r, done, _ = env.step(a)
-            #print("reward")
-            #print(r)
-            score += r
-            frame += 1
-            distance=5
-            yaw = 0
+      time.sleep(1. / 60.)
+      a = pi.act(obs)
+      obs, r, done, _ = env.step(a)
+      #print("reward")
+      #print(r)
+      score += r
+      frame += 1
+      distance = 5
+      yaw = 0
 
-            still_open = env.render("human")
-            if still_open==False:
-                return
-            if not done: continue
-            if restart_delay==0:
-                print("score=%0.2f in %i frames" % (score, frame))
-                restart_delay = 60*2  # 2 sec at 60 fps
-            else:
-                restart_delay -= 1
-                if restart_delay==0: break
+      still_open = env.render("human")
+      if still_open == False:
+        return
+      if not done: continue
+      if restart_delay == 0:
+        print("score=%0.2f in %i frames" % (score, frame))
+        restart_delay = 60 * 2  # 2 sec at 60 fps
+      else:
+        restart_delay -= 1
+        if restart_delay == 0: break
 
-weights_dense1_w = np.array([
-[ +0.2856, -0.4091, +0.3380, -0.1083, +0.1049, -0.0806, -0.0827, +0.0402, +0.2415, +0.0927, +0.1812, +0.5455, +0.3157, +0.1043, +0.0527, +0.3768, -0.2083, +0.1218, -0.0633, +0.3236, +0.2198, +0.0513, +0.3505, -0.1165, -0.1732, +0.2760, +0.0006, +0.0249, +0.5887, -0.2324, +0.1503, -0.0108, +0.1220, +0.2527, +0.0984, +0.5360, -0.0579, -0.0847, +0.1649, +0.0589, -0.2046, +0.5981, -0.1063, -0.1088, +0.3424, +0.3479, -0.1751, +0.2924, -0.0610, +0.0807, +0.3291, +0.0059, +0.0339, -0.2823, +0.2533, +0.3156, +0.1679, +0.2417, +0.1265, +0.0024, +0.0802, +0.2531, -0.2576, +0.3894, +0.3206, +0.2015, +0.3482, +0.1042, -0.2418, -0.0002, +0.1277, +0.1811, +0.1551, +0.5460, +0.1714, +0.1021, -0.1252, +0.0609, -0.0372, -0.0351, +0.1216, +0.0023, -0.2448, +0.0077, +0.0584, +0.2389, -0.0848, +0.3542, +0.3065, -0.2268, +0.2387, +0.3303, +0.4184, -0.1619, +0.2230, +0.2829, +0.3884, +0.1262, +0.6383, -0.1629, +0.3087, +0.0554, +0.2294, +0.0280, +0.4139, -0.1484, +0.1358, +0.3153, -0.2652, +0.2637, -0.1563, +0.0706, +0.4192, +0.2381, -0.3540, +0.2789, +0.2647, +0.0931, +0.1439, +0.3415, -0.2445, +0.1039, -0.3692, +0.5095, +0.0010, +0.0777, +0.3382, -0.1100],
-[ +0.0820, +0.6327, +1.0135, -0.1423, -0.0275, +0.0396, -0.5933, +0.3027, -0.2220, +0.1476, -0.0591, -0.7910, -0.0776, +0.2565, +0.0241, -1.1300, +0.1324, +0.9079, +0.2318, +0.5574, +1.0162, +0.1408, +0.7525, +0.3612, +0.6088, -0.2541, +0.8134, +0.1348, -0.1265, +0.7991, -0.2304, -0.4826, -0.4123, +0.2831, -0.5457, -0.3518, -0.9733, -0.1654, +0.2409, +0.3971, +0.6624, +0.0728, +0.2200, -0.2126, -0.5299, -0.1858, -0.3467, -0.2776, -0.2004, -0.7567, +0.4417, -0.4378, -0.6799, +0.3789, +0.2937, -0.1247, +0.5540, -1.0124, -0.5759, +0.0308, +0.2971, +1.1300, -0.1185, +0.5303, +0.0527, +0.3392, +0.7436, -0.3578, -0.0559, +0.2165, -0.5860, +0.9427, -0.2522, -0.0805, +0.9314, +1.4173, +0.6216, +0.6923, +0.6592, -0.0457, +0.5152, -0.3523, -0.5135, +0.6466, -0.4804, +0.0006, +0.3223, -0.1548, +1.5508, +0.6204, +0.2618, -0.0392, -0.6208, +0.0237, +0.0577, -0.2913, -0.3893, -0.1459, -0.1103, +0.7485, -0.0045, +0.0640, +0.0726, -0.0966, +0.0452, +0.2741, -0.3389, +0.8830, +0.9819, +0.3915, -0.1127, -0.2653, -0.6842, +0.2389, -0.4349, +1.4013, -0.4442, +1.1846, -0.7550, +0.5812, -0.2940, +0.7046, +0.4730, -0.2155, +0.1432, +0.1518, +0.5336, -0.0827],
-[ -0.2503, +0.2354, -0.0468, +0.1318, -0.0161, +0.1235, -0.1649, -0.1380, -0.1816, -0.0953, -0.0303, -0.4337, +0.0159, -0.2893, -0.1178, -0.1779, +0.0557, -0.0788, -0.1745, -0.1349, +0.1942, +0.1707, -0.3289, +0.1177, -0.3084, +0.0423, +0.0006, -0.1240, -0.2685, -0.0578, +0.3282, -0.3570, +0.0039, +0.3399, -0.3599, -0.3346, -0.2273, -0.0044, -0.0425, -0.2483, -0.2284, -0.5528, -0.2406, -0.0144, -0.0306, +0.1037, +0.0497, -0.3302, +0.0505, +0.0054, -0.2037, -0.0112, +0.1068, -0.1273, +0.1019, -0.0463, -0.0527, -0.0015, -0.1546, -0.2187, +0.0633, -0.1969, -0.0954, -0.0111, -0.5329, +0.1480, -0.1007, +0.1115, -0.0692, -0.3348, +0.1361, -0.1606, -0.1103, -0.3221, -0.2678, -0.1543, -0.4900, -0.2455, -0.0798, -0.0545, +0.0848, +0.0149, -0.1908, +0.1721, +0.0687, -0.2002, -0.1194, +0.1023, -0.3005, -0.2030, -0.0681, -0.2440, -0.2892, -0.4305, -0.2357, +0.0932, -0.4055, -0.0629, -0.1043, -0.5354, -0.1120, -0.1867, -0.1219, -0.2403, -0.5562, -0.1247, -0.5158, -0.0720, -0.2935, -0.2138, +0.1382, +0.3068, -0.1444, +0.0100, -0.3312, -0.6305, +0.0030, -0.2372, -0.0005, -0.3775, +0.1123, +0.0166, -0.0000, -0.0605, -0.5071, +0.0482, -0.1440, -0.0311],
-[ -0.2697, +0.0706, +0.3032, -0.1729, +0.3361, -0.0424, -0.1621, -0.1890, +0.0724, -0.4940, +0.0174, +0.2439, -0.4449, +0.2452, -0.2110, +0.2058, -0.3416, -0.0957, +0.0431, +0.2706, +0.0075, -0.2335, -0.4697, -0.4996, -0.0722, -0.2389, -0.0721, -0.2428, +0.4497, +0.1403, +0.0491, +0.3252, -0.3800, -0.2324, +0.2014, +0.4212, +0.4495, -0.2843, +0.2277, +0.0246, -0.6231, -0.1313, -0.5353, -0.3726, -0.1220, -0.6917, -0.2465, -0.1089, -0.2945, +0.5820, -0.2255, +0.0518, +0.1999, +0.0492, +0.1157, -0.0289, -0.7021, +0.2170, +0.2487, -0.1803, -0.0788, -0.7116, +0.2452, +0.0250, +0.5466, -0.6420, -0.0797, -0.5767, -0.1020, +0.3390, -0.1549, +0.4149, +0.1835, +0.8112, -0.3514, +0.3155, -0.6475, -0.2191, +0.1377, +0.0273, -0.0859, +0.0621, -0.9109, -0.3396, +0.3122, -0.2752, -0.3375, -0.2920, +0.1554, -0.6032, -0.3321, +0.1136, -0.0712, -0.3951, +0.2268, -0.2402, +0.4850, +0.1052, +0.5524, -0.4844, +0.5053, +0.0377, -0.1097, +0.2861, -0.1864, -0.3226, -0.0154, -0.2144, -0.6764, -1.0064, -0.2201, -0.3494, -0.1009, -0.0346, +0.0222, -0.4648, -0.6888, +0.0936, -0.7490, +0.2389, -0.0461, -0.0490, -0.4350, -0.5020, -0.6949, +0.3147, +0.3905, -0.4302],
-[ -0.5894, -0.0462, -0.0671, +0.1779, -0.2985, -0.0093, +0.1025, -0.2851, -0.5022, +0.4617, -0.0050, -0.4186, -0.4120, +0.5342, -0.4466, -0.5710, -0.0778, +0.5455, +0.4230, -0.1727, +0.0248, +0.6199, -0.0249, -0.0536, -0.2538, -0.0548, -0.5637, +0.1861, -0.3756, +0.6562, -0.0309, -0.7059, -0.5196, -0.6593, -0.0786, -0.4255, -0.2951, -0.3396, +0.2999, +0.2590, +0.5004, +0.3659, -0.1944, -0.5518, -0.0093, +0.3915, -0.0654, +0.7140, -0.0118, -0.3306, +0.4059, -0.5650, -0.4346, +0.8268, -0.2418, -0.6769, -0.1529, -0.1019, -0.0935, -0.4321, +0.3257, -0.2702, -0.0989, +0.2711, -0.6210, +0.4389, +0.3887, -0.1652, -0.0982, +0.0433, -0.1120, +0.2608, +0.1155, -0.3326, -0.5094, +0.0474, +0.1239, -0.2204, -0.1056, -0.6978, +0.1862, -0.2300, -0.1165, +0.1509, +0.7566, -0.5543, +0.3839, -0.5120, +0.3099, -0.2034, +0.7276, +0.2385, -0.7288, -0.1266, -0.2343, -0.5672, -0.0057, -0.0186, -0.3665, -1.2855, +0.6746, -0.1203, -0.3309, -0.8603, +0.4811, -0.1538, +0.5446, +0.0038, +0.1556, +0.5776, -0.1994, -0.3110, -0.4141, +0.0772, +0.1195, +0.4368, +0.3796, +0.3990, -0.3452, +0.0268, -0.0037, -0.2803, +0.7420, -0.5205, +0.7103, +0.0814, +0.0917, +0.5727],
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-])
 
-weights_dense1_b = np.array([ -0.0148, -0.0390, -0.2168, -0.0866, -0.0677, -0.1575, +0.0077, -0.1385, -0.0953, -0.2318, -0.1402, -0.0267, -0.0565, -0.1655, -0.0944, -0.1856, +0.1183, -0.0606, -0.1517, -0.1972, -0.0256, -0.1761, -0.2543, -0.1125, -0.1902, -0.1462, +0.0160, -0.2130, -0.1644, +0.0530, -0.0384, -0.1030, -0.1822, -0.0219, -0.1597, -0.1946, -0.1177, -0.1970, -0.1569, -0.2162, -0.0256, -0.2618, -0.2127, -0.0910, -0.1862, -0.0517, -0.0811, -0.2137, +0.0810, -0.2098, -0.2763, -0.0980, -0.0986, -0.0983, -0.2186, -0.1723, -0.0822, -0.1072, -0.1466, -0.3015, +0.2120, -0.0178, +0.0700, -0.1122, -0.2641, -0.1767, -0.0722, +0.0859, -0.3453, -0.2988, -0.0838, -0.1928, -0.0916, -0.1749, -0.0969, -0.2420, -0.0859, -0.1487, -0.0692, -0.0028, -0.0989, -0.1322, -0.0582, -0.2906, -0.1255, -0.0026, +0.0352, -0.2485, -0.0592, -0.0695, -0.1047, -0.1616, -0.2268, -0.1474, -0.2683, +0.1161, -0.1794, -0.1011, -0.1924, +0.0348, -0.0169, -0.3281, -0.2085, -0.2750, -0.3329, -0.1793, -0.2296, -0.2289, -0.1589, -0.3225, +0.0159, -0.2951, -0.0156, -0.0544, -0.1193, -0.1563, -0.1691, -0.1292, +0.0228, -0.2741, -0.0235, -0.0564, +0.1007, -0.0648, -0.1383, -0.0235, -0.0557, -0.0005])
+# yapf: disable
+weights_dense1_w = np.array(
+    [[
+        +0.2856, -0.4091, +0.3380, -0.1083, +0.1049, -0.0806, -0.0827, +0.0402,
+        +0.2415, +0.0927, +0.1812, +0.5455, +0.3157, +0.1043, +0.0527, +0.3768,
+        -0.2083, +0.1218, -0.0633, +0.3236, +0.2198, +0.0513, +0.3505, -0.1165,
+        -0.1732, +0.2760, +0.0006, +0.0249, +0.5887, -0.2324, +0.1503, -0.0108,
+        +0.1220, +0.2527, +0.0984, +0.5360, -0.0579, -0.0847, +0.1649, +0.0589,
+        -0.2046, +0.5981, -0.1063, -0.1088, +0.3424, +0.3479, -0.1751, +0.2924,
+        -0.0610, +0.0807, +0.3291, +0.0059, +0.0339, -0.2823, +0.2533, +0.3156,
+        +0.1679, +0.2417, +0.1265, +0.0024, +0.0802, +0.2531, -0.2576, +0.3894,
+        +0.3206, +0.2015, +0.3482, +0.1042, -0.2418, -0.0002, +0.1277, +0.1811,
+        +0.1551, +0.5460, +0.1714, +0.1021, -0.1252, +0.0609, -0.0372, -0.0351,
+        +0.1216, +0.0023, -0.2448, +0.0077, +0.0584, +0.2389, -0.0848, +0.3542,
+        +0.3065, -0.2268, +0.2387, +0.3303, +0.4184, -0.1619, +0.2230, +0.2829,
+        +0.3884, +0.1262, +0.6383, -0.1629, +0.3087, +0.0554, +0.2294, +0.0280,
+        +0.4139, -0.1484, +0.1358, +0.3153, -0.2652, +0.2637, -0.1563, +0.0706,
+        +0.4192, +0.2381, -0.3540, +0.2789, +0.2647, +0.0931, +0.1439, +0.3415,
+        -0.2445, +0.1039, -0.3692, +0.5095, +0.0010, +0.0777, +0.3382, -0.1100
+    ],
+     [
+         +0.0820, +0.6327, +1.0135, -0.1423, -0.0275, +0.0396, -0.5933,
+         +0.3027, -0.2220, +0.1476, -0.0591, -0.7910, -0.0776, +0.2565,
+         +0.0241, -1.1300, +0.1324, +0.9079, +0.2318, +0.5574, +1.0162,
+         +0.1408, +0.7525, +0.3612, +0.6088, -0.2541, +0.8134, +0.1348,
+         -0.1265, +0.7991, -0.2304, -0.4826, -0.4123, +0.2831, -0.5457,
+         -0.3518, -0.9733, -0.1654, +0.2409, +0.3971, +0.6624, +0.0728,
+         +0.2200, -0.2126, -0.5299, -0.1858, -0.3467, -0.2776, -0.2004,
+         -0.7567, +0.4417, -0.4378, -0.6799, +0.3789, +0.2937, -0.1247,
+         +0.5540, -1.0124, -0.5759, +0.0308, +0.2971, +1.1300, -0.1185,
+         +0.5303, +0.0527, +0.3392, +0.7436, -0.3578, -0.0559, +0.2165,
+         -0.5860, +0.9427, -0.2522, -0.0805, +0.9314, +1.4173, +0.6216,
+         +0.6923, +0.6592, -0.0457, +0.5152, -0.3523, -0.5135, +0.6466,
+         -0.4804, +0.0006, +0.3223, -0.1548, +1.5508, +0.6204, +0.2618,
+         -0.0392, -0.6208, +0.0237, +0.0577, -0.2913, -0.3893, -0.1459,
+         -0.1103, +0.7485, -0.0045, +0.0640, +0.0726, -0.0966, +0.0452,
+         +0.2741, -0.3389, +0.8830, +0.9819, +0.3915, -0.1127, -0.2653,
+         -0.6842, +0.2389, -0.4349, +1.4013, -0.4442, +1.1846, -0.7550,
+         +0.5812, -0.2940, +0.7046, +0.4730, -0.2155, +0.1432, +0.1518,
+         +0.5336, -0.0827
+     ],
+     [
+         -0.2503, +0.2354, -0.0468, +0.1318, -0.0161, +0.1235, -0.1649,
+         -0.1380, -0.1816, -0.0953, -0.0303, -0.4337, +0.0159, -0.2893,
+         -0.1178, -0.1779, +0.0557, -0.0788, -0.1745, -0.1349, +0.1942,
+         +0.1707, -0.3289, +0.1177, -0.3084, +0.0423, +0.0006, -0.1240,
+         -0.2685, -0.0578, +0.3282, -0.3570, +0.0039, +0.3399, -0.3599,
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+weights_dense1_b = np.array([
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+    -0.0822, -0.1072, -0.1466, -0.3015, +0.2120, -0.0178, +0.0700, -0.1122,
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+    -0.1794, -0.1011, -0.1924, +0.0348, -0.0169, -0.3281, -0.2085, -0.2750,
+    -0.3329, -0.1793, -0.2296, -0.2289, -0.1589, -0.3225, +0.0159, -0.2951,
+    -0.0156, -0.0544, -0.1193, -0.1563, -0.1691, -0.1292, +0.0228, -0.2741,
+    -0.0235, -0.0564, +0.1007, -0.0648, -0.1383, -0.0235, -0.0557, -0.0005
 ])
 
-weights_dense2_b = np.array([ +0.2226, +0.0124, +0.0027, +0.2335, +0.0952, +0.0250, +0.1087, +0.1225, +0.0476, +0.1535, +0.0940, +0.0421, +0.0967, +0.0431, -0.2867, -0.1520, -0.0796, +0.0041, +0.0599, +0.0139, -0.0608, -0.0695, +0.0222, +0.0420, -0.0988, +0.0851, +0.2547, +0.0361, +0.0341, +0.0986, +0.1827, -0.1344, +0.1575, +0.0374, +0.0497, -0.1436, +0.1622, +0.2187, +0.0257, +0.0680, -0.0228, +0.0215, +0.1596, -0.0594, -0.0886, -0.0006, -0.0940, +0.1568, +0.0610, +0.1915, +0.1162, +0.0765, -0.1731, -0.0258, +0.0896, -0.2285, +0.0948, +0.0286, +0.1174, +0.0382, +0.2536, +0.3435, +0.0281, +0.2328])
+weights_dense2_w = np.array(
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+        +0.0489, +0.2915, -0.2513, -1.0484, +0.1129, -0.7923, +0.1379, -0.2217,
+        +0.1685, -0.0273, +0.2727, -0.1051, -0.3335, +0.4067, +0.1543, -0.0099,
+        -0.2005, -0.5140, -0.1314, -0.0537, -0.6073, -0.2714, +0.4734, +0.0307
+    ],
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+         -0.3488, +0.3701, -0.0261, -0.2519, +0.3600, -0.0131, -0.5443, -0.2148
+     ],
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+     ],
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+         +0.1182, -0.3572, -0.1822, +0.3167, -0.4896, -0.3626, -0.1668, -0.2644
+     ],
+     [
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+     ],
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+weights_dense2_b = np.array([
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+    +0.0476, +0.1535, +0.0940, +0.0421, +0.0967, +0.0431, -0.2867, -0.1520,
+    -0.0796, +0.0041, +0.0599, +0.0139, -0.0608, -0.0695, +0.0222, +0.0420,
+    -0.0988, +0.0851, +0.2547, +0.0361, +0.0341, +0.0986, +0.1827, -0.1344,
+    +0.1575, +0.0374, +0.0497, -0.1436, +0.1622, +0.2187, +0.0257, +0.0680,
+    -0.0228, +0.0215, +0.1596, -0.0594, -0.0886, -0.0006, -0.0940, +0.1568,
+    +0.0610, +0.1915, +0.1162, +0.0765, -0.1731, -0.0258, +0.0896, -0.2285,
+    +0.0948, +0.0286, +0.1174, +0.0382, +0.2536, +0.3435, +0.0281, +0.2328
 ])
 
-weights_final_b = np.array([ -0.0680, +0.1401, -0.0628, -0.1317, +0.1489, +0.1844, -0.1147, +0.0137])
+weights_final_w = np.array(
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+     [-0.0089, +0.0614, +0.0522, -0.3912, -0.1872, -0.7369, +0.0852, -0.1505],
+     [+0.2779, +0.7024, -0.3493, -0.2124, -0.0220, +0.0741, +0.0670, -0.1029],
+     [-0.0519, +0.2775, -0.3879, -0.0335, +0.1150, +0.0789, -0.1963, +0.1992],
+     [+0.0241, +0.2946, +0.2777, -0.2040, +0.4150, +0.0810, -0.0170, -0.0202],
+     [-0.2781, +0.0243, -0.0485, +0.1249, -0.0354, -0.0327, +0.2284, -0.3674],
+     [-0.1843, +0.1452, -0.0728, +0.2894, +0.1892, +0.0013, +0.1527, -0.3815],
+     [+0.4556, -0.1760, +0.0188, -0.2475, -0.3291, -0.3527, -0.6556, -0.5543],
+     [+0.0309, +0.3396, +0.0626, -0.0045, +0.2597, -0.2234, +0.3634, -0.1476],
+     [-0.3221, +0.4484, +0.0538, +0.2543, -0.3213, +0.4323, -0.2503, +0.0654],
+     [+0.5351, -0.2229, -0.1097, +0.1062, -0.5535, +0.4454, +0.1802, +0.1971],
+     [+0.1813, +0.0778, +0.1647, -0.1118, +0.4696, -0.3322, -0.2033, -0.0201],
+     [+0.1423, +0.2278, +0.0263, +0.1439, +0.0134, -0.5282, +0.1236, -0.0416],
+     [+0.1217, +0.1197, -0.5194, +0.4029, -0.1307, -0.4352, -0.0514, -0.2256],
+     [+0.1484, +0.0685, +0.4156, +0.1887, -0.7306, +0.1027, -0.4018, +0.0134],
+     [+0.2326, +0.0218, +0.0390, +0.0010, -0.5719, +0.0264, -0.4600, -0.3220],
+     [-0.0586, -0.3066, +0.2050, +0.1253, +0.4542, +0.1711, -0.6051, +0.0061],
+     [+0.0108, -0.5588, -0.3976, +0.3375, +0.3065, +0.2118, -0.4879, +0.1444],
+     [-0.1443, -0.2895, -0.3992, +0.0409, +0.3109, -0.0295, -0.2016, -0.3679],
+     [-0.2630, +0.2348, +0.2592, +0.1778, -0.1501, -0.2856, +0.3477, +0.1416],
+     [-0.3078, -0.3138, +0.2040, -0.3254, +0.2891, +0.0228, -0.3580, +0.2438],
+     [-0.0035, -0.1374, +0.9035, +0.1420, +0.1437, -0.1159, -0.1822, +0.2037],
+     [+0.2533, -0.0089, -0.1549, +0.1085, -0.5376, -0.0639, +0.3232, +0.0673],
+     [-0.1033, +0.2733, +0.1426, +0.4075, -0.1140, +0.1152, +0.2189, +0.7337],
+     [+0.0682, -0.1010, +0.2224, +0.4526, +0.0599, +0.1290, -0.5523, +0.0333],
+     [+0.1089, -0.1139, -0.0310, -0.2661, +0.0781, -0.1955, -0.2610, +0.1017],
+     [+0.0011, +0.4399, -0.2365, -0.1523, -0.1508, +0.2314, +0.1414, +0.1116],
+     [+0.0014, -0.0973, +0.6542, -0.0039, -0.2656, -0.1597, +0.0158, -0.0201],
+     [-0.1700, +0.0102, +0.1321, -0.1934, -0.2208, +0.0057, +0.1504, +0.3798],
+     [+0.1586, +0.0557, +0.1042, -0.4201, +0.0613, -0.0152, -0.3855, +0.1035],
+     [-0.0969, -0.0936, +0.1289, -0.4031, +0.1148, +0.3258, +0.2017, -0.2499],
+     [+0.5422, -0.3985, -0.0758, -0.0241, +0.1124, +0.0043, -0.0495, -0.1127],
+     [+0.1679, -0.1337, -0.2102, -0.0678, +0.0768, -0.5881, +0.0327, -0.1429],
+     [+0.2756, -0.1772, -0.3729, -0.0931, +0.6402, -0.0739, -0.1221, +0.5423],
+     [-0.2310, -0.6409, -0.0729, -0.2143, +0.0735, -0.3279, -0.1317, -0.2060],
+     [-0.0864, +0.0344, -0.5635, +0.3137, -0.0566, -0.3874, -0.0119, +0.0187],
+     [+0.4066, +0.1192, +0.2065, +0.0546, +0.2182, +0.6743, +0.2802, -0.0638],
+     [+0.2526, +0.6688, -0.0943, -0.2531, +0.1259, +0.1115, +0.0581, -0.0340],
+     [+0.1820, +0.2927, -0.2457, +0.0310, -0.3335, -0.5889, +0.0088, +0.3045],
+     [+0.0072, -0.0702, +0.1246, +0.0875, +0.0488, +0.2379, -0.1760, -0.5422],
+     [+0.1416, +0.1796, -0.7311, +0.0016, -0.4996, +0.0724, -0.4854, +0.2310],
+     [-0.5525, +0.1246, +0.8667, +0.3521, -0.7227, +0.4473, +0.0805, -0.1707],
+     [-0.3557, +0.0798, +0.5322, -0.0827, +0.1836, +0.2784, -0.1676, -0.1012],
+     [-0.6738, +0.5545, -0.3111, +0.2171, -0.3377, -0.2039, -0.1205, +0.1212],
+     [-0.2249, -0.1037, -0.4715, -0.3616, +0.2250, +0.0999, +0.4772, -0.0679],
+     [-0.4895, -0.2557, +0.0892, -0.0000, -0.4443, +0.2913, -0.0450, -0.3303],
+     [-0.2729, +0.0973, +0.2516, -0.4313, -0.1743, -0.2386, -0.1142, -0.0440],
+     [-0.6820, +0.2449, -0.1079, +0.3047, +0.0325, -0.0980, +0.1868, -0.1600],
+     [-0.2085, -0.2897, -0.4539, -0.0506, +0.1225, +0.1976, -0.2360, +0.6553],
+     [+0.5299, +0.2876, +0.2354, -0.0884, -0.1821, +0.3841, -0.0701, +0.0278],
+     [-0.1594, -0.4837, +0.1483, -0.1280, +0.0962, -0.1932, +0.1730, +0.0544],
+     [+0.2879, -0.1846, +0.1474, -0.3383, +0.1483, +0.1159, +0.3761, +0.2326],
+     [-0.0960, -0.0727, -0.3099, +0.0548, +0.2339, -0.2232, +0.6066, +0.3826],
+     [-0.0171, +0.1490, -0.3324, -0.0231, +0.0700, -0.6364, -0.1944, +0.3094],
+     [+0.0804, -0.0278, +0.0182, -0.3807, -0.3628, -0.3564, +0.2279, -0.0226],
+     [+0.4219, +0.0741, +0.1299, +0.4700, +0.1927, +0.0849, -0.1574, +0.5607],
+     [+0.0810, -0.0815, +0.1356, +0.2048, -0.1404, -0.0222, -0.2464, +0.4991],
+     [+0.3000, +0.0862, -0.3917, +0.4117, +0.0306, +0.0788, -0.0403, -0.6423],
+     [-0.2445, +0.4847, +0.0478, -0.2170, -0.3700, +0.0189, +0.0001, -0.0335],
+     [+0.3710, -0.0115, -0.4207, -0.4245, +0.2041, -0.0875, -0.2369, -0.6424],
+     [-0.3608, +0.0539, -0.3928, -0.0352, +0.2832, +0.1003, -0.0082, +0.3201],
+     [+0.2221, -0.0399, +0.2092, -0.1670, -0.5146, +0.2273, +0.3011, -0.2879],
+     [+0.1500, +0.3236, +0.1295, -0.0763, -0.2708, +0.6542, +0.1379, -0.0646],
+     [+0.1291, +0.0417, +0.1386, +0.0166, +0.1373, +0.1956, +0.4986, +0.5690]])
+
+weights_final_b = np.array(
+    [-0.0680, +0.1401, -0.0628, -0.1317, +0.1489, +0.1844, -0.1147, +0.0137])
+# yapf: enable
 
-if __name__=="__main__":
-    main()
+if __name__ == "__main__":
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HalfCheetahBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HalfCheetahBulletEnv_v0_2017may.py
index 1b68afd6f..50eacc552 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HalfCheetahBulletEnv_v0_2017may.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HalfCheetahBulletEnv_v0_2017may.py
@@ -2,293 +2,2120 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 import numpy as np
 import pybullet_envs
 import time
 
+
 def relu(x):
-    return np.maximum(x, 0)
+  return np.maximum(x, 0)
+
 
 class SmallReactivePolicy:
-    "Simple multi-layer perceptron policy, no internal state"
-    def __init__(self, observation_space, action_space):
-        assert weights_dense1_w.shape == (observation_space.shape[0], 128)
-        assert weights_dense2_w.shape == (128, 64)
-        assert weights_final_w.shape  == (64, action_space.shape[0])
+  "Simple multi-layer perceptron policy, no internal state"
+
+  def __init__(self, observation_space, action_space):
+    assert weights_dense1_w.shape == (observation_space.shape[0], 128)
+    assert weights_dense2_w.shape == (128, 64)
+    assert weights_final_w.shape == (64, action_space.shape[0])
+
+  def act(self, ob):
+    x = ob
+    x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
+    x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
+    x = np.dot(x, weights_final_w) + weights_final_b
+    return x
 
-    def act(self, ob):
-        x = ob
-        x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
-        x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
-        x = np.dot(x, weights_final_w) + weights_final_b
-        return x
 
 def main():
-    env = gym.make("HalfCheetahBulletEnv-v0")
-    env.render(mode="human")
+  env = gym.make("HalfCheetahBulletEnv-v0")
+  env.render(mode="human")
 
-    pi = SmallReactivePolicy(env.observation_space, env.action_space)
-    #disable rendering during reset, makes loading much faster
-    env.reset()
+  pi = SmallReactivePolicy(env.observation_space, env.action_space)
+  #disable rendering during reset, makes loading much faster
+  env.reset()
 
+  while 1:
+    frame = 0
+    score = 0
+    restart_delay = 0
+    obs = env.reset()
     while 1:
-        frame = 0
-        score = 0
-        restart_delay = 0
-        obs = env.reset()
-        while 1:
-            time.sleep(1./60.)
-            a = pi.act(obs)
-            obs, r, done, _ = env.step(a)
-            score += r
-            frame += 1
+      time.sleep(1. / 60.)
+      a = pi.act(obs)
+      obs, r, done, _ = env.step(a)
+      score += r
+      frame += 1
 
-            still_open = env.render("human")
-            if still_open==False:
-                return
-            if not done: continue
-            if restart_delay==0:
-                print("score=%0.2f in %i frames" % (score, frame))
-                restart_delay = 60*2  # 2 sec at 60 fps
-            else:
-                restart_delay -= 1
-                if restart_delay==0: break
+      still_open = env.render("human")
+      if still_open == False:
+        return
+      if not done: continue
+      if restart_delay == 0:
+        print("score=%0.2f in %i frames" % (score, frame))
+        restart_delay = 60 * 2  # 2 sec at 60 fps
+      else:
+        restart_delay -= 1
+        if restart_delay == 0: break
 
-weights_dense1_w = np.array([
-[ -0.4105, +0.5540, +0.2791, +0.4953, -0.0309, +0.8480, +0.1630, +0.0630, +0.7107, +0.0597, +0.2675, -0.1931, -0.2436, +0.2909, +0.3563, +0.4564, -0.0445, -0.2228, +0.7005, +0.8368, +0.3631, +0.7365, +0.2453, +0.4687, +0.4819, +0.4940, +0.5096, +0.5929, +0.5504, +0.2265, +0.4118, +0.5993, +0.2402, -0.5087, -0.0756, +0.7520, -0.0086, -0.0648, +0.0564, -0.0812, -0.0093, +0.8482, +0.7333, +0.4401, -0.1273, -0.2974, +0.1284, -0.4948, +0.1120, +0.7938, +0.1297, +1.0807, +0.2480, +0.2444, +0.4258, +0.1515, -0.0126, +0.3098, -0.2786, +0.4913, +0.3065, +0.3356, +0.6884, +0.3117, +0.3163, +0.4234, +0.1172, +0.8810, +0.3574, -0.3993, -0.5597, +0.2233, +0.8058, +0.3391, -0.1113, +0.1884, +0.1324, +0.0503, +0.1807, +0.0441, -0.2065, +0.1978, +0.0852, -0.3531, +0.2180, +0.7946, -0.0318, +0.0650, -0.3174, -0.1715, -0.0486, +0.2131, -0.3901, +0.1995, +0.8593, +0.1786, +0.3595, +0.7852, +0.3491, -0.4206, +0.6117, -0.1991, +0.1876, -0.3114, -0.2007, -0.2738, -0.0388, +0.0465, +0.2726, -0.5321, +0.4720, -0.0555, +0.3415, +0.1447, -0.1132, -0.2958, -0.6729, -0.5003, -0.4960, -0.2600, -0.4158, -0.4476, -0.8244, -0.0574, +0.2194, +0.0541, -0.3526, -0.1131],
-[ -0.2662, +0.1763, +0.0520, +0.3624, +0.0564, +0.2756, +0.0743, +0.0155, +0.1861, -0.5311, +0.2181, -0.3643, +0.1433, +0.1801, -0.1781, -0.3576, -0.2487, +0.0588, +0.3906, -0.3994, +0.1343, +0.2701, +0.1613, +0.1962, -0.0277, +0.0303, +0.0845, -0.1033, -0.1092, -0.1026, -0.2261, +0.3324, +0.1881, -0.2780, -0.0865, -0.2131, -0.1238, -0.1727, +0.1086, +0.1383, +0.1794, +0.0677, -0.0582, -0.0758, +0.0834, +0.0847, +0.0945, +0.1888, -0.0696, +0.0111, -0.0028, +0.0439, -0.3847, -0.1564, -0.2226, +0.0002, +0.0926, -0.2819, -0.0868, +0.2233, +0.0738, +0.1671, -0.0326, +0.3784, -0.0117, +0.0836, +0.0285, +0.0376, +0.0157, +0.1297, +0.0556, -0.0533, -0.0103, +0.0740, -0.1825, -0.2399, -0.0609, -0.0030, +0.1811, -0.2784, -0.2249, +0.3637, +0.1895, +0.3102, -0.0767, +0.1143, -0.0840, +0.2981, -0.3040, +0.0135, +0.2495, +0.0036, -0.1493, -0.1144, -0.0006, +0.1195, +0.0961, -0.4227, -0.3022, +0.2102, +0.2024, -0.2111, +0.1507, +0.2948, -0.1822, +0.2263, -0.3213, +0.0380, -0.1188, +0.1237, +0.0731, +0.0044, -0.1637, -0.3173, +0.0895, -0.0130, +0.0703, -0.2186, +0.1401, +0.1191, +0.0225, -0.3208, -0.0578, -0.3113, +0.1041, -0.1957, -0.3054, -0.0797],
-[ +0.3485, -0.3161, -0.4092, +0.1190, -0.5706, -0.3627, +0.0576, -0.1068, +0.1367, +0.1686, +0.0284, -0.1694, -0.0496, -0.1647, +0.0111, -0.1897, -0.4613, -0.5996, -0.0601, -0.1417, -0.0236, -0.0898, +0.1239, -0.2693, -0.1709, +0.3592, -0.1284, -0.0376, -0.1308, -0.1832, -0.0538, -0.0467, -0.1181, -0.0994, -0.3281, +0.0527, -0.4579, -0.1062, -0.1058, -0.4459, -0.0901, -0.0246, -0.1125, +0.1228, -0.3428, -0.3385, +0.0366, -0.0988, +0.1345, +0.0299, +0.2921, -0.1347, +0.1688, -0.1350, -0.0888, +0.0734, -0.0936, -0.2695, +0.0154, +0.0237, -0.1334, -0.3247, -0.1856, -0.0449, -0.0772, +0.0176, -0.0856, -0.1746, -0.1575, -0.2365, -0.0741, -0.1638, +0.0864, -0.0243, -0.0472, +0.0423, -0.2962, -0.2523, -0.1430, +0.1470, -0.1122, -0.1843, -0.1437, -0.1895, +0.1110, -0.2572, -0.3307, -0.2597, -0.4706, -0.2441, -0.2988, -0.5409, -0.1696, +0.0810, -0.2336, -0.0925, +0.4226, +0.1162, -0.3211, -0.2118, -0.3100, -0.1384, +0.1625, +0.0005, -0.1936, +0.2836, +0.1660, -0.2154, +0.1341, -0.2413, -0.1203, -0.3427, +0.0863, +0.1393, -0.3580, +0.1443, -0.2158, -0.4703, -0.2687, -0.0573, +0.1099, -0.2551, -0.0506, -0.3351, -0.2325, -0.0119, -0.0907, -0.0628],
-[ +0.0565, +0.2158, +0.1461, +0.1045, -0.3548, -0.2816, -0.0124, -0.0931, +0.1249, +0.0093, -0.3325, -0.1646, +0.3022, -0.0509, -0.1403, +0.3703, +0.2642, +0.0835, +0.1347, +0.1734, -0.0112, +0.3162, -0.0076, +0.1258, -0.2059, +0.2387, +0.0775, -0.1653, -0.3899, -0.1205, -0.2263, +0.2800, -0.1979, +0.1708, +0.0505, -0.0998, +0.1838, -0.1314, -0.1932, -0.1897, -0.3157, -0.0113, -0.3488, +0.1534, -0.3546, -0.1098, +0.0652, -0.1327, -0.0131, +0.3698, -0.0927, +0.1735, -0.4869, +0.1768, -0.0187, -0.1069, -0.2118, -0.2557, -0.1380, -0.0579, -0.2147, +0.3389, -0.1801, +0.1954, -0.2856, +0.0825, +0.1846, -0.2102, +0.4063, -0.0422, +0.1645, -0.2313, -0.2017, -0.2554, +0.1597, -0.1198, +0.4493, +0.1135, +0.1556, -0.0380, +0.0527, -0.0505, -0.5094, -0.4410, +0.2543, +0.2891, -0.0031, -0.0863, +0.2982, +0.1069, -0.4814, +0.0295, -0.0547, +0.0518, -0.4326, -0.0295, -0.3795, +0.0399, +0.0363, +0.1623, +0.4509, +0.2852, -0.4404, +0.0109, -0.0394, +0.0092, -0.1684, +0.4728, -0.1317, +0.0575, -0.3862, -0.1313, +0.0676, +0.1321, -0.2816, -0.0500, -0.0027, +0.0906, +0.0297, -0.1476, +0.0337, +0.0911, -0.1591, +0.2816, -0.0435, -0.3437, -0.0732, -0.4545],
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-])
 
-weights_dense1_b = np.array([ +0.0484, -0.2398, -0.1357, +0.0298, -0.3778, -0.2392, -0.0211, -0.1728, +0.0154, -0.1052, -0.0235, -0.0825, -0.0137, -0.1951, +0.1408, -0.2835, -0.4597, -0.2002, -0.1659, -0.2736, -0.0233, -0.2472, -0.0697, -0.1892, -0.1340, -0.0051, -0.1712, -0.1986, -0.1902, -0.1213, -0.1363, -0.2731, -0.0609, +0.0803, -0.1623, +0.0661, -0.0660, -0.1206, -0.2835, -0.1281, -0.1008, -0.2377, -0.1436, -0.1027, -0.1816, -0.1699, -0.2476, -0.0886, -0.1582, -0.1714, -0.1558, -0.2484, -0.1255, -0.1657, -0.0830, -0.0800, -0.1293, -0.0594, +0.0174, +0.1097, -0.1769, -0.1648, -0.1995, -0.1082, -0.1761, +0.0342, -0.1445, -0.0106, -0.2021, -0.1352, -0.2769, -0.1503, +0.1171, -0.2275, -0.1514, -0.1064, -0.1877, +0.0308, -0.1081, -0.0229, -0.1273, -0.0516, -0.0789, -0.1148, -0.0308, -0.1946, -0.3185, -0.1215, -0.0605, -0.1876, -0.1192, -0.1545, -0.0148, -0.0218, -0.3640, -0.2230, +0.1549, +0.0196, -0.2555, -0.1814, -0.3217, -0.1741, +0.0117, -0.1752, -0.2445, -0.0863, +0.0041, +0.0058, -0.0948, -0.1010, -0.0595, +0.0182, -0.0761, -0.0141, -0.1886, -0.0474, +0.0057, -0.1247, -0.3390, +0.0672, -0.0612, -0.1059, +0.0219, -0.1802, -0.1265, -0.1287, +0.0565, -0.0502])
+# yapf: disable
+weights_dense1_w = np.array(
+    [[
+        -0.4105, +0.5540, +0.2791, +0.4953, -0.0309, +0.8480, +0.1630, +0.0630,
+        +0.7107, +0.0597, +0.2675, -0.1931, -0.2436, +0.2909, +0.3563, +0.4564,
+        -0.0445, -0.2228, +0.7005, +0.8368, +0.3631, +0.7365, +0.2453, +0.4687,
+        +0.4819, +0.4940, +0.5096, +0.5929, +0.5504, +0.2265, +0.4118, +0.5993,
+        +0.2402, -0.5087, -0.0756, +0.7520, -0.0086, -0.0648, +0.0564, -0.0812,
+        -0.0093, +0.8482, +0.7333, +0.4401, -0.1273, -0.2974, +0.1284, -0.4948,
+        +0.1120, +0.7938, +0.1297, +1.0807, +0.2480, +0.2444, +0.4258, +0.1515,
+        -0.0126, +0.3098, -0.2786, +0.4913, +0.3065, +0.3356, +0.6884, +0.3117,
+        +0.3163, +0.4234, +0.1172, +0.8810, +0.3574, -0.3993, -0.5597, +0.2233,
+        +0.8058, +0.3391, -0.1113, +0.1884, +0.1324, +0.0503, +0.1807, +0.0441,
+        -0.2065, +0.1978, +0.0852, -0.3531, +0.2180, +0.7946, -0.0318, +0.0650,
+        -0.3174, -0.1715, -0.0486, +0.2131, -0.3901, +0.1995, +0.8593, +0.1786,
+        +0.3595, +0.7852, +0.3491, -0.4206, +0.6117, -0.1991, +0.1876, -0.3114,
+        -0.2007, -0.2738, -0.0388, +0.0465, +0.2726, -0.5321, +0.4720, -0.0555,
+        +0.3415, +0.1447, -0.1132, -0.2958, -0.6729, -0.5003, -0.4960, -0.2600,
+        -0.4158, -0.4476, -0.8244, -0.0574, +0.2194, +0.0541, -0.3526, -0.1131
+    ],
+     [
+         -0.2662, +0.1763, +0.0520, +0.3624, +0.0564, +0.2756, +0.0743,
+         +0.0155, +0.1861, -0.5311, +0.2181, -0.3643, +0.1433, +0.1801,
+         -0.1781, -0.3576, -0.2487, +0.0588, +0.3906, -0.3994, +0.1343,
+         +0.2701, +0.1613, +0.1962, -0.0277, +0.0303, +0.0845, -0.1033,
+         -0.1092, -0.1026, -0.2261, +0.3324, +0.1881, -0.2780, -0.0865,
+         -0.2131, -0.1238, -0.1727, +0.1086, +0.1383, +0.1794, +0.0677,
+         -0.0582, -0.0758, +0.0834, +0.0847, +0.0945, +0.1888, -0.0696,
+         +0.0111, -0.0028, +0.0439, -0.3847, -0.1564, -0.2226, +0.0002,
+         +0.0926, -0.2819, -0.0868, +0.2233, +0.0738, +0.1671, -0.0326,
+         +0.3784, -0.0117, +0.0836, +0.0285, +0.0376, +0.0157, +0.1297,
+         +0.0556, -0.0533, -0.0103, +0.0740, -0.1825, -0.2399, -0.0609,
+         -0.0030, +0.1811, -0.2784, -0.2249, +0.3637, +0.1895, +0.3102,
+         -0.0767, +0.1143, -0.0840, +0.2981, -0.3040, +0.0135, +0.2495,
+         +0.0036, -0.1493, -0.1144, -0.0006, +0.1195, +0.0961, -0.4227,
+         -0.3022, +0.2102, +0.2024, -0.2111, +0.1507, +0.2948, -0.1822,
+         +0.2263, -0.3213, +0.0380, -0.1188, +0.1237, +0.0731, +0.0044,
+         -0.1637, -0.3173, +0.0895, -0.0130, +0.0703, -0.2186, +0.1401,
+         +0.1191, +0.0225, -0.3208, -0.0578, -0.3113, +0.1041, -0.1957,
+         -0.3054, -0.0797
+     ],
+     [
+         +0.3485, -0.3161, -0.4092, +0.1190, -0.5706, -0.3627, +0.0576,
+         -0.1068, +0.1367, +0.1686, +0.0284, -0.1694, -0.0496, -0.1647,
+         +0.0111, -0.1897, -0.4613, -0.5996, -0.0601, -0.1417, -0.0236,
+         -0.0898, +0.1239, -0.2693, -0.1709, +0.3592, -0.1284, -0.0376,
+         -0.1308, -0.1832, -0.0538, -0.0467, -0.1181, -0.0994, -0.3281,
+         +0.0527, -0.4579, -0.1062, -0.1058, -0.4459, -0.0901, -0.0246,
+         -0.1125, +0.1228, -0.3428, -0.3385, +0.0366, -0.0988, +0.1345,
+         +0.0299, +0.2921, -0.1347, +0.1688, -0.1350, -0.0888, +0.0734,
+         -0.0936, -0.2695, +0.0154, +0.0237, -0.1334, -0.3247, -0.1856,
+         -0.0449, -0.0772, +0.0176, -0.0856, -0.1746, -0.1575, -0.2365,
+         -0.0741, -0.1638, +0.0864, -0.0243, -0.0472, +0.0423, -0.2962,
+         -0.2523, -0.1430, +0.1470, -0.1122, -0.1843, -0.1437, -0.1895,
+         +0.1110, -0.2572, -0.3307, -0.2597, -0.4706, -0.2441, -0.2988,
+         -0.5409, -0.1696, +0.0810, -0.2336, -0.0925, +0.4226, +0.1162,
+         -0.3211, -0.2118, -0.3100, -0.1384, +0.1625, +0.0005, -0.1936,
+         +0.2836, +0.1660, -0.2154, +0.1341, -0.2413, -0.1203, -0.3427,
+         +0.0863, +0.1393, -0.3580, +0.1443, -0.2158, -0.4703, -0.2687,
+         -0.0573, +0.1099, -0.2551, -0.0506, -0.3351, -0.2325, -0.0119,
+         -0.0907, -0.0628
+     ],
+     [
+         +0.0565, +0.2158, +0.1461, +0.1045, -0.3548, -0.2816, -0.0124,
+         -0.0931, +0.1249, +0.0093, -0.3325, -0.1646, +0.3022, -0.0509,
+         -0.1403, +0.3703, +0.2642, +0.0835, +0.1347, +0.1734, -0.0112,
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+weights_dense1_b = np.array([
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+    +0.0154, -0.1052, -0.0235, -0.0825, -0.0137, -0.1951, +0.1408, -0.2835,
+    -0.4597, -0.2002, -0.1659, -0.2736, -0.0233, -0.2472, -0.0697, -0.1892,
+    -0.1340, -0.0051, -0.1712, -0.1986, -0.1902, -0.1213, -0.1363, -0.2731,
+    -0.0609, +0.0803, -0.1623, +0.0661, -0.0660, -0.1206, -0.2835, -0.1281,
+    -0.1008, -0.2377, -0.1436, -0.1027, -0.1816, -0.1699, -0.2476, -0.0886,
+    -0.1582, -0.1714, -0.1558, -0.2484, -0.1255, -0.1657, -0.0830, -0.0800,
+    -0.1293, -0.0594, +0.0174, +0.1097, -0.1769, -0.1648, -0.1995, -0.1082,
+    -0.1761, +0.0342, -0.1445, -0.0106, -0.2021, -0.1352, -0.2769, -0.1503,
+    +0.1171, -0.2275, -0.1514, -0.1064, -0.1877, +0.0308, -0.1081, -0.0229,
+    -0.1273, -0.0516, -0.0789, -0.1148, -0.0308, -0.1946, -0.3185, -0.1215,
+    -0.0605, -0.1876, -0.1192, -0.1545, -0.0148, -0.0218, -0.3640, -0.2230,
+    +0.1549, +0.0196, -0.2555, -0.1814, -0.3217, -0.1741, +0.0117, -0.1752,
+    -0.2445, -0.0863, +0.0041, +0.0058, -0.0948, -0.1010, -0.0595, +0.0182,
+    -0.0761, -0.0141, -0.1886, -0.0474, +0.0057, -0.1247, -0.3390, +0.0672,
+    -0.0612, -0.1059, +0.0219, -0.1802, -0.1265, -0.1287, +0.0565, -0.0502
 ])
 
-weights_dense2_b = np.array([ +0.1534, -0.0356, +0.0392, +0.0272, +0.0733, +0.2141, -0.0432, -0.0479, -0.1264, +0.1456, +0.2103, -0.0233, +0.2320, -0.0525, +0.1808, +0.1565, -0.0347, -0.0719, -0.2635, -0.1948, -0.1301, +0.0453, -0.0722, +0.0875, +0.3780, +0.1023, +0.1012, +0.3150, +0.1801, +0.1941, +0.2014, +0.1381, +0.1411, -0.0371, -0.0987, +0.1987, +0.1015, -0.2282, +0.0072, -0.0800, +0.0323, +0.1198, +0.2822, +0.0090, -0.1950, +0.0864, +0.2694, -0.0746, +0.2193, +0.1658, +0.0712, -0.1185, +0.1351, +0.1331, +0.3542, +0.1543, +0.1557, -0.0575, +0.0659, -0.0090, -0.1502, +0.0665, +0.1853, -0.0413])
+weights_dense2_w = np.array(
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+        -0.1386, +0.4026, +0.2577, -0.4374, +0.4362, +0.2120, +0.0860, +0.0373,
+        -0.5472, -0.4427, +0.0769, +0.3067, +0.3401, -0.0008, -0.7384, +0.0566,
+        -0.0583, -0.3481, -0.2953, -0.1032, -0.7956, -0.6262, -0.0733, -0.6908,
+        -0.4126, -0.2014, +0.6791, -0.5321, +0.5106, -0.1055, +0.0604, -0.5233,
+        -0.8984, -0.4227, -1.3192, +0.3693, +0.2789, -0.2774, +0.0132, -0.1824,
+        -0.0690, -1.6134, -0.2901, -0.1739, -0.5780, -0.3432, +0.0980, -0.3106
+    ],
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+         -0.2818, -0.6144, -0.2387, -0.3771, -0.0378, +0.0776, -1.4711, -0.1353
+     ],
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+     ],
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+         -0.7177, -0.6430, +0.3333, -0.7577, -0.3507, -1.0563, -0.4578, +0.0739
+     ],
+     [
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+     ],
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+         -0.6818, -0.4023, -0.1221, -0.0995, +0.1512, -0.2698, -0.0544,
+         +0.0406, +0.0104, +0.2382, +0.0113, +0.5360, +0.3837, +0.0947,
+         +0.4839, +0.0278, -0.4629, -0.1912, -0.7728, -0.0162, +0.5847,
+         +0.1752, +0.1536, -0.3113, -0.4624, -0.2032, -0.2410, -0.0095,
+         -0.3411, -0.2375, -0.3024, -0.0689, +0.0134, -0.2987, +0.2678,
+         +0.1053, +0.3568, -0.3776, -0.0956, +0.1105, +0.1802, +0.3608, +0.2763
+     ],
+     [
+         -0.0126, -0.1275, +0.1721, -0.1653, +0.0189, -0.0586, +0.1487,
+         +0.2661, -0.0924, -0.1733, +0.0411, +0.1124, -0.0426, -0.0405,
+         +0.0713, -0.1322, +0.1053, +0.2013, +0.2442, -0.4979, +0.3654,
+         -0.2099, +0.0049, -0.6743, -0.1063, -0.1213, +0.2036, +0.2109,
+         -0.2639, +0.1839, +0.3105, -0.0961, +0.3935, +0.1186, -0.0959,
+         +0.5348, -0.3605, -0.6159, -0.3268, +0.1327, +0.5812, -0.4447,
+         -1.8573, +0.1350, -0.0151, +0.1569, +0.0348, +0.2221, -0.6421,
+         -0.2810, +0.1918, -0.1290, +0.0505, +0.0517, +0.0313, -0.0781,
+         +0.1746, +0.0713, +0.2894, -1.0579, -0.1106, +0.3800, -0.6895, -1.1236
+     ]])
 
-weights_final_w = np.array([
-[ +0.5409, +0.1061, +0.1214, +0.0961, -0.0304, +0.2028],
-[ +0.3719, +0.0252, -0.1683, +0.1448, +0.0874, -0.0254],
-[ -0.1174, +0.2622, -0.1514, -0.2936, -0.1060, -0.5701],
-[ +0.0623, +0.0379, -0.0004, -0.3542, -0.0684, -0.2670],
-[ +0.0487, +0.0638, -0.5099, -0.1448, -0.0639, +0.1894],
-[ -0.3975, -0.1887, +0.2938, -0.0159, +0.1679, +0.5848],
-[ +0.1560, -0.0012, -0.0360, -0.2195, -0.3875, +0.0259],
-[ -0.0942, -0.0268, -0.1722, -0.0594, -0.0527, +0.5528],
-[ +0.1087, -0.3823, -0.1868, -0.0919, +0.2626, +0.2375],
-[ +0.0054, -0.1016, -0.3405, +0.4056, +0.0899, +0.0129],
-[ -0.0055, +0.1361, +0.2055, +0.6780, +0.0250, -0.0961],
-[ +0.0586, +0.7423, -0.0014, +0.1401, +0.4564, +0.5103],
-[ -0.1461, +0.1129, +0.2607, +0.3828, +0.2286, +0.0225],
-[ -0.0789, +0.0790, -0.1576, +0.3564, +0.4390, +0.2671],
-[ +0.2657, +0.0888, -0.2312, -0.1819, -0.1583, -0.1158],
-[ -0.0429, +0.1533, -0.0649, +0.1670, +0.5324, +0.2681],
-[ +0.1750, +0.0582, -0.1742, +0.7826, -0.0295, -0.0646],
-[ +0.1563, +0.0241, -0.4052, +0.5419, +0.0212, -0.1529],
-[ +0.1667, -0.0708, +0.0750, +0.0286, +0.2312, -0.2932],
-[ -0.0755, +0.3814, -0.3422, -0.3412, +0.0820, -0.6145],
-[ -0.0485, -0.1721, -0.1366, -0.0866, +0.0972, +0.4096],
-[ -0.0026, -0.3586, -0.0705, -0.0509, +0.0127, +0.3078],
-[ +0.1658, -0.2918, -0.4419, -0.1099, -0.1852, -0.0318],
-[ +0.3883, -0.1925, -0.3671, -0.3079, -0.1405, -0.6695],
-[ -0.3967, -0.1212, +0.2062, -0.4248, +0.2445, +0.5061],
-[ +0.2296, +0.0054, +0.1389, +0.1225, -0.2944, -0.3264],
-[ -0.3366, -0.1053, +0.1004, +0.0522, -0.2441, -0.1599],
-[ +0.3214, +0.3230, -0.3416, +0.1735, -0.0354, +0.0172],
-[ -0.0715, -0.0971, +0.2212, +0.0870, +0.4960, +0.0409],
-[ +0.6941, +0.1916, +0.4534, -0.3305, -0.1206, +0.2755],
-[ -0.0466, +0.2676, -0.2066, -0.2910, +0.0603, +0.1708],
-[ +0.3716, +0.1599, -0.1473, +0.0362, -0.0572, +0.4125],
-[ +0.0225, -0.1431, +0.3085, -0.1022, -0.3053, -0.3167],
-[ +0.1484, +0.3096, +0.2186, -0.2676, +0.2821, -0.0416],
-[ +0.2600, -0.1355, +0.1571, -0.4409, +0.1009, +0.1011],
-[ +0.2334, +0.3423, -0.0794, +0.0448, -0.0112, -0.0245],
-[ -0.3402, +0.1981, -0.0945, +0.1727, -0.0363, -0.1486],
-[ -0.4074, -0.1319, +0.0434, +0.4359, -0.2658, -0.3302],
-[ +0.2756, -0.2130, -0.2856, -0.1436, -0.4224, +0.1142],
-[ -0.1194, +0.2529, -0.2741, -0.0691, -0.2947, -0.0033],
-[ -0.0449, +0.4437, -0.0389, +0.1377, +0.1102, -0.1049],
-[ +0.4368, +0.1699, +0.3433, -0.4160, -0.3043, +0.1180],
-[ -0.1328, -0.6681, +0.4017, +0.3457, +0.1843, -0.0134],
-[ +0.0630, +0.2032, +0.2331, -0.1384, -0.1024, +0.3183],
-[ +0.1946, +0.1460, -0.1598, -0.2830, +0.2411, +0.3073],
-[ +0.4296, -0.2071, +0.3769, +0.0840, -0.0062, -0.1579],
-[ -0.0790, -0.1186, +0.0962, +0.4871, -0.2901, +0.6272],
-[ -0.2573, +0.3325, +0.1097, -0.2340, -0.0787, +0.1208],
-[ -0.4469, -0.3637, -0.1740, -0.4408, +0.0512, +0.5575],
-[ +0.1426, -0.3975, -0.0685, -0.0452, -0.4517, +0.4320],
-[ -0.5925, +0.3324, -0.1968, -0.2537, +0.6491, -0.2922],
-[ -0.2201, -0.3118, -0.3722, +0.0216, +0.1670, -0.1343],
-[ +0.0638, -0.2374, -0.1480, +0.4090, +0.2347, +0.1456],
-[ -0.0656, +0.1516, -0.3281, -0.2661, +0.0645, +0.0550],
-[ +0.3725, +0.2281, +0.3031, +0.0774, -0.3138, +0.0967],
-[ +0.3016, +0.0322, +0.0556, -0.0571, -0.1177, -0.2442],
-[ -0.2769, -0.5119, -0.0336, -0.1588, +0.0503, -0.2331],
-[ +0.1518, +0.4363, +0.0093, -0.3960, -0.1347, +0.2931],
-[ +0.1897, -0.0655, +0.1977, +0.4321, +0.6998, -0.0863],
-[ +0.0391, -0.0305, +0.3576, -0.4996, -0.0640, -0.0039],
-[ +0.2922, +0.3815, +0.2834, +0.1990, -0.0568, +0.2077],
-[ -0.5371, +0.2796, +0.2952, +0.1182, +0.0523, -0.2151],
-[ -0.5111, +0.2231, -0.1034, +0.0479, -0.0543, +0.0605],
-[ +0.0822, -0.4530, -0.0345, +0.1621, -0.2487, +0.1516]
+weights_dense2_b = np.array([
+    +0.1534, -0.0356, +0.0392, +0.0272, +0.0733, +0.2141, -0.0432, -0.0479,
+    -0.1264, +0.1456, +0.2103, -0.0233, +0.2320, -0.0525, +0.1808, +0.1565,
+    -0.0347, -0.0719, -0.2635, -0.1948, -0.1301, +0.0453, -0.0722, +0.0875,
+    +0.3780, +0.1023, +0.1012, +0.3150, +0.1801, +0.1941, +0.2014, +0.1381,
+    +0.1411, -0.0371, -0.0987, +0.1987, +0.1015, -0.2282, +0.0072, -0.0800,
+    +0.0323, +0.1198, +0.2822, +0.0090, -0.1950, +0.0864, +0.2694, -0.0746,
+    +0.2193, +0.1658, +0.0712, -0.1185, +0.1351, +0.1331, +0.3542, +0.1543,
+    +0.1557, -0.0575, +0.0659, -0.0090, -0.1502, +0.0665, +0.1853, -0.0413
 ])
 
-weights_final_b = np.array([ +0.1367, +0.1107, -0.0148, +0.1158, -0.0820, +0.3047])
+weights_final_w = np.array(
+    [[+0.5409, +0.1061, +0.1214, +0.0961, -0.0304, +0.2028],
+     [+0.3719, +0.0252, -0.1683, +0.1448, +0.0874, -0.0254],
+     [-0.1174, +0.2622, -0.1514, -0.2936, -0.1060, -0.5701],
+     [+0.0623, +0.0379, -0.0004, -0.3542, -0.0684, -0.2670],
+     [+0.0487, +0.0638, -0.5099, -0.1448, -0.0639, +0.1894],
+     [-0.3975, -0.1887, +0.2938, -0.0159, +0.1679, +0.5848],
+     [+0.1560, -0.0012, -0.0360, -0.2195, -0.3875, +0.0259],
+     [-0.0942, -0.0268, -0.1722, -0.0594, -0.0527, +0.5528],
+     [+0.1087, -0.3823, -0.1868, -0.0919, +0.2626, +0.2375],
+     [+0.0054, -0.1016, -0.3405, +0.4056, +0.0899, +0.0129],
+     [-0.0055, +0.1361, +0.2055, +0.6780, +0.0250, -0.0961],
+     [+0.0586, +0.7423, -0.0014, +0.1401, +0.4564, +0.5103],
+     [-0.1461, +0.1129, +0.2607, +0.3828, +0.2286, +0.0225],
+     [-0.0789, +0.0790, -0.1576, +0.3564, +0.4390, +0.2671],
+     [+0.2657, +0.0888, -0.2312, -0.1819, -0.1583, -0.1158],
+     [-0.0429, +0.1533, -0.0649, +0.1670, +0.5324, +0.2681],
+     [+0.1750, +0.0582, -0.1742, +0.7826, -0.0295, -0.0646],
+     [+0.1563, +0.0241, -0.4052, +0.5419, +0.0212, -0.1529],
+     [+0.1667, -0.0708, +0.0750, +0.0286, +0.2312, -0.2932],
+     [-0.0755, +0.3814, -0.3422, -0.3412, +0.0820, -0.6145],
+     [-0.0485, -0.1721, -0.1366, -0.0866, +0.0972, +0.4096],
+     [-0.0026, -0.3586, -0.0705, -0.0509, +0.0127, +0.3078],
+     [+0.1658, -0.2918, -0.4419, -0.1099, -0.1852, -0.0318],
+     [+0.3883, -0.1925, -0.3671, -0.3079, -0.1405, -0.6695],
+     [-0.3967, -0.1212, +0.2062, -0.4248, +0.2445, +0.5061],
+     [+0.2296, +0.0054, +0.1389, +0.1225, -0.2944, -0.3264],
+     [-0.3366, -0.1053, +0.1004, +0.0522, -0.2441, -0.1599],
+     [+0.3214, +0.3230, -0.3416, +0.1735, -0.0354, +0.0172],
+     [-0.0715, -0.0971, +0.2212, +0.0870, +0.4960, +0.0409],
+     [+0.6941, +0.1916, +0.4534, -0.3305, -0.1206, +0.2755],
+     [-0.0466, +0.2676, -0.2066, -0.2910, +0.0603, +0.1708],
+     [+0.3716, +0.1599, -0.1473, +0.0362, -0.0572, +0.4125],
+     [+0.0225, -0.1431, +0.3085, -0.1022, -0.3053, -0.3167],
+     [+0.1484, +0.3096, +0.2186, -0.2676, +0.2821, -0.0416],
+     [+0.2600, -0.1355, +0.1571, -0.4409, +0.1009, +0.1011],
+     [+0.2334, +0.3423, -0.0794, +0.0448, -0.0112, -0.0245],
+     [-0.3402, +0.1981, -0.0945, +0.1727, -0.0363, -0.1486],
+     [-0.4074, -0.1319, +0.0434, +0.4359, -0.2658, -0.3302],
+     [+0.2756, -0.2130, -0.2856, -0.1436, -0.4224, +0.1142],
+     [-0.1194, +0.2529, -0.2741, -0.0691, -0.2947, -0.0033],
+     [-0.0449, +0.4437, -0.0389, +0.1377, +0.1102, -0.1049],
+     [+0.4368, +0.1699, +0.3433, -0.4160, -0.3043, +0.1180],
+     [-0.1328, -0.6681, +0.4017, +0.3457, +0.1843, -0.0134],
+     [+0.0630, +0.2032, +0.2331, -0.1384, -0.1024, +0.3183],
+     [+0.1946, +0.1460, -0.1598, -0.2830, +0.2411, +0.3073],
+     [+0.4296, -0.2071, +0.3769, +0.0840, -0.0062, -0.1579],
+     [-0.0790, -0.1186, +0.0962, +0.4871, -0.2901, +0.6272],
+     [-0.2573, +0.3325, +0.1097, -0.2340, -0.0787, +0.1208],
+     [-0.4469, -0.3637, -0.1740, -0.4408, +0.0512, +0.5575],
+     [+0.1426, -0.3975, -0.0685, -0.0452, -0.4517, +0.4320],
+     [-0.5925, +0.3324, -0.1968, -0.2537, +0.6491, -0.2922],
+     [-0.2201, -0.3118, -0.3722, +0.0216, +0.1670, -0.1343],
+     [+0.0638, -0.2374, -0.1480, +0.4090, +0.2347, +0.1456],
+     [-0.0656, +0.1516, -0.3281, -0.2661, +0.0645, +0.0550],
+     [+0.3725, +0.2281, +0.3031, +0.0774, -0.3138, +0.0967],
+     [+0.3016, +0.0322, +0.0556, -0.0571, -0.1177, -0.2442],
+     [-0.2769, -0.5119, -0.0336, -0.1588, +0.0503, -0.2331],
+     [+0.1518, +0.4363, +0.0093, -0.3960, -0.1347, +0.2931],
+     [+0.1897, -0.0655, +0.1977, +0.4321, +0.6998, -0.0863],
+     [+0.0391, -0.0305, +0.3576, -0.4996, -0.0640, -0.0039],
+     [+0.2922, +0.3815, +0.2834, +0.1990, -0.0568, +0.2077],
+     [-0.5371, +0.2796, +0.2952, +0.1182, +0.0523, -0.2151],
+     [-0.5111, +0.2231, -0.1034, +0.0479, -0.0543, +0.0605],
+     [+0.0822, -0.4530, -0.0345, +0.1621, -0.2487, +0.1516]])
+
+weights_final_b = np.array(
+    [+0.1367, +0.1107, -0.0148, +0.1158, -0.0820, +0.3047])
+# yapf: enable
 
-if __name__=="__main__":
-    main()
+if __name__ == "__main__":
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HopperBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HopperBulletEnv_v0_2017may.py
index 367e80299..a58b79411 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HopperBulletEnv_v0_2017may.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HopperBulletEnv_v0_2017may.py
@@ -2,285 +2,1889 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 import numpy as np
 import pybullet_envs
 import time
 
+
 def relu(x):
-    return np.maximum(x, 0)
+  return np.maximum(x, 0)
+
 
 class SmallReactivePolicy:
-    """
+  """
     Simple multi-layer perceptron policy, no internal state
     """
-    def __init__(self, observation_space, action_space):
-        assert weights_dense1_w.shape == (observation_space.shape[0], 128)
-        assert weights_dense2_w.shape == (128, 64)
-        assert weights_final_w.shape  == (64, action_space.shape[0])
 
-    def act(self, ob):
-        x = ob
-        x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
-        x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
-        x = np.dot(x, weights_final_w) + weights_final_b
-        return x
+  def __init__(self, observation_space, action_space):
+    assert weights_dense1_w.shape == (observation_space.shape[0], 128)
+    assert weights_dense2_w.shape == (128, 64)
+    assert weights_final_w.shape == (64, action_space.shape[0])
+
+  def act(self, ob):
+    x = ob
+    x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
+    x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
+    x = np.dot(x, weights_final_w) + weights_final_b
+    return x
+
 
 def main():
-    env = gym.make("HopperBulletEnv-v0")
-    env.render(mode="human")
-    
-    pi = SmallReactivePolicy(env.observation_space, env.action_space)
-    env.reset()
-    while 1:
-        frame = 0
-        score = 0
-        restart_delay = 0
-        #disable rendering during reset, makes loading much faster
-        obs = env.reset()
-    
-        while 1:
-            time.sleep(1./60.)
-            a = pi.act(obs)
-            obs, r, done, _ = env.step(a)
-            score += r
-            frame += 1
+  env = gym.make("HopperBulletEnv-v0")
+  env.render(mode="human")
 
+  pi = SmallReactivePolicy(env.observation_space, env.action_space)
+  env.reset()
+  while 1:
+    frame = 0
+    score = 0
+    restart_delay = 0
+    #disable rendering during reset, makes loading much faster
+    obs = env.reset()
 
-            still_open = env.render("human")
-            if still_open==False:
-                return
-            if not done: continue
-            if restart_delay==0:
-                print("score=%0.2f in %i frames" % (score, frame))
-                restart_delay = 60*2  # 2 sec at 60 fps
-            else:
-                restart_delay -= 1
-                if restart_delay==0: break
+    while 1:
+      time.sleep(1. / 60.)
+      a = pi.act(obs)
+      obs, r, done, _ = env.step(a)
+      score += r
+      frame += 1
 
-weights_dense1_w = np.array([
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-])
+      still_open = env.render("human")
+      if still_open == False:
+        return
+      if not done: continue
+      if restart_delay == 0:
+        print("score=%0.2f in %i frames" % (score, frame))
+        restart_delay = 60 * 2  # 2 sec at 60 fps
+      else:
+        restart_delay -= 1
+        if restart_delay == 0: break
 
-weights_dense1_b = np.array([ +0.0032, +0.0591, +0.1871, -0.0873, +0.0194, -0.1684, -0.0091, +0.0135, -0.0694, -0.3312, +0.0673, -0.1489, +0.1365, +0.0575, -0.1155, -0.0904, -0.1321, +0.0492, -0.2257, -0.2418, -0.0204, -0.1910, -0.0512, -0.0482, -0.2969, -0.3574, -0.1852, -0.1644, -0.2957, -0.1906, -0.0170, -0.0760, +0.0006, -0.1286, -0.0746, +0.0039, -0.1005, -0.0535, +0.0375, -0.2162, -0.1251, -0.0368, +0.0101, -0.3220, -0.1152, -0.3656, -0.2491, +0.0638, -0.0312, +0.0054, -0.2318, -0.2900, -0.1189, -0.0725, +0.0490, -0.0679, +0.0117, +0.0926, -0.0824, +0.0479, +0.0756, -0.0462, +0.1529, +0.0081, -0.0984, -0.1227, -0.0247, -0.1277, -0.0633, -0.1953, +0.1023, +0.1582, -0.0400, -0.1115, -0.1625, -0.2216, -0.0195, -0.0650, +0.0701, -0.1573, -0.1187, -0.0801, -0.1424, +0.1873, -0.2309, -0.1815, -0.0408, -0.1173, +0.0185, -0.1408, -0.0938, -0.2810, +0.2447, -0.4046, -0.1790, -0.0165, +0.1334, +0.0500, +0.0283, -0.0321, -0.2388, -0.0726, -0.3444, -0.3250, -0.1338, -0.0579, -0.1647, -0.0691, -0.0835, +0.0734, +0.1667, -0.1478, -0.2212, -0.0899, -0.0050, -0.2379, +0.0709, +0.0464, +0.1569, -0.2669, -0.2181, -0.4331, +0.0534, -0.0648, -0.1026, -0.1509, -0.2278, -0.0437])
 
-weights_dense2_w = np.array([
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-[ +0.0315, -1.1600, -0.1894, +0.1841, -0.0069, -0.9793, -0.0278, -0.9642, -1.0357, +0.5406, -0.1473, +0.2056, -0.3358, +0.3904, -0.0970, -1.3872, +0.2364, +0.2908, +0.0140, -0.1797, -0.0895, +0.1147, +0.0184, +0.2090, -0.2029, -0.0183, +0.3583, -0.6774, -0.3236, +0.4919, +0.1323, -0.3519, +0.2735, -0.4859, +0.1274, +0.0592, +0.0293, -0.0612, -0.2484, +0.0962, -0.4288, +0.5136, -0.6259, +0.2224, +0.2347, +0.0524, -0.0633, +0.0179, +0.0616, -0.9062, -0.1752, -0.0501, +0.3795, +0.4146, +0.0351, +0.3203, -0.0841, -1.0873, -1.3012, -0.3519, +0.3106, +0.3838, +0.1764, -0.1131],
-[ -0.0067, +0.0395, -0.7516, -0.2958, -0.1750, +0.4141, -0.3325, -0.3768, -0.4620, +0.8233, +0.3492, -0.0237, +0.0106, -0.8323, +0.3937, -0.1977, -0.4075, -0.2006, -0.0474, +0.3061, +0.0789, -0.2584, -0.0647, +0.1114, +0.0884, +0.4178, -0.6012, -0.1629, +0.4286, +0.4013, -0.9800, -0.6920, +0.3876, +0.7810, -0.7333, -0.0620, -0.2162, -0.4436, -0.1308, +0.0799, -0.0925, -1.3182, +0.3494, -0.7913, +0.1979, +0.5827, +0.2238, +0.0280, +0.0497, -0.0354, -0.0631, +0.8220, -0.1120, -0.2673, +0.1897, -0.6081, -0.1298, -0.7712, -0.2133, -0.6315, -0.3532, -0.9268, +0.2826, +0.2492],
-[ -0.6092, -0.1699, +0.0756, -1.0743, +0.7791, -0.0013, +0.0261, +0.0341, +0.0039, -1.3614, +0.0474, +0.1043, -0.1149, -0.3031, -0.1387, -0.2534, -0.1865, -0.0783, +0.0465, +0.1722, +0.0714, -0.3566, -0.3526, -0.3374, -0.2516, +0.0578, -1.6820, +0.6733, +0.1903, +0.3942, +0.0378, -0.0427, -0.0685, +0.2141, -0.7520, -0.5861, -0.1980, -0.8542, -0.2299, -0.4345, -0.1855, -0.1432, +0.3582, -0.4741, -0.5136, +0.3023, -0.0360, -0.0623, +0.1555, -0.1655, +0.2128, -0.4508, -1.2209, -0.0260, +0.0659, -1.1528, +0.2362, -0.5791, -0.2722, -0.1899, -0.1704, +0.0203, -0.7536, +0.2306],
-[ -0.1456, +0.4228, -0.2169, -0.1864, +0.2956, +0.0975, -0.4902, -0.4168, -0.0061, -0.0129, +0.5856, +0.0718, -0.1802, -0.3765, +0.3940, -0.0377, -0.1783, +0.1068, +0.1506, +0.1025, -0.4088, +0.1379, +0.4524, -0.2770, +0.0499, +0.0828, +0.0832, +0.1792, -0.0652, +0.3557, +0.1142, +0.1000, +0.5156, -0.0644, -0.2629, -0.4445, +0.2076, +0.1216, +0.3907, +0.0722, -0.0435, -0.1319, +0.2939, +0.1871, -0.1707, -0.1241, +0.0260, -0.0176, +0.7158, +0.0326, +0.2360, -0.0155, -0.2790, -0.0850, -0.0872, +0.0258, +0.1475, -0.2429, -0.3992, -0.5970, -0.0884, +0.0104, +0.2510, -0.3747],
-[ -0.1019, +0.0217, +0.0604, +0.2556, +0.0670, -0.2470, -0.0896, +0.0972, -0.5061, -0.0539, -0.1494, -0.6850, -0.1314, +0.4489, +0.3238, -0.1830, -0.3250, -0.4818, +0.1131, +0.0809, -0.1849, +0.0768, -0.4203, -0.1073, +0.0289, +0.0777, -0.2676, -0.1635, +0.2819, -1.1156, +0.4612, -0.5983, +0.2212, -0.2626, -0.3611, -0.8076, +0.0604, +0.1751, -0.1189, -0.8877, +0.1995, -0.5779, +0.1674, +0.1556, -0.4025, -0.7868, -0.6574, -0.0562, -0.3712, -0.0835, +0.2261, -0.0039, -0.0864, +0.1381, -0.2375, -0.1138, +0.0788, -0.7128, -2.4133, +0.2474, -0.5965, +0.0887, -0.3717, +0.1583],
-[ -0.0173, -0.8841, -0.1036, -0.4535, -0.0171, -0.5824, -0.0683, -0.1105, -0.6359, +0.0860, -0.4490, +0.1697, -0.1639, +0.2569, +0.2831, +0.2610, -0.6809, -0.4909, -0.5067, -0.2111, +0.0006, -0.9312, -0.1564, -0.0030, -0.4018, -1.0880, -0.2422, -0.5496, +0.4572, -0.2074, +0.5928, -0.6699, +0.2007, -0.1501, +0.6496, +0.0006, -0.8182, +0.1627, -0.9395, -0.1071, -0.1078, +0.5001, -0.9152, -0.0298, +0.2665, -0.2468, +0.3327, +0.1171, +0.2442, -0.4903, -0.3593, +0.0516, -0.2479, +0.0401, +0.2455, +0.1927, +0.4553, -0.4156, +0.4963, -0.4824, -0.7662, +0.1025, -0.1231, -0.0549],
-[ -0.5653, -0.0579, +0.0711, +0.1978, +0.1483, -0.2487, +0.5816, -0.2505, -1.0865, -0.3124, +0.4662, +0.3429, +0.1187, +0.3957, -0.3051, -0.2266, -0.5454, -0.4433, +0.1236, -0.2226, +0.1168, -0.1802, -0.2434, -1.0033, -0.3189, +0.1394, -0.7433, +0.5946, +0.1073, -0.4106, -0.5485, -0.5361, +0.3073, +0.3227, -0.6396, -0.2144, +0.3187, -1.2617, +0.1526, -0.4829, +0.1673, -0.4504, +0.3942, -0.6261, +0.1535, +0.1946, -0.2928, -1.2917, -0.4011, +0.0209, -0.2192, +0.1189, -0.2842, -0.3277, -0.0050, +0.1858, -0.7773, -0.1619, -0.2147, -0.6281, +0.3831, -0.8085, -0.6315, +0.4716],
-[ -0.8026, -0.3107, +0.0903, +0.0076, +0.4653, +0.2245, +0.1581, +0.3757, +0.3310, -1.2516, -0.1040, -0.6431, +0.0373, +0.0716, +0.1643, -0.6158, -0.0895, +0.2540, -0.1583, -0.0699, +0.1837, +0.4705, -0.9086, -0.0468, -0.2022, +0.2916, -0.3869, +0.1620, -0.2646, -0.1511, -0.2067, -0.3882, -0.6044, -0.1775, -0.8657, +0.2309, +0.3567, +0.0654, +0.5363, +0.0686, -0.0198, -0.9192, -0.1272, +0.5987, -0.0621, -0.3943, -0.0519, -0.5483, +0.0777, -0.5409, -0.0296, +0.0010, +0.2560, -0.1014, -0.8840, -0.4932, -0.2339, -1.0472, -0.4789, -0.2379, -0.6807, -0.0958, +0.2527, -0.5875],
-[ +0.0894, +0.2394, +0.4565, +0.5095, -0.9334, -1.2892, -0.8885, +0.0167, +1.0713, -0.3041, -0.7280, +0.2223, -0.5221, -0.0032, -0.0743, +0.1534, +0.5751, -0.1298, -0.1282, -0.4674, +0.5752, +0.2239, +0.4114, +0.3252, -0.1642, +0.2532, +0.3494, -0.8035, -0.2348, +0.3177, -0.5218, +0.0481, -0.3052, -0.9177, +0.1627, +0.6603, -0.7468, +0.0274, +0.0626, +0.0355, -0.2757, +0.2697, +0.4732, -0.2061, -0.0190, +0.5470, -0.5898, -0.8168, -0.0884, -0.4522, -0.1064, -0.8207, +0.2726, +0.1109, -0.2514, +0.1580, +0.6825, +0.7124, +0.8108, +0.2258, -0.1274, +0.2130, -0.3830, -0.9575],
-[ +0.0671, +0.2013, -0.4460, -1.0518, -0.0167, +0.1024, +0.0547, +0.3296, +0.0516, +0.0503, +0.1956, -0.0536, +0.0108, -0.2160, +0.1852, -0.0177, -0.4770, -0.2327, -0.2887, +0.2862, -0.0357, +0.0026, -0.6428, -0.5677, -0.0354, -0.0304, -0.1272, +0.1630, -0.1319, -0.1122, +0.3381, +0.0000, -1.0265, +0.1319, -0.2374, -0.1598, -0.0059, +0.1722, +0.2300, +0.1748, -0.0167, -0.0891, +0.0350, -0.4563, -0.5325, +0.0078, -0.2475, +0.1184, +0.0133, +0.2512, +0.3639, -1.5981, -0.5117, -0.2703, +0.0222, -0.5620, -0.1466, -0.1626, -0.3288, -0.0070, +0.0508, -0.3687, -0.4245, -0.2751]
+# yapf: disable
+weights_dense1_w = np.array(
+    [[
+        +0.6734, +1.1364, +0.6117, +0.5185, +0.5099, -0.2038, -0.0045, -0.1448,
+        +0.5081, +1.1685, -0.7904, -0.4623, +0.0027, -0.0473, -0.1144, +0.5095,
+        -0.1913, +0.2021, +0.3485, +0.1104, -0.4992, +0.5207, -0.1013, -0.6947,
+        +0.1624, +0.3533, -0.2485, -0.0012, +0.1674, +0.1253, +1.5485, +0.3576,
+        +0.8236, +0.7361, +0.6604, -0.0834, +0.1212, -0.8404, -0.8337, +0.3709,
+        -0.4218, -0.1011, -1.1418, -0.0554, +0.6676, +0.4739, -0.2105, +0.3187,
+        -0.4321, -0.7018, +0.1845, +0.2525, +0.0205, +0.9391, +0.6123, +0.6868,
+        +0.5116, +0.3483, +0.1148, +0.6747, -0.1590, +0.1879, +0.4836, +0.1997,
+        +0.5105, -0.2695, +0.0645, +0.5566, +0.0502, +0.2292, -0.4234, -0.3778,
+        -0.7639, -0.6084, -0.0375, -0.4799, +0.6465, -0.4097, +0.1091, -0.0681,
+        -0.1813, +0.3625, +0.6067, +0.1837, +0.1600, -0.1706, +0.0531, -0.3710,
+        +0.1320, +0.5035, +0.1106, +0.9955, -0.2657, +0.6051, -0.2525, -0.9118,
+        -0.6031, +0.2025, -0.2774, +0.3985, -0.0809, +0.3601, -0.0410, -0.5067,
+        +0.4987, +0.4126, -0.1393, -0.6596, +0.8182, -0.2352, -0.9337, +0.1438,
+        +0.3871, -0.1844, -0.4010, -0.3338, +0.1597, +0.2381, -0.4403, +0.5105,
+        +1.0354, -0.1503, +0.2731, +0.6555, +0.2048, +0.4932, -0.0067, +0.0413
+    ],
+     [
+         -0.0031, -0.2183, +0.2275, -0.4836, +0.1347, +0.3581, +0.0921,
+         +0.1900, -0.1721, +0.0150, -0.2146, +0.5346, +0.2407, -0.0791,
+         +0.5115, -0.0763, +0.2025, +0.2792, +0.4087, +0.2082, +0.0086,
+         -0.2742, +0.3703, +0.4297, -0.3141, -0.3896, +0.4101, -0.3967,
+         -0.2173, -0.0532, +0.2521, +0.3938, -0.3727, +0.6136, +0.0902,
+         -0.4617, -0.3266, -0.2273, +0.0031, +0.4605, +0.1221, +0.3603,
+         -0.1018, +0.1839, +0.3198, -0.1404, -0.1751, -0.1030, -0.5847,
+         +0.2526, -0.1280, +0.1644, +0.2988, -0.1793, -0.2120, +0.3398,
+         -0.4582, +0.0758, -0.2848, -0.1537, -0.2718, -0.2207, +0.0109,
+         +0.1374, -0.0281, -0.0494, -0.0093, +0.0098, +0.2691, +0.1589,
+         +0.2483, +0.1394, +0.2531, +0.3884, +0.0740, -0.4444, +0.1441,
+         +0.0768, +0.2154, +0.4015, -0.2109, -0.0985, -0.1066, -0.0167,
+         -0.0204, -0.2345, +0.3131, +0.1026, +0.1873, +0.2644, +0.2253,
+         -0.0173, +0.1014, +0.0781, +0.0455, -0.0489, -0.3541, -0.1257,
+         -0.1248, +0.4402, -0.0682, -0.3045, +0.1751, +0.0397, -0.3392,
+         -0.2758, +0.1542, -0.0460, -0.0622, +0.2255, +0.2442, +0.2070,
+         +0.3175, -0.0913, -0.2772, +0.7036, -0.0876, +0.0907, +0.3794,
+         +0.4784, -0.5111, +0.0982, -0.0145, +0.0397, -0.3429, -0.2727,
+         -0.1384, +0.0512
+     ],
+     [
+         +0.2192, +0.0688, -0.1092, +0.1436, -0.2598, -0.0012, -0.2220,
+         -0.2450, -0.0783, -0.0078, +0.3065, +0.1604, -0.0593, -0.0759,
+         -0.4101, -0.1819, +0.4160, +0.0141, +0.0059, -0.2494, -0.0616,
+         -0.3100, -0.3231, -0.3935, +0.1965, -0.0352, -0.0119, +0.3883,
+         -0.5905, -0.0536, -0.0617, -0.2884, +0.1134, +0.1539, +0.2002,
+         -0.3158, -0.5476, -0.4131, -0.1966, +0.1083, -0.3329, +0.1219,
+         +0.2977, -0.7132, -0.1963, -0.2359, -0.4734, +0.0130, -0.1034,
+         -0.1376, -0.1824, +0.1999, -0.3775, -0.0697, +0.1875, -0.0673,
+         -0.0673, -0.0555, -0.3243, +0.2020, +0.1416, +0.1995, -0.1931,
+         -0.6125, -0.3465, +0.0640, +0.3529, -0.4684, +0.2335, -0.0449,
+         -0.1216, +0.1921, +0.0636, -0.0420, +0.1371, -0.1246, -0.2963,
+         +0.0264, +0.3962, -0.3106, -0.2107, -0.2983, +0.0386, -0.0458,
+         -0.3277, -0.1686, -0.3064, +0.2433, +0.3454, +0.0046, +0.0885,
+         -0.2479, +0.2886, -0.0804, +0.0110, -0.1369, -0.1210, +0.1901,
+         +0.0001, -0.1155, +0.2887, -0.0943, -0.1167, +0.1746, +0.0379,
+         +0.0964, -0.2615, -0.0209, -0.0680, +0.0031, -0.0905, -0.0618,
+         -0.5630, -0.0664, -0.1976, -0.2367, -0.2360, -0.2243, +0.2448,
+         +0.2446, -0.0012, -0.6920, -0.0880, -0.4630, -0.5512, +0.0312,
+         +0.1522, -0.1278
+     ],
+     [
+         -0.2378, +0.2783, +0.0848, -0.3973, -0.5359, +0.2541, +0.1616,
+         +0.1856, +0.6054, +0.0894, +0.2599, -0.0082, -0.5433, -0.3567,
+         -0.1134, -0.2706, -0.9177, +0.1949, +0.4099, -0.3198, +0.3856,
+         -0.0285, -0.0692, +0.0882, -0.1052, -0.0122, -0.0271, -0.4122,
+         -0.0921, -0.2014, +0.0314, -0.0638, -0.0986, +0.1912, +0.6157,
+         -0.0856, +0.0450, +0.3492, -0.0958, -0.1261, +0.1242, +0.0930,
+         +0.1372, -0.1914, +0.4377, +0.0586, +0.2271, -0.1985, -0.1012,
+         -0.2074, +0.2934, -0.2271, -0.4235, -0.0077, -0.2304, -1.1012,
+         -0.1832, -0.1528, -0.4307, -0.4059, -0.4289, +0.1290, -0.1688,
+         -0.0446, +0.2036, -0.5397, -0.3280, +0.3672, -0.3012, -0.1896,
+         +0.0923, +0.1472, -0.0703, -0.0328, -0.1597, -0.3102, -0.4221,
+         +0.3261, +0.4840, +0.0083, +0.2617, -0.2548, -0.5314, -0.4192,
+         +0.6303, +0.2605, -0.6675, -0.0837, -0.8402, -0.4546, +0.2169,
+         +0.0480, +0.2543, +0.0004, +0.2982, -0.2037, -0.4812, +0.0583,
+         -0.8738, +0.0586, -0.3745, -0.5477, -0.2971, -0.1551, -0.4880,
+         -0.1060, +0.2188, -0.4043, -0.0349, -0.1349, -0.4093, -0.4371,
+         +0.3101, +0.5219, -0.0655, -0.2546, -0.6408, +0.2019, +0.1351,
+         +0.0971, -0.4105, +0.1299, -0.6688, -0.0550, -0.2388, -0.4246,
+         +0.1126, -0.5573
+     ],
+     [
+         +0.3232, -0.0110, -0.0201, -0.2022, +0.1144, -0.2510, -0.3709,
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+         +0.2011, +0.3487
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+
+weights_dense1_b = np.array([
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+    -0.0694, -0.3312, +0.0673, -0.1489, +0.1365, +0.0575, -0.1155, -0.0904,
+    -0.1321, +0.0492, -0.2257, -0.2418, -0.0204, -0.1910, -0.0512, -0.0482,
+    -0.2969, -0.3574, -0.1852, -0.1644, -0.2957, -0.1906, -0.0170, -0.0760,
+    +0.0006, -0.1286, -0.0746, +0.0039, -0.1005, -0.0535, +0.0375, -0.2162,
+    -0.1251, -0.0368, +0.0101, -0.3220, -0.1152, -0.3656, -0.2491, +0.0638,
+    -0.0312, +0.0054, -0.2318, -0.2900, -0.1189, -0.0725, +0.0490, -0.0679,
+    +0.0117, +0.0926, -0.0824, +0.0479, +0.0756, -0.0462, +0.1529, +0.0081,
+    -0.0984, -0.1227, -0.0247, -0.1277, -0.0633, -0.1953, +0.1023, +0.1582,
+    -0.0400, -0.1115, -0.1625, -0.2216, -0.0195, -0.0650, +0.0701, -0.1573,
+    -0.1187, -0.0801, -0.1424, +0.1873, -0.2309, -0.1815, -0.0408, -0.1173,
+    +0.0185, -0.1408, -0.0938, -0.2810, +0.2447, -0.4046, -0.1790, -0.0165,
+    +0.1334, +0.0500, +0.0283, -0.0321, -0.2388, -0.0726, -0.3444, -0.3250,
+    -0.1338, -0.0579, -0.1647, -0.0691, -0.0835, +0.0734, +0.1667, -0.1478,
+    -0.2212, -0.0899, -0.0050, -0.2379, +0.0709, +0.0464, +0.1569, -0.2669,
+    -0.2181, -0.4331, +0.0534, -0.0648, -0.1026, -0.1509, -0.2278, -0.0437
 ])
 
-weights_dense2_b = np.array([ +0.0266, +0.3395, +0.0968, +0.0097, +0.0443, -0.1722, -0.2537, +0.1501, +0.0233, -0.0174, +0.1783, -0.0513, -0.1531, -0.2145, +0.0299, -0.1589, +0.1143, +0.1132, -0.2700, -0.0481, +0.2876, +0.1824, +0.2418, +0.1009, -0.1451, +0.1637, +0.1703, +0.0336, -0.1405, -0.0162, -0.1158, +0.1691, -0.0223, -0.1828, +0.2352, +0.0209, -0.1362, -0.2636, +0.1271, -0.0936, -0.1436, +0.0606, +0.1311, -0.0989, -0.2877, +0.2593, +0.0781, -0.1118, -0.1264, +0.2478, +0.2398, -0.1515, +0.0959, -0.0821, -0.1740, +0.1116, -0.0682, +0.1743, +0.1746, -0.1950, -0.0951, +0.2190, -0.0294, +0.1950])
+weights_dense2_w = np.array(
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+        -0.4885, +0.3460, -0.0437, -0.4517, +0.1636, +0.0027, -0.5574, -0.4712,
+        -0.2482, +0.1202, +0.6143, -0.6510, -0.5672, -0.7578, -0.2628, -0.5118,
+        +0.2114, +0.3419, +0.3262, -0.1558, +0.4818, -0.4314, -0.4611, -0.3294,
+        +0.2443, -0.4138, +0.2451, -0.2765, -0.4818, +0.0854, -0.0011, +0.0080,
+        -0.3759, -0.1235, -0.2526, +0.3319, -0.7957, -0.3891, +0.6225, +0.7334,
+        -0.3936, -0.2982, +0.2974, -0.2766, +0.0199, -1.0206, -0.4054, -0.0960
+    ],
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+         +0.2927, +0.2728, +0.2955, -0.0641, +0.2888, +0.0278, +0.1980,
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+         -0.0534, -0.1540, +0.0872, +0.1585, -0.4927, -0.0311, +0.2163, -0.8230
+     ],
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+         -1.7876, +0.3755, +0.2791, +0.1744, -0.3218, -0.4807, -0.2936, +0.2639
+     ],
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+         -0.4035, +0.0161, -0.4715, +0.0235, +0.5217, -0.2785, +0.0707, -0.2289
+     ],
+     [
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+     ],
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+     ],
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+     [
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+         +0.3296, +0.0516, +0.0503, +0.1956, -0.0536, +0.0108, -0.2160,
+         +0.1852, -0.0177, -0.4770, -0.2327, -0.2887, +0.2862, -0.0357,
+         +0.0026, -0.6428, -0.5677, -0.0354, -0.0304, -0.1272, +0.1630,
+         -0.1319, -0.1122, +0.3381, +0.0000, -1.0265, +0.1319, -0.2374,
+         -0.1598, -0.0059, +0.1722, +0.2300, +0.1748, -0.0167, -0.0891,
+         +0.0350, -0.4563, -0.5325, +0.0078, -0.2475, +0.1184, +0.0133,
+         +0.2512, +0.3639, -1.5981, -0.5117, -0.2703, +0.0222, -0.5620,
+         -0.1466, -0.1626, -0.3288, -0.0070, +0.0508, -0.3687, -0.4245, -0.2751
+     ]])
 
-weights_final_w = np.array([
-[ -0.6826, +0.0812, +0.5225],
-[ -0.2533, +0.7043, +0.6677],
-[ +0.0684, +0.6807, +0.4726],
-[ +0.4605, +0.3736, -0.3187],
-[ +0.4913, +0.0049, +0.4186],
-[ +0.3066, +0.3756, -0.3588],
-[ -0.3078, +0.3643, -0.0335],
-[ -0.5433, +0.5926, -0.1254],
-[ -0.2532, +0.7950, -0.1380],
-[ -0.6121, -0.3012, +0.0149],
-[ +0.3181, +0.0973, +0.1705],
-[ +0.2236, -0.1020, +0.3147],
-[ -0.0038, -0.3193, +0.1068],
-[ +0.3799, -0.0215, -0.0715],
-[ +0.3440, -0.2333, -0.1001],
-[ +0.1852, +0.7445, +0.4295],
-[ +0.4888, -0.0469, -0.1287],
-[ +0.0297, -0.3913, -0.5198],
-[ -0.0092, -0.1013, +0.2193],
-[ +0.2590, +0.0846, +0.0886],
-[ +0.0744, +0.4771, -0.3553],
-[ +0.2019, +0.1670, -0.1630],
-[ -0.4549, -0.6127, +0.5951],
-[ -0.2582, -0.6401, -0.6541],
-[ +0.0248, +0.0646, +0.1598],
-[ +0.2109, -0.0794, -0.2444],
-[ +0.2435, -0.4159, -0.2927],
-[ -0.0713, +0.2441, -0.4683],
-[ -0.0737, +0.2075, +0.4609],
-[ +0.3240, -0.4084, -0.4554],
-[ +0.3463, +0.4490, -0.2689],
-[ +0.5252, -0.3714, +0.2808],
-[ +0.5561, +0.1137, -0.0258],
-[ +0.6168, +0.0587, -0.1059],
-[ -0.0737, -0.6782, -0.4729],
-[ -0.6030, -0.2577, +0.4194],
-[ +0.4061, -0.1966, +0.2603],
-[ -0.1265, +0.3940, +0.0447],
-[ +0.2460, +0.3328, +0.1654],
-[ -0.1878, +0.6672, +0.0333],
-[ +0.5618, -0.1013, -0.2911],
-[ +0.8250, -0.0788, -0.3731],
-[ +0.2661, -0.2775, +0.2854],
-[ -0.1036, +0.2179, -0.6408],
-[ -0.5371, -0.7837, +0.1141],
-[ +0.4754, -0.0923, +0.1714],
-[ +0.0448, +0.4901, -0.1578],
-[ +0.8797, +0.3316, +0.4349],
-[ +0.1514, -0.4129, -0.3333],
-[ +0.2847, +0.2285, +0.1408],
-[ +0.0045, +0.3333, +0.0694],
-[ +0.1553, -0.5147, +0.0549],
-[ -1.0765, -0.5130, +0.1472],
-[ +0.0808, -0.2612, +0.2591],
-[ +0.5104, +0.3062, +0.3979],
-[ -0.0758, -0.4512, -0.4422],
-[ +0.3236, +0.4479, -0.1759],
-[ +0.2181, -0.0028, +0.5615],
-[ -0.0254, -0.1924, +0.4608],
-[ -0.1566, +0.5634, -0.2498],
-[ -0.0042, +0.5403, +0.0272],
-[ +0.1206, +0.3941, -1.1469],
-[ +0.7642, -1.0114, +0.4905],
-[ +0.5071, +0.2270, +0.0046]
+weights_dense2_b = np.array([
+    +0.0266, +0.3395, +0.0968, +0.0097, +0.0443, -0.1722, -0.2537, +0.1501,
+    +0.0233, -0.0174, +0.1783, -0.0513, -0.1531, -0.2145, +0.0299, -0.1589,
+    +0.1143, +0.1132, -0.2700, -0.0481, +0.2876, +0.1824, +0.2418, +0.1009,
+    -0.1451, +0.1637, +0.1703, +0.0336, -0.1405, -0.0162, -0.1158, +0.1691,
+    -0.0223, -0.1828, +0.2352, +0.0209, -0.1362, -0.2636, +0.1271, -0.0936,
+    -0.1436, +0.0606, +0.1311, -0.0989, -0.2877, +0.2593, +0.0781, -0.1118,
+    -0.1264, +0.2478, +0.2398, -0.1515, +0.0959, -0.0821, -0.1740, +0.1116,
+    -0.0682, +0.1743, +0.1746, -0.1950, -0.0951, +0.2190, -0.0294, +0.1950
 ])
 
-weights_final_b = np.array([ +0.4868, -0.0987, -0.0946])
+weights_final_w = np.array([[-0.6826, +0.0812, +0.5225],
+                            [-0.2533, +0.7043, +0.6677],
+                            [+0.0684, +0.6807, +0.4726],
+                            [+0.4605, +0.3736, -0.3187],
+                            [+0.4913, +0.0049, +0.4186],
+                            [+0.3066, +0.3756, -0.3588],
+                            [-0.3078, +0.3643, -0.0335],
+                            [-0.5433, +0.5926, -0.1254],
+                            [-0.2532, +0.7950, -0.1380],
+                            [-0.6121, -0.3012, +0.0149],
+                            [+0.3181, +0.0973, +0.1705],
+                            [+0.2236, -0.1020, +0.3147],
+                            [-0.0038, -0.3193, +0.1068],
+                            [+0.3799, -0.0215, -0.0715],
+                            [+0.3440, -0.2333, -0.1001],
+                            [+0.1852, +0.7445, +0.4295],
+                            [+0.4888, -0.0469, -0.1287],
+                            [+0.0297, -0.3913, -0.5198],
+                            [-0.0092, -0.1013, +0.2193],
+                            [+0.2590, +0.0846, +0.0886],
+                            [+0.0744, +0.4771, -0.3553],
+                            [+0.2019, +0.1670, -0.1630],
+                            [-0.4549, -0.6127, +0.5951],
+                            [-0.2582, -0.6401, -0.6541],
+                            [+0.0248, +0.0646, +0.1598],
+                            [+0.2109, -0.0794, -0.2444],
+                            [+0.2435, -0.4159, -0.2927],
+                            [-0.0713, +0.2441, -0.4683],
+                            [-0.0737, +0.2075, +0.4609],
+                            [+0.3240, -0.4084, -0.4554],
+                            [+0.3463, +0.4490, -0.2689],
+                            [+0.5252, -0.3714, +0.2808],
+                            [+0.5561, +0.1137, -0.0258],
+                            [+0.6168, +0.0587, -0.1059],
+                            [-0.0737, -0.6782, -0.4729],
+                            [-0.6030, -0.2577, +0.4194],
+                            [+0.4061, -0.1966, +0.2603],
+                            [-0.1265, +0.3940, +0.0447],
+                            [+0.2460, +0.3328, +0.1654],
+                            [-0.1878, +0.6672, +0.0333],
+                            [+0.5618, -0.1013, -0.2911],
+                            [+0.8250, -0.0788, -0.3731],
+                            [+0.2661, -0.2775, +0.2854],
+                            [-0.1036, +0.2179, -0.6408],
+                            [-0.5371, -0.7837, +0.1141],
+                            [+0.4754, -0.0923, +0.1714],
+                            [+0.0448, +0.4901, -0.1578],
+                            [+0.8797, +0.3316, +0.4349],
+                            [+0.1514, -0.4129, -0.3333],
+                            [+0.2847, +0.2285, +0.1408],
+                            [+0.0045, +0.3333, +0.0694],
+                            [+0.1553, -0.5147, +0.0549],
+                            [-1.0765, -0.5130, +0.1472],
+                            [+0.0808, -0.2612, +0.2591],
+                            [+0.5104, +0.3062, +0.3979],
+                            [-0.0758, -0.4512, -0.4422],
+                            [+0.3236, +0.4479, -0.1759],
+                            [+0.2181, -0.0028, +0.5615],
+                            [-0.0254, -0.1924, +0.4608],
+                            [-0.1566, +0.5634, -0.2498],
+                            [-0.0042, +0.5403, +0.0272],
+                            [+0.1206, +0.3941, -1.1469],
+                            [+0.7642, -1.0114, +0.4905],
+                            [+0.5071, +0.2270, +0.0046]])
+
+weights_final_b = np.array([+0.4868, -0.0987, -0.0946])
+# yapf: enable
 
-if __name__=="__main__":
-    main()
+if __name__ == "__main__":
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidBulletEnv_v0_2017may.py
index 08245626b..572d495ca 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidBulletEnv_v0_2017may.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidBulletEnv_v0_2017may.py
@@ -2,502 +2,7856 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 import numpy as np
 import pybullet_envs
 import time
 
+
 def relu(x):
-    return np.maximum(x, 0)
+  return np.maximum(x, 0)
+
 
 class SmallReactivePolicy:
-    "Simple multi-layer perceptron policy, no internal state"
-    def __init__(self, observation_space, action_space):
-        assert weights_dense1_w.shape == (observation_space.shape[0], 256)
-        assert weights_dense2_w.shape == (256, 128)
-        assert weights_final_w.shape  == (128, action_space.shape[0])
+  "Simple multi-layer perceptron policy, no internal state"
+
+  def __init__(self, observation_space, action_space):
+    assert weights_dense1_w.shape == (observation_space.shape[0], 256)
+    assert weights_dense2_w.shape == (256, 128)
+    assert weights_final_w.shape == (128, action_space.shape[0])
+
+  def act(self, ob):
+    ob[0] += -1.4 + 0.8
+    x = ob
+    x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
+    x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
+    x = np.dot(x, weights_final_w) + weights_final_b
+    return x
 
-    def act(self, ob):
-        ob[0] += -1.4 + 0.8
-        x = ob
-        x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
-        x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
-        x = np.dot(x, weights_final_w) + weights_final_b
-        return x
 
 def main():
-    env = gym.make("HumanoidBulletEnv-v0")
-    env.render(mode="human")
-    pi = SmallReactivePolicy(env.observation_space, env.action_space)
-    env.reset()
+  env = gym.make("HumanoidBulletEnv-v0")
+  env.render(mode="human")
+  pi = SmallReactivePolicy(env.observation_space, env.action_space)
+  env.reset()
+  while 1:
+    frame = 0
+    score = 0
+    restart_delay = 0
+    obs = env.reset()
+
     while 1:
-        frame = 0
-        score = 0
-        restart_delay = 0
-        obs = env.reset()
-       
-        while 1:
-            a = pi.act(obs)
-            obs, r, done, _ = env.step(a)
-            score += r
-            frame += 1
-            time.sleep(1./60.)
+      a = pi.act(obs)
+      obs, r, done, _ = env.step(a)
+      score += r
+      frame += 1
+      time.sleep(1. / 60.)
 
-            still_open = env.render("human")
-            if still_open==False:
-                return
-            if not done: continue
-            if restart_delay==0:
-                print("score=%0.2f in %i frames" % (score, frame))
-                restart_delay = 60*2  # 2 sec at 60 fps
-            else:
-                restart_delay -= 1
-                if restart_delay==0: break
+      still_open = env.render("human")
+      if still_open == False:
+        return
+      if not done: continue
+      if restart_delay == 0:
+        print("score=%0.2f in %i frames" % (score, frame))
+        restart_delay = 60 * 2  # 2 sec at 60 fps
+      else:
+        restart_delay -= 1
+        if restart_delay == 0: break
 
-weights_dense1_w = np.array([
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-])
 
-weights_dense1_b = np.array([ +0.1494, +0.0039, -0.0527, -0.0962, +0.0315, -0.1903, -0.0055, -0.0615, -0.0553, -0.1921, -0.1072, -0.0451, -0.1178, -0.0108, -0.0561, -0.1273, -0.1139, -0.0796, -0.0175, -0.0684, -0.0933, -0.1067, -0.1255, +0.0234, -0.0703, -0.2935, -0.1144, -0.1077, -0.1229, -0.1456, -0.1558, +0.0667, +0.0781, -0.0370, -0.1065, -0.0846, -0.0311, +0.0140, -0.1026, -0.1604, -0.1456, -0.2161, -0.0539, -0.1573, -0.0612, -0.0970, -0.2703, -0.1411, -0.2202, +0.1398, -0.0699, -0.1028, -0.0294, -0.1635, +0.1305, -0.1769, +0.1233, -0.3288, +0.0167, +0.0028, -0.0550, -0.1999, +0.0864, +0.0176, -0.0370, +0.0562, -0.0984, +0.0783, +0.0478, -0.1250, -0.2548, -0.1360, -0.0914, -0.0499, -0.0393, +0.0658, -0.1763, -0.1398, -0.0844, -0.1208, -0.0743, -0.1800, +0.0300, -0.0678, -0.0124, -0.0126, -0.0269, -0.1089, -0.0306, -0.0458, -0.0984, -0.0951, -0.0395, +0.0311, -0.1592, -0.1021, -0.3524, -0.1006, -0.1488, -0.2021, -0.0736, -0.1389, -0.0688, +0.0139, +0.0820, -0.1823, -0.0241, -0.0035, -0.0373, +0.0326, -0.0615, +0.0254, -0.0134, +0.0239, -0.1209, -0.0289, -0.1025, -0.1184, -0.1214, -0.0290, -0.0975, +0.0992, -0.0952, -0.1083, -0.1422, -0.0548, -0.0305, -0.2508, -0.0206, -0.2072, -0.0452, +0.0112, -0.0011, +0.0417, +0.0035, +0.0200, -0.1942, -0.0485, -0.1137, +0.0025, +0.0786, +0.0299, -0.1345, +0.1579, -0.0281, -0.0762, -0.0884, +0.0861, -0.0782, -0.1839, -0.0320, -0.2262, +0.0617, -0.1688, -0.2112, -0.1037, -0.1056, -0.0510, -0.0038, +0.0895, -0.2475, -0.0174, -0.1610, +0.0683, -0.2035, -0.0045, -0.1483, +0.0695, -0.1126, -0.1221, +0.0023, -0.0862, -0.1456, -0.1136, -0.0341, +0.0209, -0.0928, -0.0567, +0.0725, -0.0127, -0.0353, -0.1072, -0.0613, -0.1992, +0.0168, -0.1877, -0.1331, -0.1073, -0.1444, +0.0069, +0.0563, -0.1909, -0.1581, -0.0970, -0.0138, -0.0487, -0.1580, -0.1599, -0.0073, -0.2733, -0.1690, -0.2064, +0.0071, -0.2040, -0.1194, -0.0529, -0.1376, -0.1651, -0.0822, -0.1775, -0.1370, -0.2621, -0.1093, -0.0536, -0.0812, +0.0422, +0.0078, -0.0437, -0.2098, -0.1435, +0.0691, -0.1066, -0.1223, +0.0092, -0.0332, -0.0460, +0.0013, -0.0559, -0.1134, -0.1300, -0.0909, +0.0010, -0.2052, -0.1058, -0.0746, +0.0049, +0.0770, -0.0889, +0.0130, -0.1252, -0.2867, -0.0034, +0.0268, -0.0217, -0.0712, -0.0839, +0.0532, -0.1541, +0.0243, -0.2173, +0.0914, -0.1751, -0.1907, -0.0940, -0.0697, -0.1361])
+# yapf: disable
+weights_dense1_w = np.array(
+    [[
+        +0.2289, +0.2584, +0.2595, +0.0173, +0.1293, -0.2980, +0.1410, +0.0982,
+        +0.0216, +0.3724, +0.2204, -0.0734, -0.2420, -0.3443, -0.2738, -0.3825,
+        +0.1504, -0.0930, -0.2680, +0.0685, +0.1592, +0.2534, -0.0787, -0.0426,
+        +0.2591, +0.2134, -0.1631, +0.5168, +0.1444, +0.2736, +0.3623, -0.3472,
+        +0.0393, -0.3056, +0.3850, -0.5231, +0.4511, +0.4223, -0.0905, +0.2265,
+        +0.1662, +0.1092, +0.0426, -0.0209, +0.3260, -0.1788, +0.5045, -0.0254,
+        +0.6684, +0.4659, +0.2193, +0.6121, -0.1771, +0.3024, +0.3233, -0.3380,
+        +0.1834, +0.1947, +0.2840, -0.0212, +0.0610, +0.0254, -0.0687, +0.3342,
+        -0.2010, +0.4851, -0.5739, -0.3228, -0.2242, +0.6149, +0.2704, -0.1006,
+        +0.3950, -0.2684, +0.0090, -0.2419, +0.1112, -0.0795, +0.0021, -0.0317,
+        +0.1345, -0.2847, +0.2323, +0.5374, -0.0119, -0.2098, +0.2074, +0.1693,
+        +0.4537, -0.1453, +0.2661, -0.2997, +0.1043, -0.2340, +0.4472, -0.0415,
+        +0.6437, +0.0279, +0.1609, +0.3353, -0.2240, -0.5433, -0.0053, +0.1863,
+        +0.1038, +0.3337, +0.3889, +0.4159, -0.0836, -0.0826, +0.0872, -0.1362,
+        +0.0061, -0.2982, -0.0074, -0.1452, -0.0655, -0.1369, -0.0493, -0.1082,
+        -0.4080, +0.4732, +0.1229, +0.3087, -0.1222, +0.3846, +0.0719, -0.4536,
+        +0.1202, -0.3903, -0.0445, +0.4052, +0.2922, +0.1095, +0.0317, +0.0974,
+        +0.1149, -0.3794, +0.4364, -0.1597, +0.2889, +0.2431, +0.6867, +0.5117,
+        -0.2517, +0.2835, -0.0365, +0.3303, +0.2569, +0.3636, -0.0945, +0.1853,
+        -0.1341, +0.2595, -0.0174, -0.3227, +0.1999, +0.2348, +0.5826, -0.0480,
+        +0.1449, +0.3866, +0.4740, +0.2486, +0.5011, -0.0334, +0.0953, +0.1072,
+        -0.0901, -0.3235, +0.3258, +0.3003, +0.1229, -0.2257, -0.1920, -0.2025,
+        -0.2022, +0.0843, +0.4563, -0.2654, +0.3158, -0.3102, +0.1105, -0.1171,
+        +0.0465, -0.0692, +0.2824, +0.2598, -0.0289, -0.7774, +0.3501, -0.0635,
+        +0.2257, -0.1644, +0.4091, -0.1967, +0.1614, +0.5468, +0.1365, +0.5054,
+        +0.1986, +0.1368, +0.0886, -0.1305, +0.4297, +0.2482, -0.1914, -0.2909,
+        +0.1784, +0.0690, +0.0224, +0.0378, +0.4991, -0.3410, +0.4281, -0.4574,
+        +0.1952, -0.0444, -0.1939, +0.1637, +0.2385, +0.1354, +0.1817, -0.3849,
+        +0.1909, +0.6585, +0.0997, +0.1376, +0.3598, +0.0611, +0.0124, -0.1492,
+        -0.0659, -0.2671, -0.3925, +0.2333, +0.4184, -0.1364, -0.0361, -0.5937,
+        -0.0327, -0.1142, -0.3334, +0.2963, +0.2350, -0.2600, +0.2784, -0.0720,
+        -0.1594, -0.2652, +0.3922, +0.7291, -0.1830, +0.2253, +0.1965, +0.1972
+    ],
+     [
+         +0.2244, +0.6008, -0.2907, -0.1769, -0.1935, +0.0902, +0.3499,
+         +0.0213, +0.1343, -0.4925, -0.0996, -0.2332, -0.0739, -0.3686,
+         -0.8946, +0.0956, +0.0229, -0.1700, -0.1541, -0.1738, -0.2712,
+         +0.2399, -0.9785, +0.0332, -0.1472, +0.2317, -0.2498, -0.5549,
+         +0.2247, +0.1835, +0.3699, +0.4326, +1.0277, +0.5037, +0.1917,
+         -0.0519, +0.6952, +0.0699, +0.5892, -0.2437, +0.4122, +0.8816,
+         +0.1263, -0.2072, +0.7932, +0.1292, -0.0770, +0.0025, +0.5216,
+         -0.0476, +0.1862, +0.5225, -0.1914, +0.2424, +0.9420, +0.3432,
+         +0.0285, +0.1507, +0.1983, -0.3111, -0.2958, -0.3750, -0.3894,
+         +0.4764, -0.0933, -0.1671, -0.3327, -0.1734, +0.3197, -0.2884,
+         +0.0234, -0.3527, -0.4019, +0.4847, +0.8950, +0.1055, +0.1383,
+         +0.2209, -0.0691, +0.6060, +0.3265, -0.0979, -0.2110, +0.6802,
+         -0.4336, -0.6381, -0.1507, +0.4082, -0.1635, -0.0835, -0.0082,
+         +0.5745, -0.2450, +0.7778, -0.2730, -0.6112, -0.0839, -0.0785,
+         -0.6745, -0.1420, +0.4217, +0.3215, -0.2859, +0.3225, +0.0936,
+         +0.0283, -0.1876, +0.4980, -0.4691, -0.0344, +0.1162, +0.1886,
+         -0.1320, +0.4492, +0.0019, -0.0631, +0.2038, -0.3549, +0.2250,
+         -0.2285, -0.0618, -0.0311, +0.7220, +0.0530, -0.3637, +0.2023,
+         -0.3015, +0.1247, +0.2858, -0.2926, +0.2305, +0.2896, +0.1855,
+         -0.3343, -0.1031, -0.3579, -0.6165, -0.3269, +0.0746, +0.2497,
+         -0.3880, -0.5785, -0.7582, -0.1729, -0.3449, -0.1357, -0.5979,
+         -0.7973, +0.1202, +0.6009, -0.0103, -0.0233, -0.0987, +0.4404,
+         +0.4355, -0.0934, -0.0910, -0.0382, -0.0268, +0.0425, +0.0329,
+         +0.7613, +0.1151, +0.1962, +0.3848, +0.6449, +0.0600, +0.8192,
+         +0.2580, +0.4444, -0.5772, -0.1268, -0.0429, +0.0785, +0.1237,
+         +0.5161, -0.3665, +0.0825, +0.1226, +0.4157, +0.4844, -0.5870,
+         -0.6568, +0.1661, +0.0846, +0.9718, +0.8856, +0.4171, -0.4568,
+         -0.0714, +0.0394, -0.1495, +0.1462, -0.1572, +1.3937, +0.1682,
+         +0.4968, -0.3699, +0.0710, +0.2328, +0.4747, -0.4286, +0.4434,
+         -0.0531, +0.8446, +0.0101, -0.4317, -0.2297, +0.4299, -0.1323,
+         +0.4804, -0.2152, +0.0161, +0.0560, -0.3013, +0.2911, +0.3542,
+         +0.3124, +0.3897, +0.1082, +0.2437, +0.0183, +0.2230, +0.0093,
+         +0.1507, -0.3895, -0.2750, +0.0991, +0.1170, -0.5877, +0.4045,
+         +1.0306, -0.1141, -0.0084, +0.3079, +0.4545, +0.0084, +0.1517,
+         -0.0344, +0.4704, -0.2666, -0.0728, -0.0447, +0.4098, -0.4524,
+         -0.4638, -0.4063, -0.2521, -0.2830, +0.1845, -0.3146, +0.4381,
+         -0.0215, +0.2613, -0.1182, +0.4527
+     ],
+     [
+         +0.1845, -0.1290, +0.0236, -0.1312, +0.0155, -0.6011, -0.0454,
+         +0.0183, -0.0613, -0.1651, +0.0204, -0.2374, +0.1045, -0.2035,
+         -0.2268, +0.2069, +0.0483, -0.3226, -0.2196, +0.0847, +0.1314,
+         +0.0426, -0.4253, -0.0748, -0.1497, -0.1902, +0.3815, +0.1306,
+         +0.0276, -0.2593, -0.0081, +0.1098, -0.0062, -0.1922, -0.0409,
+         -0.2615, +0.1296, +0.0267, -0.1602, -0.4755, +0.0039, +0.2688,
+         -0.0225, -0.1433, -0.0383, -0.0131, +0.0675, +0.1684, +0.1298,
+         +0.3818, -0.0260, +0.1636, -0.2338, +0.0062, +0.1756, -0.1825,
+         +0.1473, -0.2689, -0.1376, -0.0224, +0.2016, -0.2086, +0.0723,
+         +0.2100, -0.3345, +0.1170, -0.4292, -0.1302, -0.1132, +0.0030,
+         -0.3599, -0.1974, -0.4807, +0.0184, -0.0768, -0.0310, -0.2677,
+         -0.0838, -0.0072, -0.1049, -0.2841, -0.2426, +0.2338, +0.0917,
+         -0.1451, -0.3906, -0.0315, +0.1058, -0.0429, +0.1218, +0.0590,
+         +0.0446, -0.0043, -0.0168, -0.0899, -0.3793, -0.3134, +0.0907,
+         -0.5332, -0.0995, -0.0256, -0.2710, -0.0487, -0.0059, +0.0274,
+         -0.1885, +0.3208, +0.0437, -0.0411, -0.3716, -0.5700, +0.0576,
+         +0.0903, -0.1064, -0.1600, +0.1009, -0.1957, -0.0539, -0.2426,
+         -0.5847, -0.2240, +0.0023, -0.2533, -0.2903, -0.0328, +0.1289,
+         +0.0927, -0.2596, +0.2300, -0.4833, -0.1772, +0.3817, -0.1000,
+         -0.2391, -0.2917, -0.3748, +0.0640, +0.0005, +0.1664, +0.0173,
+         +0.2214, -0.2440, +0.0039, +0.2924, +0.3009, +0.0540, -0.3460,
+         +0.1538, +0.3727, -0.0801, +0.0130, -0.0963, -0.0172, +0.0524,
+         -0.1681, +0.1478, -0.1827, +0.2902, +0.2376, +0.0703, -0.3115,
+         -0.0634, +0.0512, +0.1366, +0.0646, -0.0811, -0.1668, +0.1052,
+         -0.1976, -0.1411, -0.1401, +0.0263, -0.0454, -0.0315, -0.1376,
+         +0.0403, +0.1526, -0.0385, +0.0782, -0.1382, +0.0399, -0.1812,
+         -0.3915, -0.1615, -0.0648, -0.3724, +0.1436, -0.1380, -0.4987,
+         -0.1783, +0.2295, -0.4115, -0.2025, -0.3245, -0.3584, -0.1729,
+         -0.0822, -0.0460, -0.1321, -0.3484, -0.4094, -0.1418, +0.0986,
+         -0.2276, -0.0267, -0.1027, -0.1794, -0.1338, -0.2575, -0.2837,
+         -0.0241, -0.2849, -0.2569, -0.0871, -0.0534, +0.1427, +0.1857,
+         -0.1384, -0.3600, -0.1343, -0.0075, +0.1601, +0.1113, -0.1131,
+         -0.1716, -0.2434, +0.1357, -0.1294, -0.2366, -0.0562, -0.1674,
+         -0.0974, -0.1556, -0.5273, -0.1928, -0.0431, +0.1909, +0.0233,
+         +0.2048, -0.2176, -0.1908, +0.0150, +0.1610, +0.0468, -0.1319,
+         +0.0579, +0.4051, -0.2020, +0.0208, -0.5383, +0.1756, +0.0117,
+         -0.2675, -0.1795, -0.1730, -0.1394
+     ],
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+weights_dense2_w = np.array(
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+weights_dense2_b = np.array([
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+    -0.0224, +0.0156, +0.0664, -0.0583, +0.1267, +0.3404, +0.2669, +0.0034
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-weights_final_b = np.array([ -0.0356, +0.0776, -0.0344, +0.1375, +0.1048, +0.3648, +0.3240, +0.1319, +0.1161, +0.3373, +0.3193, +0.0120, +0.0253, -0.2434, -0.1291, +0.1042, -0.2448])
+weights_final_w = np.array(
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+        -0.0588
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+         +0.0346, +0.0151, -0.0333
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+         -0.0632, +0.2111, -0.2802
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+         -0.1136, +0.1565, +0.1244, -0.0161, -0.0614, +0.1204, -0.0771,
+         +0.2362, +0.0355, +0.2028, -0.0155, -0.0652, -0.2262, -0.0749,
+         -0.1730, -0.0957, +0.2760
+     ],
+     [
+         +0.0634, -0.0011, -0.1770, -0.1426, +0.1599, +0.0892, -0.0261,
+         +0.0790, +0.0812, +0.4281, +0.0143, +0.1554, +0.3770, -0.0206,
+         -0.1564, +0.0818, -0.0514
+     ],
+     [
+         +0.0102, -0.1232, -0.2659, -0.1866, +0.0090, +0.2672, -0.0540,
+         +0.1100, -0.2942, -0.2454, -0.0566, -0.2273, +0.1938, +0.0229,
+         -0.1563, -0.0024, -0.0975
+     ],
+     [
+         -0.0645, -0.0004, +0.1548, +0.2129, -0.0429, -0.0628, +0.0306,
+         +0.1769, -0.0491, -0.0285, -0.0621, -0.4293, +0.1821, +0.1154,
+         -0.0027, +0.0264, -0.0107
+     ],
+     [
+         +0.3158, -0.0591, +0.0461, +0.0682, +0.0015, -0.0667, +0.1125,
+         +0.0242, -0.0998, +0.0405, -0.1104, +0.0892, -0.1950, -0.0443,
+         -0.1472, +0.1181, -0.0764
+     ],
+     [
+         -0.0697, -0.0853, +0.1715, +0.0623, +0.0484, -0.0205, -0.2043,
+         -0.0463, -0.1115, +0.0058, -0.0556, -0.0808, +0.0454, -0.0959,
+         +0.1666, +0.3646, +0.2677
+     ],
+     [
+         -0.1574, -0.2403, -0.1285, -0.0211, -0.2054, -0.0027, +0.1099,
+         -0.1112, -0.2730, -0.1247, +0.1788, -0.1336, -0.1028, +0.1764,
+         +0.2594, -0.0231, +0.1732
+     ],
+     [
+         +0.2124, +0.1167, -0.0895, -0.0324, +0.2043, +0.0680, -0.0162,
+         -0.0062, -0.0897, +0.0854, +0.0748, +0.3693, -0.0414, +0.0972,
+         +0.0094, +0.0354, +0.1289
+     ],
+     [
+         +0.2460, -0.1170, -0.1432, -0.0896, +0.0093, +0.0557, -0.0195,
+         +0.0088, +0.0839, +0.0375, +0.1659, -0.0451, -0.1184, -0.2017,
+         +0.0481, -0.1517, +0.1372
+     ],
+     [
+         -0.0602, +0.3316, -0.0452, -0.0173, +0.0148, +0.0459, +0.0167,
+         -0.0842, +0.0428, +0.2873, -0.0527, +0.0752, -0.0441, +0.0583,
+         +0.0443, +0.1112, -0.0272
+     ],
+     [
+         +0.0051, -0.0555, +0.1717, +0.1398, -0.1614, -0.0300, -0.1820,
+         -0.0658, +0.0742, -0.0847, -0.1139, +0.0728, +0.1420, +0.2099,
+         -0.2902, +0.1338, +0.1417
+     ],
+     [
+         -0.0094, -0.0020, -0.1126, -0.2572, -0.2766, +0.0049, +0.1369,
+         +0.0054, -0.1328, +0.0361, +0.1361, +0.3090, +0.0047, +0.1695,
+         +0.0896, -0.1901, -0.1786
+     ],
+     [
+         +0.1339, -0.0886, +0.0770, +0.0189, -0.1017, -0.0070, +0.1551,
+         -0.0489, +0.0966, -0.0131, -0.0856, -0.1183, +0.1585, -0.0139,
+         +0.1861, -0.1487, +0.0005
+     ],
+     [
+         +0.2260, +0.0098, -0.3146, -0.0441, +0.1948, +0.0520, +0.0863,
+         -0.0021, -0.1183, +0.0328, +0.0124, +0.0056, +0.0944, -0.0319,
+         +0.0679, +0.0774, +0.0409
+     ],
+     [
+         -0.0237, -0.3453, -0.1483, -0.2272, +0.1675, -0.1041, -0.0126,
+         +0.3376, -0.0138, +0.1525, -0.0322, +0.2070, -0.2725, -0.0600,
+         -0.1466, -0.2563, +0.0551
+     ],
+     [
+         +0.1966, -0.0838, -0.0537, +0.0933, -0.0060, -0.0721, -0.0399,
+         -0.1153, -0.1987, -0.0413, +0.2588, +0.0648, -0.0473, +0.1387,
+         +0.6180, +0.0229, -0.2610
+     ],
+     [
+         -0.1566, -0.0589, -0.0600, +0.0966, +0.0738, -0.0744, -0.0010,
+         +0.0253, +0.2387, -0.0539, +0.0625, +0.1197, +0.0486, +0.0605,
+         +0.2775, -0.0594, -0.0759
+     ],
+     [
+         -0.4288, -0.0693, +0.0067, +0.3567, +0.0813, -0.1369, -0.3431,
+         +0.0559, -0.0320, +0.0158, -0.1947, -0.3235, +0.1294, -0.2915,
+         -0.2301, -0.1980, +0.0892
+     ],
+     [
+         -0.1741, +0.0445, -0.0525, +0.0650, -0.0474, +0.1845, -0.0831,
+         -0.0693, -0.0071, -0.0772, +0.0915, -0.0571, -0.1035, -0.0038,
+         -0.1508, -0.2323, -0.1788
+     ],
+     [
+         +0.1543, -0.0994, +0.0674, +0.1953, +0.1254, -0.1263, +0.0854,
+         -0.1048, +0.0106, -0.0203, +0.1019, -0.0881, +0.2788, -0.2237,
+         -0.0723, +0.0393, -0.0969
+     ],
+     [
+         +0.0560, +0.2425, +0.1031, +0.1155, -0.1187, -0.1155, -0.0789,
+         +0.2113, +0.0024, +0.2648, -0.1811, -0.0259, -0.1363, -0.2149,
+         -0.2199, -0.1105, -0.1616
+     ],
+     [
+         -0.0967, +0.0007, -0.2230, +0.1197, +0.1085, +0.0267, -0.0633,
+         +0.1611, -0.0078, +0.0088, -0.0879, -0.1362, +0.1364, -0.1784,
+         +0.1725, -0.0491, -0.0177
+     ],
+     [
+         +0.0724, -0.0574, +0.0696, -0.0095, -0.1601, +0.1243, +0.2373,
+         -0.1058, +0.1610, -0.1874, -0.0689, +0.1806, -0.2117, -0.0855,
+         -0.0657, +0.0366, -0.0797
+     ]])
+
+weights_final_b = np.array([
+    -0.0356, +0.0776, -0.0344, +0.1375, +0.1048, +0.3648, +0.3240, +0.1319,
+    +0.1161, +0.3373, +0.3193, +0.0120, +0.0253, -0.2434, -0.1291, +0.1042,
+    -0.2448
+])
+# yapf: enable
 
-if __name__=="__main__":
-    main()
+if __name__ == "__main__":
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidFlagrunHarderBulletEnv_v1_2017jul.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidFlagrunHarderBulletEnv_v1_2017jul.py
index 916094b1f..84220d533 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidFlagrunHarderBulletEnv_v1_2017jul.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HumanoidFlagrunHarderBulletEnv_v1_2017jul.py
@@ -2,7 +2,7 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 import numpy as np
@@ -10,500 +10,7853 @@ import pybullet_envs
 import os.path
 import time
 
+
 def relu(x):
-                return np.maximum(x, 0)
+  return np.maximum(x, 0)
+
 
 class SmallReactivePolicy:
-                "Simple multi-layer perceptron policy, no internal state"
-                def __init__(self, observation_space, action_space):
-                                assert weights_dense1_w.shape == (observation_space.shape[0], 256)
-                                assert weights_dense2_w.shape == (256, 128)
-                                assert weights_final_w.shape  == (128, action_space.shape[0])
+  "Simple multi-layer perceptron policy, no internal state"
+
+  def __init__(self, observation_space, action_space):
+    assert weights_dense1_w.shape == (observation_space.shape[0], 256)
+    assert weights_dense2_w.shape == (256, 128)
+    assert weights_final_w.shape == (128, action_space.shape[0])
 
-                def act(self, ob):
-                                x = ob
-                                x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
-                                x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
-                                x = np.dot(x, weights_final_w) + weights_final_b
-                                return x
+  def act(self, ob):
+    x = ob
+    x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
+    x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
+    x = np.dot(x, weights_final_w) + weights_final_b
+    return x
 
 
 def demo_run():
-    gui = True
-    env = gym.make("HumanoidFlagrunHarderBulletEnv-v0")
-    if (gui):
-      env.render(mode="human")
+  gui = True
+  env = gym.make("HumanoidFlagrunHarderBulletEnv-v0")
+  if (gui):
+    env.render(mode="human")
+
+  pi = SmallReactivePolicy(env.observation_space, env.action_space)
+  while 1:
+    frame = 0
+    score = 0
+    restart_delay = 0
+    obs = env.reset()
 
-    pi = SmallReactivePolicy(env.observation_space, env.action_space)
     while 1:
-        frame = 0
-        score = 0
-        restart_delay = 0
-        obs = env.reset()
+      a = pi.act(obs)
+      obs, r, done, _ = env.step(a)
+      score += r
+      frame += 1
+      if (gui):
+        time.sleep(1. / 60)
+
+      still_open = env.render("human")
 
-        while 1:
-            a = pi.act(obs)
-            obs, r, done, _ = env.step(a)
-            score += r
-            frame += 1
-            if (gui):
-              time.sleep(1./60)
+      if still_open == False:
+        return
+      if not done: continue
+      if restart_delay == 0:
+        print("score=%0.2f in %i frames" % (score, frame))
+        if still_open != True:  # not True in multiplayer or non-Roboschool environment
+          break
+        restart_delay = 60 * 2  # 2 sec at 60 fps
+      restart_delay -= 1
+      if restart_delay == 0: break
 
-            still_open = env.render("human")
 
-            if still_open==False:
-                return
-            if not done: continue
-            if restart_delay==0:
-                print("score=%0.2f in %i frames" % (score, frame))
-                if still_open!=True:      # not True in multiplayer or non-Roboschool environment
-                    break
-                restart_delay = 60*2  # 2 sec at 60 fps
-            restart_delay -= 1
-            if restart_delay==0: break
+# yapf: disable
+weights_dense1_w = np.array(
+    [[
+        +0.3543, -0.3833, +0.3987, +0.1828, +0.5746, +0.3807, +0.1427, +0.2510,
+        -0.3705, -0.2212, +0.0019, +0.7898, +0.2609, -0.5883, -0.1780, +0.1319,
+        +0.4746, -0.5833, -0.4251, +0.5949, +0.6170, -0.2344, +0.1624, -0.7524,
+        -0.1314, +0.3180, -0.3183, -0.5343, +0.6109, -0.0071, +0.0890, +0.1470,
+        +0.3201, -0.1915, -0.1192, -0.2254, +0.1921, +0.5020, +0.8814, +0.7632,
+        +0.2973, -0.0293, +0.0134, -0.1171, +0.4190, +0.2720, -0.1020, +0.2688,
+        -0.3085, -0.0548, +0.4256, +0.5690, -0.2243, +0.4055, +0.7467, +0.2371,
+        -0.1666, -0.3083, -0.2019, -0.1256, -0.2922, -0.0295, -0.1915, +0.4345,
+        +0.4745, +0.4599, +0.7173, +0.1671, -0.1942, +0.6437, +0.1593, -0.2243,
+        -0.3772, +0.0888, +0.0181, +1.5305, +0.1103, -0.0983, +0.5553, -0.2304,
+        +0.1091, -0.7436, -0.4132, -0.0910, -0.3729, +0.5907, -0.0080, -0.3815,
+        +0.4647, -0.4074, +0.2752, +0.2604, +0.2102, +0.1988, +0.5086, +0.1865,
+        -0.5980, +0.1655, +0.3442, -0.0285, +0.4722, -0.4559, -0.8286, +0.2297,
+        +0.1441, +0.5148, +0.9493, +0.3310, +0.1502, +0.1993, +0.4227, +0.8433,
+        -0.3427, +0.7080, +0.3844, +0.4788, +0.0027, +0.0413, -0.2622, -0.2789,
+        -0.1309, -0.0235, +0.9649, -0.3073, +0.4120, -0.1224, +0.3168, -0.4171,
+        +0.2409, +0.8089, +0.2216, +0.7449, +0.1001, -0.0277, -0.2632, +0.0697,
+        +0.2002, +0.4163, -0.1231, +0.3181, +0.4182, +0.9432, +0.5265, +0.0247,
+        +0.0824, +0.2249, -0.5920, +0.1227, -0.4454, +0.2406, +0.2175, +0.4905,
+        +0.2078, +0.1180, -0.2074, +0.2553, -0.4872, +1.0349, +0.0574, -0.0449,
+        -0.3136, +0.1975, -0.1428, +0.1385, +0.0113, -0.0366, -0.2522, +0.7889,
+        -0.5059, +1.0163, +0.4410, +0.0459, -0.2020, +0.3168, -0.8575, +0.4499,
+        -0.0539, -0.0231, -0.0449, +0.0083, +0.0040, +1.1597, -0.3938, +0.0570,
+        +0.2773, +0.7838, +0.5444, +0.3460, +0.0922, -0.0916, -0.1977, -0.2641,
+        +0.5723, +0.1033, -0.6443, +0.1281, +0.4627, +0.6561, +0.0765, +0.2997,
+        +0.4318, -0.4581, +0.0353, +0.1669, +0.0664, -0.0233, +0.0447, +0.3139,
+        +0.0570, +0.0322, +0.7074, -0.0692, +0.0741, +0.1177, +0.7109, +0.1507,
+        -1.0050, +0.0803, +0.1696, -0.7228, -0.3827, +0.3475, -0.0327, +0.0961,
+        +0.5664, +0.2477, +0.3436, -0.0406, +0.2801, +0.1499, +0.0971, -0.2689,
+        +0.0691, -0.1481, -0.1288, +0.5560, +0.2778, +0.2370, +0.1795, +0.3816,
+        +0.8086, +0.2443, -0.2036, +0.8701, +0.3228, +0.0493, +0.4232, +0.2833,
+        +0.1421, +0.0004, -0.3722, -0.8666, -0.1084, -0.2561, -0.2230, -0.0161
+    ],
+     [
+         +0.0958, -0.1782, +0.3288, +0.0482, +0.0290, -0.3382, +0.3124,
+         +0.1661, +0.0309, +0.0167, +0.3039, -0.2731, +0.2352, -0.0909,
+         +0.0472, -0.0685, +0.2378, +0.0030, -0.0057, -0.2164, +0.1759,
+         +0.2240, +0.1018, +0.3923, +0.2377, +0.2124, -0.2076, +0.2497,
+         +0.2697, -0.2019, -0.1881, -0.0446, -0.4666, +0.4909, -0.0879,
+         -0.3494, -0.3057, -0.0409, +0.0555, -0.1819, -0.0586, -0.2323,
+         +1.0968, -0.1451, +0.0927, +0.1340, -0.3688, -0.0321, -0.1371,
+         +0.2704, -0.2831, +0.0576, +0.2786, -0.4570, +0.0034, -0.1193,
+         -0.2615, +0.1571, -0.0981, +0.1934, +0.2114, +0.4198, +0.0797,
+         +0.0997, -0.0848, -0.1339, +0.2575, -0.0421, +0.2907, +0.2311,
+         +0.2077, -0.1234, +0.2197, +0.3210, +0.2515, -0.1523, +0.0573,
+         +0.0863, +0.0221, -0.1319, +0.1101, -0.1512, -0.0253, +0.3307,
+         +0.3122, +0.2278, +0.0856, +0.0814, -0.2028, -0.0373, -0.2210,
+         +0.2000, +0.1657, -0.0533, +0.1978, -0.2226, +0.0377, +0.4188,
+         +0.0766, +0.2088, +0.2568, -0.0574, -0.3792, +0.2971, -0.1956,
+         -0.0845, -0.1638, -0.0269, -0.2203, +0.5461, +0.0076, +0.3380,
+         -0.2262, +0.1973, -0.2503, -0.1507, -0.2942, +0.1642, -0.2460,
+         -0.1684, -0.0207, +0.0026, +0.3213, -0.0170, +0.5102, -0.2842,
+         +0.2971, -0.2165, +0.1205, -0.0015, +0.0166, +0.1441, -0.1231,
+         +0.0713, +0.1159, -0.3383, +0.0572, -0.0782, -0.0589, +0.2331,
+         -0.0825, -0.0095, -0.1379, -0.0909, +0.0111, -0.0969, +0.1849,
+         -0.1454, +0.1298, -0.1478, -0.1134, -0.0017, +0.4045, +0.1368,
+         -0.2911, +0.0401, -0.0180, +0.5063, +0.3238, +0.1427, -0.1532,
+         -0.0783, -0.1042, -0.0411, +0.0817, -0.1855, +0.0211, +0.0212,
+         +0.0748, -0.1480, -0.0379, +0.0186, +0.0107, -0.0290, +0.0324,
+         +0.0711, +0.2620, +0.1047, -0.4431, -0.1354, +0.0647, +0.1472,
+         -0.0249, -0.3687, +0.4135, +0.1640, -0.1606, +0.1635, -0.2894,
+         +0.1293, +0.1063, -0.3286, -0.5707, -0.1076, +0.2624, +0.0245,
+         -0.3619, +0.0624, -0.1214, +0.3565, -0.2067, +0.2906, +0.2800,
+         +0.0814, +0.4657, +0.1716, -0.2339, +0.1425, -0.2436, -0.0386,
+         +0.6793, +0.0337, +0.3238, +0.2363, -0.1065, -0.3087, -0.1684,
+         -0.0784, +0.1175, +0.1939, +0.5676, -0.1382, -0.1300, +0.6986,
+         -0.1262, -0.1023, -0.6929, +0.0871, -0.2003, -0.2158, -0.4234,
+         +0.1276, +0.2671, +0.1822, +0.7594, +0.1000, +0.4422, +0.0027,
+         +0.0716, +0.2667, +0.1928, +0.2579, -0.1292, -0.1112, -0.0446,
+         -0.1893, +0.5958, -0.0906, +0.1439, +0.3357, +0.4613, +0.2427,
+         -0.2639, -0.2407, -0.0296, +0.1122
+     ],
+     [
+         -0.5187, +0.2287, +0.2752, +0.1228, -0.4072, +0.0853, -0.0049,
+         -0.0030, -0.0514, +0.2529, +0.0103, +0.3002, -0.1213, -0.3048,
+         +0.1508, +0.0940, +0.1131, -0.0398, -0.1921, +0.0943, +0.3511,
+         +0.1262, -0.1832, -0.0754, -0.3122, -0.1396, -0.1342, +0.0026,
+         +0.1042, +0.1073, -0.3781, +0.1753, -0.1475, -0.0098, -0.1979,
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+weights_dense2_w = np.array(
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+    +0.1154, -0.0607, +0.3318, +0.1063, +0.2589, +0.3722, -0.0955, +0.1107,
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+    -0.0581, +0.1114, +0.0446, +0.0422, +0.1618, +0.1712, +0.1250, +0.0345
 ])
 
-weights_dense2_b = np.array([ -0.1167, -0.0510, +0.1155, +0.2151, -0.0416, +0.1452, -0.0794, +0.1217, -0.0268, +0.1016, +0.1274, -0.0271, -0.1298, +0.0793, +0.0404, +0.0955, -0.0855, -0.0578, +0.3129, +0.0950, -0.0090, -0.0090, +0.2334, +0.1715, +0.1043, +0.0024, -0.0009, +0.0342, +0.0742, +0.1323, +0.0760, -0.0173, -0.0577, +0.1013, +0.0974, -0.0338, +0.0429, +0.3263, +0.1869, +0.1798, -0.0126, -0.1002, +0.1193, +0.1248, -0.1544, +0.1357, -0.0956, +0.1405, +0.1551, +0.4303, +0.1871, -0.0234, +0.1528, +0.3371, +0.3916, +0.2319, +0.1516, +0.1534, +0.1397, -0.1125, -0.0907, +0.0794, -0.0481, +0.1252, -0.0279, +0.0712, -0.0944, +0.2633, +0.0062, -0.0458, +0.2715, +0.1041, -0.1381, -0.0402, +0.1133, -0.0761, +0.1408, +0.2070, -0.0576, -0.1057, -0.0464, +0.1069, -0.0581, +0.2263, +0.0632, -0.0277, +0.0625, +0.2455, +0.1857, -0.0172, -0.0332, -0.0384, +0.1591, +0.1721, +0.1568, +0.1680, +0.0232, +0.1134, +0.2353, +0.0235, +0.1007, -0.0667, -0.1689, +0.1512, +0.1154, -0.0607, +0.3318, +0.1063, +0.2589, +0.3722, -0.0955, +0.1107, +0.0735, +0.0284, +0.2233, -0.0427, +0.1083, +0.2224, +0.1169, +0.3346, -0.0581, +0.1114, +0.0446, +0.0422, +0.1618, +0.1712, +0.1250, +0.0345])
+weights_final_w = np.array(
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+        -0.1780, -0.1288, +0.2905, -0.0481, -0.5252, -0.0515, -0.1952, -0.2929,
+        -0.0497
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+         -0.2782, +0.0571, +0.0573
+     ],
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+         +0.7842, +0.8608, -0.2391
+     ],
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-[ +0.1821, +0.4514, -0.0363, +0.2531, -0.1088, -0.0400, -0.1291, +0.1353, +0.1665, +0.3382, -0.4276, -0.1547, -0.1877, +0.1149, -0.0641, -0.4630, +0.1352],
-[ +0.1122, -0.1090, +0.3136, +0.3880, +0.1287, +0.0237, +0.0728, +0.0179, +0.0349, -0.1230, +0.0111, +0.6014, -0.3108, -0.0339, -0.2139, +0.1688, +0.0408],
-[ -0.0585, +0.1857, -0.5246, +0.2287, +0.2376, +0.0977, +0.1277, +0.8259, -0.0420, -0.0909, +0.2259, -0.2873, -0.0027, +0.0197, +0.4685, -0.2872, -0.1987],
-[ -0.0200, -0.2907, +0.1864, -0.1628, -0.1605, +0.1749, -0.2185, -0.1611, -0.1128, -0.0374, +0.2906, +0.3250, +0.4870, +0.0309, -0.3275, +0.0048, -0.1310],
-[ -0.0080, -0.4754, +0.1296, -0.0301, -0.3817, -0.1197, +0.4911, -0.4934, -0.2824, -0.0310, +0.0279, +0.2155, -0.6603, +0.0401, -0.8678, +0.4578, -0.0707],
-[ -0.1653, +0.0769, -0.1368, -0.0154, -0.2174, -0.0164, +0.2595, +0.2301, +0.1736, -0.0489, -0.3271, -0.0782, -0.0477, -0.2108, -0.0857, -0.0149, -0.1159],
-[ -0.0895, +0.3569, +0.0548, +0.3007, +0.2871, +0.1508, -0.0002, +0.3219, +0.1019, +0.1113, -0.0978, +0.2202, -0.2125, -0.0533, +0.6329, +0.2266, +0.0218],
-[ -0.0314, +0.2153, -0.1019, -0.1699, +0.0126, +0.0158, -0.3012, -0.0264, +0.2299, -0.0200, +0.0445, +0.3879, +0.5208, +0.2000, +0.4442, -0.4519, +0.0081],
-[ -0.0303, +0.2435, -0.1155, -0.0029, +0.0924, +0.1226, -0.0519, +0.4167, -0.0235, -0.0145, +0.0102, +0.4426, +0.3652, +0.0790, +0.0670, +0.3292, -0.1155],
-[ -0.4764, +0.1008, +0.2453, +0.6503, -0.2319, -0.2316, +0.1459, -0.3701, +0.4247, +0.1477, +0.0578, +1.2480, +0.2435, -0.2183, +0.2025, -0.5131, +0.0148],
-[ +0.0369, -0.6000, +0.2335, +0.2427, -0.2383, -0.2967, -0.1404, -0.0762, -0.1701, +0.0050, -0.3853, +0.2045, +0.1601, -0.0450, +0.3609, -0.2774, -0.2418],
-[ -0.1558, +0.1644, -0.1136, +0.2121, -0.0032, -0.2172, -0.0341, -0.0589, +0.2133, +0.1157, +0.3303, +0.3821, +0.4223, +0.0544, -0.1871, +0.2528, +0.0532],
-[ -0.1705, +0.0316, +0.0959, -0.4905, -0.1434, -0.4177, -0.0139, +0.0173, +0.2193, +0.0451, -0.4526, -0.1873, +0.4062, +0.0100, -1.2451, +1.2191, -0.2005],
-[ -0.0578, -0.1543, +0.0090, +0.2091, -0.0905, -0.3092, -0.0324, -0.9097, +0.1443, +0.0027, +0.5500, -0.5892, -0.6884, +0.0737, +0.3726, +0.0123, -0.3109],
-[ +0.0170, +0.1850, +0.0151, +0.0633, +0.4575, +0.1327, +0.0153, -0.1831, -0.2411, -0.0660, -0.0843, +0.2057, -0.1343, +0.0240, +0.6390, -0.3008, +0.2598],
-[ +0.2004, -0.2134, -0.3945, -0.3735, +0.0749, -0.0204, +0.1091, +0.0723, -0.3266, -0.0416, -0.1158, -0.0958, +0.0165, -0.0369, +0.3759, -0.0372, -0.0946],
-[ -0.0773, -0.0728, -0.4872, -0.3943, -0.2244, -0.1453, -0.0684, -0.0067, -0.2013, +0.1170, +0.1176, +0.1532, -0.1420, -0.0700, -0.0435, +0.1984, -0.1804],
-[ +0.3493, +0.5615, +0.4853, -0.0299, +0.3986, +0.2032, +0.1871, +0.3303, -0.0351, -0.0214, -0.3183, +0.2865, -0.1298, +0.1266, +0.2793, -0.0451, -0.0698],
-[ +0.3691, -0.0414, +0.1429, -0.1778, +0.3977, -0.0978, -0.3099, -0.2705, +0.1606, -0.0288, -0.1289, -0.1052, -0.0404, +0.0166, -0.5058, -0.4936, -0.0004],
-[ +0.1870, -0.4584, +0.1216, -0.0170, +0.2037, -0.1470, +0.3353, +0.1069, -0.0034, -0.4070, +0.3482, -0.5598, -0.1958, +0.0897, +0.1491, -0.0716, +0.1305],
-[ +0.0389, +0.0905, +0.1360, -0.1183, +0.2531, +0.1238, -0.2772, +0.2455, +0.4092, -0.0462, -0.0588, -0.0419, +0.1674, +0.0068, -0.1061, +0.0287, -0.0828],
-[ +0.1001, -0.4006, +0.0288, -0.3762, -0.0410, +0.2275, +0.1096, -0.1166, -0.1334, -0.4214, -0.4301, +0.3060, -0.2374, +0.0423, -0.3930, -0.2825, +0.1954],
-[ +0.1034, +0.6710, +0.3191, +0.3582, -0.1224, +0.1598, -0.1600, +0.0934, -0.1515, +0.0675, +0.2047, +0.1186, -0.1772, +0.0256, -0.1494, +0.0952, +0.1113],
-[ -0.4012, -0.1076, +0.0205, -0.5024, +0.0729, +0.1485, +0.0070, +0.1607, -0.0114, -0.1145, -0.1079, +0.3810, +0.1917, -0.0099, +0.0344, +0.3523, -0.0808],
-[ -0.2240, +0.1399, +0.1148, +0.2574, +0.0202, +0.0974, +0.0275, +0.4080, +0.2255, +0.0175, +0.0463, -0.1122, -0.6143, -0.1468, -0.5921, +0.6715, +0.0307],
-[ -0.3402, -0.2659, -0.1670, +0.3877, +0.1549, -0.0218, +0.0430, +0.1702, +0.0147, -0.0137, +0.0798, -0.2539, -0.0238, -0.0276, +0.1152, -0.1507, -0.0094],
-[ -0.0436, +0.2453, -0.0635, -0.0957, +0.0092, +0.2026, +0.0469, -0.0911, -0.0252, -0.0527, +0.1833, -0.0522, -0.3532, -0.0643, +0.8052, -0.2086, +0.1393],
-[ +0.1356, +0.2174, -0.4187, -0.1698, -0.3939, -0.3418, +0.0977, -0.4303, -0.1747, +0.3483, -0.3828, +0.6477, -0.0193, -0.0432, -0.3092, +0.0965, +0.0512],
-[ -0.0526, +0.0788, -0.0251, -0.0711, +0.1021, -0.0033, -0.1653, -0.4175, +0.6269, +0.1131, -0.0403, -0.9119, -0.0319, +0.0312, -0.7391, +0.4969, -0.2035],
-[ +0.0237, -0.3621, +0.6398, -0.4092, -0.3548, +0.1151, -0.2588, +0.1652, -0.9864, -0.0429, -0.2583, -0.7839, -0.1742, -0.1615, -0.5220, +0.3167, +0.0245],
-[ +0.2773, -0.2533, -0.1067, +0.1248, -0.0263, -0.0443, -0.6161, -0.1194, +0.0183, -0.0374, -0.0112, +0.0789, -0.4672, -0.0865, -0.3901, +0.1714, -0.0693],
-[ +0.3108, +0.0953, -0.1789, -0.1484, +0.0256, +0.0596, +0.3846, -0.1545, -0.1468, +0.0058, +0.0122, +0.0373, +0.1194, +0.1557, +0.2206, -0.2213, +0.0944],
-[ -0.3364, +0.0759, +0.0602, +0.0081, -0.1324, -0.1008, +0.0013, -0.6405, +0.2720, +0.0274, -0.0404, -0.1801, +0.1287, -0.0663, +0.4262, +0.3619, -0.0217],
-[ -0.4219, -0.0153, -0.1689, -0.4714, -0.3856, +0.1550, -0.0396, +0.3023, -0.2935, +0.0254, -0.0017, +0.1311, +0.0634, -0.1347, -0.5716, +0.1617, -0.0855],
-[ +0.1646, +0.1777, -0.1430, -0.0439, +0.0316, -0.1789, +0.2856, +0.1370, +0.2351, +0.2530, -0.3670, -0.1025, -0.2779, +0.0917, +0.1178, +0.3486, -0.0894],
-[ -0.2763, +0.1173, -0.0395, -0.3646, -0.2316, +0.0089, +0.0877, -0.2104, -0.1127, +0.0016, +0.0464, +0.0721, +0.0388, +0.0491, +0.3302, +0.0052, -0.0643],
-[ +0.1751, -0.3577, +0.0741, -0.0451, -0.0270, -0.0501, +0.1754, -0.4259, +0.0053, -0.1944, +0.0852, -0.5543, +0.3060, +0.2323, +0.5691, -0.0617, +0.0329],
-[ -0.1774, +0.0011, +0.2617, +0.1197, +0.0459, -0.0254, +0.1981, +0.3108, +0.0400, -0.1453, -0.0713, -0.3316, +0.1455, -0.0747, +0.2722, +0.3458, -0.0431],
-[ +0.2174, +0.0580, -0.3161, +0.3940, -0.2644, -0.1984, -0.1044, +0.2935, +0.0019, +0.3177, -0.0683, -0.4946, +0.2040, +0.2826, -0.1606, -0.4582, +0.2052],
-[ +0.0825, -0.2587, -0.6098, -0.7666, -0.1891, +0.1545, -0.1752, -0.1813, -0.3137, -0.1742, -0.0400, +0.3352, -0.3175, +0.0617, +0.2097, -0.1684, -0.0412],
-[ -0.1112, +0.2379, -0.0920, -0.0707, -0.0307, +0.0797, -0.1313, -0.1122, -0.3038, +0.0529, -0.3645, -0.2686, +0.0730, -0.0592, -0.0104, +0.1403, +0.0906],
-[ +0.3680, +0.0528, +0.1097, +0.1391, -0.0911, +0.0238, +0.0677, +0.1313, -0.3832, -0.0146, +0.3670, -0.3521, -0.0276, +0.1317, +0.1161, -0.2734, +0.0763],
-[ +0.1579, +0.1185, +0.2387, -0.5239, -0.3156, -0.0856, +0.1244, -0.2377, -0.0763, +0.1003, -0.1049, -0.3365, +0.0325, -0.0759, -0.1114, -0.1658, -0.1217],
-[ -0.1804, -0.4128, +0.1567, +0.5702, -0.3200, +0.0408, -0.0244, -0.0840, -0.0527, -0.1145, +0.1109, +0.2210, +0.3458, +0.0023, -0.3264, -0.4558, +0.1183],
-[ +0.0659, -0.1952, -0.1324, +0.1010, -0.2289, -0.1007, +0.0708, +0.1144, -0.0813, +0.0920, +0.3396, +0.0104, +0.1072, -0.0226, -0.1626, -0.1254, +0.1932],
-[ +0.1231, +0.2101, -0.1953, -0.3647, +0.4056, -0.1017, -0.0869, -0.1301, +0.1228, -0.0524, -0.0305, +0.0722, -0.0188, +0.0059, -0.0921, -0.0607, +0.1297],
-[ +0.3724, +0.0801, +0.2260, +0.3577, +0.0365, -0.1573, +0.4085, +0.1012, +0.2122, +0.1751, -0.0605, +0.3211, +0.3520, +0.0157, -0.3718, +0.2995, -0.2521],
-[ +0.0650, -0.0647, +0.2336, -0.0282, -0.0795, +0.1878, +0.1919, +0.1609, +0.0767, -0.2512, +0.1858, -0.4086, +0.2826, +0.1826, +0.1671, +0.0621, +0.1772],
-[ +0.2919, -0.1500, +0.1861, -0.1115, -0.0102, -0.2352, -0.0982, +0.1106, +0.3164, +0.0698, -0.4432, +0.2496, +0.0924, +0.0937, -0.2256, -0.2866, +0.0928],
-[ -0.1124, +0.0442, +0.3579, +0.1774, +0.1772, +0.2148, +0.2384, +0.2642, -0.0853, -0.7385, +0.1676, -0.0897, -0.0031, +0.0578, -0.0487, -0.0222, +0.1840],
-[ -0.1470, +0.0944, +0.3412, +0.3212, +0.2143, +0.3426, -0.1407, -0.0518, -0.3807, -0.0837, -0.0714, +0.2675, -0.3793, -0.3674, -0.3998, +0.0780, +0.0363],
-[ -0.3876, -0.0642, +0.0959, -0.2092, -0.0610, +0.2080, +0.1597, -0.1985, +0.0557, -0.1315, -0.1643, +0.2609, +0.4146, -0.0175, +0.2486, -0.1299, +0.0421],
-[ -0.2262, -0.1184, +0.2385, -0.7373, -0.0148, +0.1660, +0.2681, -0.0921, +0.1252, -0.2751, -0.1190, -0.8493, +0.1025, +0.1227, -0.4286, +0.5744, -0.0405],
-[ -0.0374, +0.0379, -0.4226, -0.1330, +0.1566, +0.0088, -0.0520, -0.0289, -0.1716, +0.0312, +0.0962, +0.3347, +0.5196, +0.0220, -0.1559, -0.3106, +0.2403],
-[ +0.0513, +0.4744, -0.0200, +0.2514, +0.3022, +0.0020, +0.4068, +0.9192, +0.1873, +0.0072, +0.2791, +0.2587, +0.2999, +0.2515, +0.2632, -0.2145, -0.1129],
-[ -0.0104, +0.0656, +0.3292, -0.1732, -0.1230, +0.2711, +0.1398, -0.5466, -0.4228, -0.0740, +0.2116, +0.3474, +0.0030, -0.0176, +0.3739, -0.1911, -0.0056]
+weights_final_b = np.array([
+    +0.0149, +0.1334, -0.1019, -0.0498, +0.0919, -0.2079, +0.1914, -0.0708,
+    +0.0737, -0.1228, +0.0112, -0.0441, +0.0087, -0.1149, +0.0063, +0.0170,
+    -0.0965
 ])
+# yapf: enable
 
-weights_final_b = np.array([ +0.0149, +0.1334, -0.1019, -0.0498, +0.0919, -0.2079, +0.1914, -0.0708, +0.0737, -0.1228, +0.0112, -0.0441, +0.0087, -0.1149, +0.0063, +0.0170, -0.0965])
-
-if __name__=="__main__":
-    demo_run()
+if __name__ == "__main__":
+  demo_run()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedDoublePendulumBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedDoublePendulumBulletEnv_v0_2017may.py
index 4bd7752ff..fbd35164d 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedDoublePendulumBulletEnv_v0_2017may.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedDoublePendulumBulletEnv_v0_2017may.py
@@ -2,179 +2,582 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 import numpy as np
 import pybullet_envs
 import time
 
+
 def relu(x):
-    return np.maximum(x, 0)
+  return np.maximum(x, 0)
+
 
 class SmallReactivePolicy:
-    "Simple multi-layer perceptron policy, no internal state"
-    def __init__(self, observation_space, action_space):
-        assert weights_dense1_w.shape == (observation_space.shape[0], 64.0)
-        assert weights_dense2_w.shape == (64.0, 32.0)
-        assert weights_final_w.shape  == (32.0, action_space.shape[0])
-
-    def act(self, ob):
-        x = ob
-        x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
-        x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
-        x = np.dot(x, weights_final_w) + weights_final_b
-        return x
+  "Simple multi-layer perceptron policy, no internal state"
+
+  def __init__(self, observation_space, action_space):
+    assert weights_dense1_w.shape == (observation_space.shape[0], 64.0)
+    assert weights_dense2_w.shape == (64.0, 32.0)
+    assert weights_final_w.shape == (32.0, action_space.shape[0])
+
+  def act(self, ob):
+    x = ob
+    x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
+    x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
+    x = np.dot(x, weights_final_w) + weights_final_b
+    return x
+
 
 def main():
-    env = gym.make("InvertedDoublePendulumBulletEnv-v0")
-    env.render(mode="human")
-   
-    pi = SmallReactivePolicy(env.observation_space, env.action_space)
+  env = gym.make("InvertedDoublePendulumBulletEnv-v0")
+  env.render(mode="human")
+
+  pi = SmallReactivePolicy(env.observation_space, env.action_space)
+
+  while 1:
+    frame = 0
+    score = 0
+    restart_delay = 0
+    obs = env.reset()
 
     while 1:
-        frame = 0
-        score = 0
-        restart_delay = 0
-        obs = env.reset()
-
-        while 1:
-            time.sleep(1./60.)
-            a = pi.act(obs)
-            obs, r, done, _ = env.step(a)
-            score += r
-            frame += 1
-            still_open = env.render("human")
-            if still_open==False:
-                return
-            if not done: continue
-            if restart_delay==0:
-                print("score=%0.2f in %i frames" % (score, frame))
-                restart_delay = 60*2  # 2 sec at 60 fps
-            else:
-                restart_delay -= 1
-                if restart_delay > 0: continue
-                break
-
-weights_dense1_w = np.array([
-[ -0.5857, +0.1810, +0.2839, +0.1278, -0.4302, -0.3152, +0.5916, -0.0635, +0.6259, +0.2873, -0.0572, -0.3538, -0.8121, +0.2707, +0.1656, -0.2103, -0.1614, -0.2789, -0.5856, -0.4733, +0.1838, +0.1063, +0.7629, +0.0873, +0.1480, +0.1768, +0.6522, +0.1158, -0.0816, +0.6542, -0.8870, +0.1775, +0.1532, +0.2268, -0.0313, -0.0470, +0.5328, -0.0570, +0.4820, -0.3772, -0.7581, +0.2835, -0.3566, +0.9371, -0.0441, -0.1797, -0.2859, -0.0238, +0.0261, -0.0296, -0.1406, +0.2869, +0.1279, +0.6653, +0.5643, -0.3136, +0.7751, +0.2341, +0.1903, +0.8283, -0.0697, +0.1276, -0.0250, -0.0053],
-[ +0.3741, +0.4844, -0.0638, -0.3205, +0.3137, +0.9636, +0.5329, +0.6882, +0.2983, -0.6675, -0.6372, +0.2065, -0.2645, -0.4789, +0.2326, -0.0691, -0.5905, -0.3354, +0.3428, +0.4253, +0.9111, -0.4751, -0.2124, +0.3920, +0.2897, -1.1101, +0.1894, -0.4025, -0.1125, -0.0627, +0.2347, -0.8787, +0.1019, +0.9128, +0.2544, -0.3933, +0.6485, -0.1936, -0.2402, +0.5012, -0.0918, +0.3160, -0.7860, +0.3439, -0.4268, -0.1788, -0.3930, +0.5128, +0.2338, +0.2571, +0.1343, +0.9850, -0.7074, +0.3532, +0.3048, -0.4542, +0.5539, -0.4409, -0.2003, -0.4837, -0.3554, -0.4447, -0.0817, -0.8497],
-[ +0.0825, +0.5847, +0.4837, +0.5144, +0.4770, +0.0199, +0.4275, -0.4530, +0.8499, -0.2840, +0.3817, -0.5098, -0.2155, -0.1475, +0.1145, -0.1871, -0.0526, +0.3583, -0.3537, -0.7111, -0.6116, +0.3406, -0.6360, +0.7786, +0.6628, -0.0493, +0.3505, -0.0376, -0.6556, +1.0748, -0.5329, +0.6477, -0.7117, +0.3723, +0.6006, +0.0171, +0.0012, +0.4910, -0.5651, -0.6868, +0.2403, +0.0254, -0.4416, +0.7534, -0.0138, -1.1298, +0.5447, +0.0974, +0.1988, -0.2161, -0.3126, -0.5731, -0.1278, +0.2995, -0.1200, -0.7917, +0.5326, +0.4562, -0.0144, +0.5717, -0.4065, +0.1494, +0.7100, +0.2461],
-[ -0.2861, +0.4314, -0.2982, -0.1401, -0.1033, +0.5287, -0.6620, -0.3975, +0.0038, +0.1991, -0.7079, -0.9000, +0.1659, +0.3623, -0.0752, -0.1907, -0.2335, -0.5143, +0.2324, -0.0487, +0.1583, -0.5989, +0.5957, +0.2150, -0.0335, +0.2154, +0.3279, -0.7976, +0.5320, -0.4438, +0.2170, -0.3841, -0.0039, -0.0847, -0.0028, -0.4278, -0.2393, -0.9239, +0.2880, -0.1437, -0.0941, -0.0796, -0.3906, -0.3224, +0.1038, -0.1929, -0.2713, -0.4157, -0.2178, +0.5729, -0.2065, +0.0059, +0.3879, +0.0590, +0.1759, +0.0677, -0.0170, -0.2252, +0.3301, -0.0599, +0.3791, -0.1455, +0.2200, -0.0499],
-[ -0.4403, +0.7182, +0.7941, +0.1469, +1.5777, +0.3426, +0.0923, +0.2160, +1.1492, -0.5206, -0.2659, -0.1504, +0.2739, -1.3939, +0.8992, -1.1433, -0.3828, -0.2497, -0.2172, +0.2318, -0.3605, +0.6413, -1.9095, +1.4785, -0.1274, -0.7208, -0.0802, -0.8779, -1.6260, +0.9151, +0.8289, -0.0902, -0.3551, +0.6198, +1.7488, +0.0739, -1.2022, -0.3536, -1.5187, +0.1839, +1.4258, +0.4217, +0.1503, -0.0460, +0.2327, -0.4139, -0.3668, +0.2997, +0.6856, +0.6917, -0.3856, -0.3620, +0.1578, -0.8349, -1.0796, -0.0319, -1.1966, -0.8122, +0.5053, -0.5033, -0.9207, -0.1193, -0.7625, +0.1379],
-[ -0.0321, -0.3206, -0.4516, +0.3420, +1.0964, +0.0311, +0.4654, -0.2367, +0.3347, -0.2798, -0.8169, -0.1555, +0.9397, -0.5597, +0.7113, -0.3642, -0.2840, -0.1323, -0.1000, +0.2283, +0.3612, -0.4784, +0.0504, +0.5310, -0.0887, +0.2926, +0.5069, -0.5645, -0.0976, -0.2594, +0.4425, +0.9223, -0.5637, -0.2336, -0.1316, -0.6564, -0.2780, -0.2409, -0.1637, +0.4506, +0.7018, -0.1299, +0.7172, +0.1207, +0.4375, +0.3836, +0.2781, -0.7792, -0.5317, +0.4510, +0.2423, -0.0588, -0.4254, -0.6381, -0.8205, +0.6417, +0.1904, -0.2618, +0.5900, -0.3899, -0.7851, -0.4769, -0.3688, -0.3510],
-[ -0.8366, -0.3157, -0.1130, +0.2005, +0.3713, -0.4351, -0.1278, -0.5689, +0.3229, -0.5981, -0.4917, -0.4160, -0.5504, +0.2225, -0.1581, -0.6457, +0.1001, -1.0635, +0.2368, +0.2494, -0.4054, -0.1699, -0.1316, +0.2614, +0.3016, +0.4222, -0.1548, -0.0766, -0.5226, -0.3576, -0.2433, -0.5495, +0.0056, +0.0193, +0.2353, +0.3986, +0.3580, -0.7886, +0.3928, +0.1831, +0.4319, +0.2276, -0.3062, +0.0413, -0.4288, +0.1365, +0.3176, +0.3564, +0.5668, -0.4401, -0.9576, -0.1435, +0.0304, -0.5575, +0.0412, -0.1096, +0.2207, +0.1227, -0.0051, +0.5808, -0.1331, +0.1368, +0.4170, -0.8095],
-[ -0.6368, -1.3221, -0.4492, -1.5414, +0.4004, -2.8780, -0.1748, -0.8166, +1.7066, +1.0714, -0.4755, +0.3020, +0.0422, +0.3466, +0.4472, -0.6209, -3.3768, -0.0806, +1.3624, -2.4155, +1.0886, +0.3412, +0.0891, +1.6821, -0.5361, +0.3952, +1.5120, +0.3910, +1.9500, -0.9065, -1.3452, +0.0904, -0.0389, +0.2817, -1.8375, +0.8131, -1.5287, +0.3115, +1.4069, -0.3424, +1.6101, +2.6775, +0.5516, +1.6500, -0.4138, -0.0170, +1.0008, -0.7865, +0.0551, +2.2068, -0.0108, +0.3207, -1.1884, +0.3792, -0.6435, +0.2858, -0.6881, +0.1554, -1.6926, -0.0975, -1.4120, -0.0827, -1.5186, +0.2526],
-[ -0.2900, -0.2805, +0.9182, -0.8893, +0.7345, -0.9015, -0.2696, +0.2344, +0.3889, +0.6790, +0.3657, -0.1995, -0.6738, -0.4166, +0.1690, -0.3798, -0.9872, -0.2558, -0.4205, -0.6190, -0.0092, -0.2261, -0.2738, +0.2977, -0.7348, +0.4872, +0.4776, -0.1364, +0.5836, -0.2688, -0.4261, -0.3612, -0.3533, +0.4665, +0.0155, +1.0116, -0.7139, -0.3707, -0.4429, -0.0383, +0.6716, +0.5972, +0.3506, +0.3294, -1.3734, -0.5905, -0.1168, -0.2609, +0.3436, +0.8277, +0.4965, +0.3005, -0.2929, +0.1501, -0.2655, +0.3860, -0.3946, +0.8764, +0.7927, +0.0541, -1.0912, -0.2006, -0.6928, +0.4653]
-])
+      time.sleep(1. / 60.)
+      a = pi.act(obs)
+      obs, r, done, _ = env.step(a)
+      score += r
+      frame += 1
+      still_open = env.render("human")
+      if still_open == False:
+        return
+      if not done: continue
+      if restart_delay == 0:
+        print("score=%0.2f in %i frames" % (score, frame))
+        restart_delay = 60 * 2  # 2 sec at 60 fps
+      else:
+        restart_delay -= 1
+        if restart_delay > 0: continue
+        break
+
 
-weights_dense1_b = np.array([ -0.1146, +0.2897, +0.0137, +0.0822, +0.0367, +0.0951, -0.0657, +0.0653, -0.0729, -0.0501, -0.6380, -0.4403, +0.0660, +0.0693, -0.4353, -0.2766, -0.1258, -0.6947, -0.1616, -0.0453, +0.1498, -0.2340, -0.0764, +0.2020, +0.4812, +0.0908, -0.1883, -0.0753, -0.0373, -0.4172, -0.1071, +0.0861, -0.1550, -0.0648, -0.1473, +0.1507, -0.0121, -0.5468, -0.1529, -0.3341, +0.0239, -0.0463, -0.0044, -0.0541, +0.0384, +0.3028, +0.3378, +0.0965, +0.0740, +0.1948, -0.1655, +0.1558, +0.1367, -0.1514, +0.0540, -0.0015, -0.1256, +0.3402, -0.0273, -0.2239, -0.0073, -0.6246, +0.0755, -0.2002])
+# yapf: disable
+weights_dense1_w = np.array(
+    [[
+        -0.5857, +0.1810, +0.2839, +0.1278, -0.4302, -0.3152, +0.5916, -0.0635,
+        +0.6259, +0.2873, -0.0572, -0.3538, -0.8121, +0.2707, +0.1656, -0.2103,
+        -0.1614, -0.2789, -0.5856, -0.4733, +0.1838, +0.1063, +0.7629, +0.0873,
+        +0.1480, +0.1768, +0.6522, +0.1158, -0.0816, +0.6542, -0.8870, +0.1775,
+        +0.1532, +0.2268, -0.0313, -0.0470, +0.5328, -0.0570, +0.4820, -0.3772,
+        -0.7581, +0.2835, -0.3566, +0.9371, -0.0441, -0.1797, -0.2859, -0.0238,
+        +0.0261, -0.0296, -0.1406, +0.2869, +0.1279, +0.6653, +0.5643, -0.3136,
+        +0.7751, +0.2341, +0.1903, +0.8283, -0.0697, +0.1276, -0.0250, -0.0053
+    ],
+     [
+         +0.3741, +0.4844, -0.0638, -0.3205, +0.3137, +0.9636, +0.5329,
+         +0.6882, +0.2983, -0.6675, -0.6372, +0.2065, -0.2645, -0.4789,
+         +0.2326, -0.0691, -0.5905, -0.3354, +0.3428, +0.4253, +0.9111,
+         -0.4751, -0.2124, +0.3920, +0.2897, -1.1101, +0.1894, -0.4025,
+         -0.1125, -0.0627, +0.2347, -0.8787, +0.1019, +0.9128, +0.2544,
+         -0.3933, +0.6485, -0.1936, -0.2402, +0.5012, -0.0918, +0.3160,
+         -0.7860, +0.3439, -0.4268, -0.1788, -0.3930, +0.5128, +0.2338,
+         +0.2571, +0.1343, +0.9850, -0.7074, +0.3532, +0.3048, -0.4542,
+         +0.5539, -0.4409, -0.2003, -0.4837, -0.3554, -0.4447, -0.0817, -0.8497
+     ],
+     [
+         +0.0825, +0.5847, +0.4837, +0.5144, +0.4770, +0.0199, +0.4275,
+         -0.4530, +0.8499, -0.2840, +0.3817, -0.5098, -0.2155, -0.1475,
+         +0.1145, -0.1871, -0.0526, +0.3583, -0.3537, -0.7111, -0.6116,
+         +0.3406, -0.6360, +0.7786, +0.6628, -0.0493, +0.3505, -0.0376,
+         -0.6556, +1.0748, -0.5329, +0.6477, -0.7117, +0.3723, +0.6006,
+         +0.0171, +0.0012, +0.4910, -0.5651, -0.6868, +0.2403, +0.0254,
+         -0.4416, +0.7534, -0.0138, -1.1298, +0.5447, +0.0974, +0.1988,
+         -0.2161, -0.3126, -0.5731, -0.1278, +0.2995, -0.1200, -0.7917,
+         +0.5326, +0.4562, -0.0144, +0.5717, -0.4065, +0.1494, +0.7100, +0.2461
+     ],
+     [
+         -0.2861, +0.4314, -0.2982, -0.1401, -0.1033, +0.5287, -0.6620,
+         -0.3975, +0.0038, +0.1991, -0.7079, -0.9000, +0.1659, +0.3623,
+         -0.0752, -0.1907, -0.2335, -0.5143, +0.2324, -0.0487, +0.1583,
+         -0.5989, +0.5957, +0.2150, -0.0335, +0.2154, +0.3279, -0.7976,
+         +0.5320, -0.4438, +0.2170, -0.3841, -0.0039, -0.0847, -0.0028,
+         -0.4278, -0.2393, -0.9239, +0.2880, -0.1437, -0.0941, -0.0796,
+         -0.3906, -0.3224, +0.1038, -0.1929, -0.2713, -0.4157, -0.2178,
+         +0.5729, -0.2065, +0.0059, +0.3879, +0.0590, +0.1759, +0.0677,
+         -0.0170, -0.2252, +0.3301, -0.0599, +0.3791, -0.1455, +0.2200, -0.0499
+     ],
+     [
+         -0.4403, +0.7182, +0.7941, +0.1469, +1.5777, +0.3426, +0.0923,
+         +0.2160, +1.1492, -0.5206, -0.2659, -0.1504, +0.2739, -1.3939,
+         +0.8992, -1.1433, -0.3828, -0.2497, -0.2172, +0.2318, -0.3605,
+         +0.6413, -1.9095, +1.4785, -0.1274, -0.7208, -0.0802, -0.8779,
+         -1.6260, +0.9151, +0.8289, -0.0902, -0.3551, +0.6198, +1.7488,
+         +0.0739, -1.2022, -0.3536, -1.5187, +0.1839, +1.4258, +0.4217,
+         +0.1503, -0.0460, +0.2327, -0.4139, -0.3668, +0.2997, +0.6856,
+         +0.6917, -0.3856, -0.3620, +0.1578, -0.8349, -1.0796, -0.0319,
+         -1.1966, -0.8122, +0.5053, -0.5033, -0.9207, -0.1193, -0.7625, +0.1379
+     ],
+     [
+         -0.0321, -0.3206, -0.4516, +0.3420, +1.0964, +0.0311, +0.4654,
+         -0.2367, +0.3347, -0.2798, -0.8169, -0.1555, +0.9397, -0.5597,
+         +0.7113, -0.3642, -0.2840, -0.1323, -0.1000, +0.2283, +0.3612,
+         -0.4784, +0.0504, +0.5310, -0.0887, +0.2926, +0.5069, -0.5645,
+         -0.0976, -0.2594, +0.4425, +0.9223, -0.5637, -0.2336, -0.1316,
+         -0.6564, -0.2780, -0.2409, -0.1637, +0.4506, +0.7018, -0.1299,
+         +0.7172, +0.1207, +0.4375, +0.3836, +0.2781, -0.7792, -0.5317,
+         +0.4510, +0.2423, -0.0588, -0.4254, -0.6381, -0.8205, +0.6417,
+         +0.1904, -0.2618, +0.5900, -0.3899, -0.7851, -0.4769, -0.3688, -0.3510
+     ],
+     [
+         -0.8366, -0.3157, -0.1130, +0.2005, +0.3713, -0.4351, -0.1278,
+         -0.5689, +0.3229, -0.5981, -0.4917, -0.4160, -0.5504, +0.2225,
+         -0.1581, -0.6457, +0.1001, -1.0635, +0.2368, +0.2494, -0.4054,
+         -0.1699, -0.1316, +0.2614, +0.3016, +0.4222, -0.1548, -0.0766,
+         -0.5226, -0.3576, -0.2433, -0.5495, +0.0056, +0.0193, +0.2353,
+         +0.3986, +0.3580, -0.7886, +0.3928, +0.1831, +0.4319, +0.2276,
+         -0.3062, +0.0413, -0.4288, +0.1365, +0.3176, +0.3564, +0.5668,
+         -0.4401, -0.9576, -0.1435, +0.0304, -0.5575, +0.0412, -0.1096,
+         +0.2207, +0.1227, -0.0051, +0.5808, -0.1331, +0.1368, +0.4170, -0.8095
+     ],
+     [
+         -0.6368, -1.3221, -0.4492, -1.5414, +0.4004, -2.8780, -0.1748,
+         -0.8166, +1.7066, +1.0714, -0.4755, +0.3020, +0.0422, +0.3466,
+         +0.4472, -0.6209, -3.3768, -0.0806, +1.3624, -2.4155, +1.0886,
+         +0.3412, +0.0891, +1.6821, -0.5361, +0.3952, +1.5120, +0.3910,
+         +1.9500, -0.9065, -1.3452, +0.0904, -0.0389, +0.2817, -1.8375,
+         +0.8131, -1.5287, +0.3115, +1.4069, -0.3424, +1.6101, +2.6775,
+         +0.5516, +1.6500, -0.4138, -0.0170, +1.0008, -0.7865, +0.0551,
+         +2.2068, -0.0108, +0.3207, -1.1884, +0.3792, -0.6435, +0.2858,
+         -0.6881, +0.1554, -1.6926, -0.0975, -1.4120, -0.0827, -1.5186, +0.2526
+     ],
+     [
+         -0.2900, -0.2805, +0.9182, -0.8893, +0.7345, -0.9015, -0.2696,
+         +0.2344, +0.3889, +0.6790, +0.3657, -0.1995, -0.6738, -0.4166,
+         +0.1690, -0.3798, -0.9872, -0.2558, -0.4205, -0.6190, -0.0092,
+         -0.2261, -0.2738, +0.2977, -0.7348, +0.4872, +0.4776, -0.1364,
+         +0.5836, -0.2688, -0.4261, -0.3612, -0.3533, +0.4665, +0.0155,
+         +1.0116, -0.7139, -0.3707, -0.4429, -0.0383, +0.6716, +0.5972,
+         +0.3506, +0.3294, -1.3734, -0.5905, -0.1168, -0.2609, +0.3436,
+         +0.8277, +0.4965, +0.3005, -0.2929, +0.1501, -0.2655, +0.3860,
+         -0.3946, +0.8764, +0.7927, +0.0541, -1.0912, -0.2006, -0.6928, +0.4653
+     ]])
+
+weights_dense1_b = np.array([
+    -0.1146, +0.2897, +0.0137, +0.0822, +0.0367, +0.0951, -0.0657, +0.0653,
+    -0.0729, -0.0501, -0.6380, -0.4403, +0.0660, +0.0693, -0.4353, -0.2766,
+    -0.1258, -0.6947, -0.1616, -0.0453, +0.1498, -0.2340, -0.0764, +0.2020,
+    +0.4812, +0.0908, -0.1883, -0.0753, -0.0373, -0.4172, -0.1071, +0.0861,
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+])
 
 weights_dense2_w = np.array([
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+        +0.0657, +0.1230, -0.2558, +0.3103, -0.0795, -0.1243, +0.1956, +0.0262,
+        -0.2626, -0.0554, +0.3760, +0.3076, -0.4633, +0.0790, +0.2363, -0.3311,
+        +0.1235, -0.1727, -0.2468, +0.0188, -0.1121, -0.2807, -0.5865, -0.4197,
+        +0.1949, -0.4970, -1.0413, -0.1698, +0.1798, +0.2004, -0.0514, +0.0254
+    ],
+    [
+        -0.1566, -1.1156, +0.4431, -0.1503, -0.5682, +0.1822, -0.1201, +0.5151,
+        -0.1386, -0.1764, +0.2063, -0.8582, +0.3750, -0.1405, +0.0852, +0.2641,
+        -0.1951, -0.0575, -0.4181, +0.2273, +0.1332, -0.2797, +0.5406, -0.0869,
+        +0.2453, +0.0648, +0.2252, -0.0628, -0.6882, -0.0514, -0.4663, -0.0954
+    ],
+    [
+        -0.4780, +0.5844, +0.1782, -0.0831, +0.1547, -0.0595, -0.5646, -0.0488,
+        -0.1774, -0.0098, +0.1833, +0.3520, -0.3359, -0.1492, +0.1139, -0.1223,
+        -0.5312, -0.5361, +0.1689, -0.2020, +0.1069, +0.2327, +0.2887, +0.0526,
+        -0.5916, -0.2435, -0.2342, +0.3422, +0.4399, -1.1880, +0.1293, -0.1021
+    ],
+    [
+        -1.2784, -1.8266, +0.0630, -0.3332, -0.5833, -0.3733, +0.3265, +0.1977,
+        +0.0716, -0.2575, +0.0403, -0.1961, +0.1541, -0.2311, -0.1734, -0.1785,
+        +0.0168, +0.1134, +0.0407, -0.1661, +0.5985, -1.9681, +0.1342, +0.3432,
+        +0.3934, +0.0663, +0.3141, -2.0177, -1.7071, +0.2942, -1.0684, -0.0737
+    ],
+    [
+        +0.1763, +0.2191, +0.2609, +0.0723, +0.1038, -0.2516, -0.9086, +0.1536,
+        +0.0153, +0.1061, +0.1675, +0.3839, -0.5326, +0.2007, -0.4943, -0.1048,
+        +0.1614, -0.4703, +0.3453, -0.7441, -0.6187, +0.4247, +0.1721, -0.1776,
+        -0.0919, -0.8387, +0.0798, -0.0598, +0.2711, -0.0508, +0.1761, +0.0029
+    ],
+    [
+        -0.2003, +0.2194, -0.6280, +0.1593, +0.1648, -0.1007, +0.3162, -0.3881,
+        -0.1584, -0.0148, +0.7057, +0.0085, +0.3488, +0.0977, +0.4018, -0.8195,
+        -0.1944, +0.4359, -0.6605, -0.1929, +0.2237, +0.1087, -0.4213, -0.7149,
+        +0.3972, -0.1313, -0.2815, -0.7234, -0.0561, -0.5364, +0.0178, +0.0349
+    ],
+    [
+        +0.0567, +0.1687, +0.0007, +0.2939, -1.3854, +0.0168, +0.1909, +0.4919,
+        -0.4547, +0.0562, -0.1188, +0.1653, -0.0265, -0.0541, -0.1117, -0.3240,
+        +0.2545, +0.6516, +0.0124, -0.1258, -0.0656, -0.3524, +0.0174, +0.3926,
+        +0.1125, +0.2834, -0.1961, -0.3603, +0.1783, -0.0224, -0.6900, -0.1688
+    ],
+    [
+        +0.0672, +0.6339, -0.3839, +0.0077, +0.8224, -0.3197, -0.0589, -0.1318,
+        +0.0222, -0.1530, +0.1237, +0.4014, -0.1952, -0.1130, +0.4214, -0.2741,
+        +0.2291, +0.0757, +0.0563, -0.0967, +0.4210, +0.5133, +0.0412, -0.9212,
+        +0.1377, -0.4068, -0.3652, +0.4283, +0.6182, -0.6187, +0.1997, +0.1240
+    ],
+    [
+        -0.0067, +0.3307, -0.7751, -0.2084, +0.4740, -0.0264, -0.0768, -0.9519,
+        -0.0632, -0.0753, +0.3293, +0.5260, -0.6023, +0.0060, +0.2799, -0.2904,
+        -0.8262, -0.6644, -0.3900, -0.1461, +0.4965, +0.3996, -0.7569, +0.0612,
+        +0.5168, -0.5160, -0.4875, +0.3759, +0.0295, +0.1027, +0.6096, -0.0115
+    ],
+    [
+        -0.0110, +0.4652, -0.1486, -0.6029, +0.2581, -0.3184, -0.3759, +0.3213,
+        -0.2748, -0.0630, +0.0953, +0.2101, -1.2738, -0.1353, +0.2710, -0.2276,
+        +0.2586, -0.2347, -0.3320, +0.0487, -0.2318, -0.1002, +0.1236, +0.2660,
+        -0.1172, +0.1437, -0.0850, +0.1659, -0.2152, -0.0764, +0.2838, -0.1325
+    ],
+    [
+        +0.0152, -0.0906, -0.1897, -0.3521, -0.1836, -0.1694, -0.4150, -0.1695,
+        +0.0509, -0.0716, +0.3118, +0.2422, -0.5058, -0.0637, -0.1038, -0.2828,
+        -0.0528, -0.2051, +0.2062, -0.2105, -0.7317, +0.1881, -0.2992, -0.0883,
+        +0.0115, -1.5295, -0.1671, +0.0411, +0.0648, -0.0119, -0.2941, +0.0273
+    ],
+    [
+        +0.5028, +0.1780, -0.4643, -0.0373, +0.3067, -0.1974, +0.2643, -0.2365,
+        -0.2083, +0.0472, +0.4830, +0.0630, +0.2155, -0.0916, +0.6290, -0.4427,
+        -0.6266, +0.3576, -0.3541, -0.2034, +0.3733, +0.8247, -0.5837, -0.4372,
+        +0.2696, -0.4042, -0.3436, +0.0355, -0.2288, -0.6382, +0.7358, -0.1229
+    ]
 ])
 
-weights_dense2_b = np.array([ -0.0730, +0.0456, +0.0877, -0.2607, +0.0029, -0.2705, -0.1420, +0.2403, -0.2135, -0.0646, +0.1378, +0.1105, -0.4639, -0.0583, -0.0872, -0.1473, +0.1460, -0.0234, +0.0740, -0.0745, -0.1283, +0.0316, +0.0361, -0.0726, -0.0304, +0.0417, -0.0313, +0.0935, +0.0815, +0.0814, +0.0818, -0.1111])
-
-weights_final_w = np.array([
-[ +1.0397],
-[ +0.7049],
-[ -0.2128],
-[ +0.2172],
-[ +0.3027],
-[ -0.1991],
-[ +0.3398],
-[ -0.5932],
-[ -0.1439],
-[ -0.0236],
-[ +0.5679],
-[ +0.8571],
-[ +0.1934],
-[ -0.1652],
-[ +0.6933],
-[ -0.5510],
-[ -1.0587],
-[ +0.6996],
-[ -0.5009],
-[ -0.4000],
-[ -0.6958],
-[ +0.7716],
-[ -0.5342],
-[ -0.5095],
-[ +0.3040],
-[ -1.1986],
-[ -0.4900],
-[ +0.7726],
-[ +0.5871],
-[ -0.2533],
-[ +0.2633],
-[ -0.0004]
+weights_dense2_b = np.array([
+    -0.0730, +0.0456, +0.0877, -0.2607, +0.0029, -0.2705, -0.1420, +0.2403,
+    -0.2135, -0.0646, +0.1378, +0.1105, -0.4639, -0.0583, -0.0872, -0.1473,
+    +0.1460, -0.0234, +0.0740, -0.0745, -0.1283, +0.0316, +0.0361, -0.0726,
+    -0.0304, +0.0417, -0.0313, +0.0935, +0.0815, +0.0814, +0.0818, -0.1111
 ])
 
-weights_final_b = np.array([ +0.0190])
+weights_final_w = np.array([[+1.0397], [+0.7049], [-0.2128], [+0.2172],
+                            [+0.3027], [-0.1991], [+0.3398], [-0.5932],
+                            [-0.1439], [-0.0236], [+0.5679], [+0.8571],
+                            [+0.1934], [-0.1652], [+0.6933], [-0.5510],
+                            [-1.0587], [+0.6996], [-0.5009], [-0.4000],
+                            [-0.6958], [+0.7716], [-0.5342], [-0.5095],
+                            [+0.3040], [-1.1986], [-0.4900], [+0.7726],
+                            [+0.5871], [-0.2533], [+0.2633], [-0.0004]])
+
+weights_final_b = np.array([+0.0190])
+# yapf: enable
 
-if __name__=="__main__":
-    main()
+if __name__ == "__main__":
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumBulletEnv_v0_2017may.py
index 9235d167d..e4e84dd93 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumBulletEnv_v0_2017may.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumBulletEnv_v0_2017may.py
@@ -2,174 +2,537 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 import numpy as np
 import pybullet_envs
 import time
 
+
 def relu(x):
-    return np.maximum(x, 0)
+  return np.maximum(x, 0)
+
 
 class SmallReactivePolicy:
-    "Simple multi-layer perceptron policy, no internal state"
-    def __init__(self, observation_space, action_space):
-        assert weights_dense1_w.shape == (observation_space.shape[0], 64)
-        assert weights_dense2_w.shape == (64, 32)
-        assert weights_final_w.shape  == (32, action_space.shape[0])
-
-    def act(self, ob):
-        x = ob
-        x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
-        x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
-        x = np.dot(x, weights_final_w) + weights_final_b
-        return x
+  "Simple multi-layer perceptron policy, no internal state"
+
+  def __init__(self, observation_space, action_space):
+    assert weights_dense1_w.shape == (observation_space.shape[0], 64)
+    assert weights_dense2_w.shape == (64, 32)
+    assert weights_final_w.shape == (32, action_space.shape[0])
+
+  def act(self, ob):
+    x = ob
+    x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
+    x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
+    x = np.dot(x, weights_final_w) + weights_final_b
+    return x
+
 
 def main():
-    print("create env")
-    env = gym.make("InvertedPendulumBulletEnv-v0")
-    env.render(mode="human")
-    pi = SmallReactivePolicy(env.observation_space, env.action_space)
+  print("create env")
+  env = gym.make("InvertedPendulumBulletEnv-v0")
+  env.render(mode="human")
+  pi = SmallReactivePolicy(env.observation_space, env.action_space)
 
+  while 1:
+    frame = 0
+    score = 0
+    restart_delay = 0
+    obs = env.reset()
+    print("frame")
     while 1:
-        frame = 0
-        score = 0
-        restart_delay = 0
-        obs = env.reset()
-        print("frame")
-        while 1:
-            time.sleep(1./60.)
-            a = pi.act(obs)
-            obs, r, done, _ = env.step(a)
-            score += r
-            frame += 1
-            still_open = env.render("human")
-            if still_open==False:
-                return
-            if not done: continue
-            if restart_delay==0:
-                print("score=%0.2f in %i frames" % (score, frame))
-                restart_delay = 60*2  # 2 sec at 60 fps
-            else:
-                restart_delay -= 1
-                if restart_delay==0: break
-
-weights_dense1_w = np.array([
-[ +0.8621, +0.3260, +0.0986, -0.1225, +0.2038, -0.8051, -0.7498, +0.1905, -0.3418, +0.5002, -0.1093, +0.0285, +0.3480, -0.1596, -0.1781, +0.3643, -0.4283, -0.3715, -0.1571, +0.3531, +0.0934, -0.2215, -0.3085, +0.9581, +0.2485, -0.6232, -0.3175, +0.9771, +0.3651, -0.8850, -0.4212, -0.0301, +0.0432, +0.3390, +0.7537, +0.1649, -0.0128, -0.1374, +0.3793, +1.0430, +0.8043, -0.9001, +0.4334, -0.1243, +1.2373, +0.1890, +0.3333, -0.0520, +0.1654, +0.2521, -0.0168, +0.8439, -0.6960, +0.1884, +0.0991, +0.5242, -0.6837, +0.6844, -0.2593, -0.3298, +0.2212, +0.0281, +0.2608, +0.6527],
-[ -0.9350, -0.2122, +0.0162, +0.5306, -0.2914, -0.8573, +0.2552, +0.7069, +0.7862, -0.0315, -1.0844, +0.2707, +0.5102, -1.1359, +0.3066, +0.0357, +0.1833, -0.1946, +0.0948, +0.6685, -0.6101, +0.4774, -0.3017, +0.3823, -0.2835, -0.6760, +1.2963, +0.4466, -0.7132, -0.9109, -0.0589, -0.8726, +0.6972, -0.2256, -0.0286, +0.4646, -0.5113, -0.1692, +0.7638, +0.2274, -0.5734, +0.7430, +0.9680, +0.7809, -0.2457, -0.4952, +0.0197, -0.6428, +0.2367, -0.5887, -0.5167, +0.2299, -0.5853, -0.4101, +0.9042, +0.0913, +0.5774, +0.2756, +0.2436, -0.6068, -0.2232, -0.1415, -0.5094, -0.1012],
-[ +0.0983, -0.3266, +0.2611, +0.0664, +0.6222, +0.0773, -0.2516, -0.4416, -0.3770, +0.0535, +0.3391, -0.7475, +0.5874, -0.0405, -0.2058, -0.5957, +0.2659, -0.8477, -0.5814, -0.0494, -0.1678, +0.2650, -0.4039, +0.1414, -0.6635, +0.0447, +0.2932, +0.1167, +0.1195, +0.0669, -0.4223, +0.1196, +0.0553, -0.7123, -0.4011, +0.3557, -0.4503, +0.7047, -0.4471, +0.0807, +0.3926, -0.1427, +0.4355, -0.3678, +0.3453, +0.1597, -0.3076, +0.4689, +0.3128, -0.7050, +0.6505, +0.3427, +0.1981, +0.1190, +0.2554, +0.8283, +0.1647, -0.4257, +0.1481, +0.4361, -0.5497, -0.6114, -0.0138, +0.0932],
-[ +0.1866, +0.6408, -1.8201, +1.0946, +0.7742, -0.7651, +0.1082, +0.6842, +0.3794, +0.3547, -0.8172, -0.0921, -0.6736, +1.0251, -0.9618, -0.6869, +1.8465, +0.2425, +0.7910, +1.0009, -0.8031, +1.6697, -0.8962, +0.1873, +0.4960, -0.6812, +0.6860, +1.1932, -0.7019, +0.4028, +0.4841, +0.6497, +1.6490, -0.5464, +0.7060, +1.8087, -0.6118, -0.7955, -0.3797, -1.2048, +1.2356, -0.6141, +1.2502, +0.5641, -0.1019, -1.7516, -0.1134, -0.6719, +1.5014, -0.2718, -0.5933, +0.1714, -1.3590, -0.3656, +1.0083, -0.8511, -0.5597, -0.4446, -1.7158, -0.0851, +0.3089, +0.0967, -1.0121, +0.3048],
-[ +0.3329, +0.5382, +0.1585, +0.8205, +0.5510, +0.2796, -0.7120, +0.3434, +0.2931, +1.2275, +0.4191, -0.6828, -0.5091, +0.8408, -0.3101, -0.5183, +0.2651, +0.2073, -0.1383, +0.6539, -0.2167, +0.7798, -0.5690, +0.3750, +0.4358, +0.6537, -0.2202, -0.0563, +0.6605, +0.4599, -0.5327, +0.6610, +0.8387, +0.1887, -0.2593, +0.7904, -0.3567, +0.4121, +0.4378, -0.2935, +0.1291, +0.0021, -0.3416, -0.5920, -0.2895, -0.4610, +0.7380, -0.6322, +0.6738, -0.1378, -0.3304, -0.2894, -0.3582, -0.8311, +0.2660, -0.2079, -0.1765, -0.6825, -0.1754, -0.4455, +0.7202, -0.8177, -0.9900, -0.7425]
-])
+      time.sleep(1. / 60.)
+      a = pi.act(obs)
+      obs, r, done, _ = env.step(a)
+      score += r
+      frame += 1
+      still_open = env.render("human")
+      if still_open == False:
+        return
+      if not done: continue
+      if restart_delay == 0:
+        print("score=%0.2f in %i frames" % (score, frame))
+        restart_delay = 60 * 2  # 2 sec at 60 fps
+      else:
+        restart_delay -= 1
+        if restart_delay == 0: break
+
 
-weights_dense1_b = np.array([ +0.0009, -0.2179, -0.0299, +0.2069, -0.0099, +0.0907, +0.0271, +0.1957, +0.0185, +0.1671, -0.0699, -0.0332, -0.0244, +0.0022, +0.1877, -0.0801, +0.0235, -0.0097, -0.0088, +0.1961, -0.1055, +0.0605, -0.0913, +0.0884, -0.0638, +0.0229, -0.1101, +0.1966, -0.0042, +0.0221, -0.0966, +0.1554, +0.1623, -0.0454, +0.1068, +0.0114, +0.0544, +0.0201, +0.0257, +0.0637, +0.0761, +0.2120, +0.0225, +0.2023, -0.0931, +0.0585, -0.2253, -0.0302, +0.0682, +0.0000, -0.1215, -0.1105, +0.1376, +0.0583, -0.1706, -0.0262, -0.0897, +0.0728, +0.0787, +0.0912, -0.0964, -0.0959, -0.0195, +0.0232])
+# yapf: disable
+weights_dense1_w = np.array(
+    [[
+        +0.8621, +0.3260, +0.0986, -0.1225, +0.2038, -0.8051, -0.7498, +0.1905,
+        -0.3418, +0.5002, -0.1093, +0.0285, +0.3480, -0.1596, -0.1781, +0.3643,
+        -0.4283, -0.3715, -0.1571, +0.3531, +0.0934, -0.2215, -0.3085, +0.9581,
+        +0.2485, -0.6232, -0.3175, +0.9771, +0.3651, -0.8850, -0.4212, -0.0301,
+        +0.0432, +0.3390, +0.7537, +0.1649, -0.0128, -0.1374, +0.3793, +1.0430,
+        +0.8043, -0.9001, +0.4334, -0.1243, +1.2373, +0.1890, +0.3333, -0.0520,
+        +0.1654, +0.2521, -0.0168, +0.8439, -0.6960, +0.1884, +0.0991, +0.5242,
+        -0.6837, +0.6844, -0.2593, -0.3298, +0.2212, +0.0281, +0.2608, +0.6527
+    ],
+     [
+         -0.9350, -0.2122, +0.0162, +0.5306, -0.2914, -0.8573, +0.2552,
+         +0.7069, +0.7862, -0.0315, -1.0844, +0.2707, +0.5102, -1.1359,
+         +0.3066, +0.0357, +0.1833, -0.1946, +0.0948, +0.6685, -0.6101,
+         +0.4774, -0.3017, +0.3823, -0.2835, -0.6760, +1.2963, +0.4466,
+         -0.7132, -0.9109, -0.0589, -0.8726, +0.6972, -0.2256, -0.0286,
+         +0.4646, -0.5113, -0.1692, +0.7638, +0.2274, -0.5734, +0.7430,
+         +0.9680, +0.7809, -0.2457, -0.4952, +0.0197, -0.6428, +0.2367,
+         -0.5887, -0.5167, +0.2299, -0.5853, -0.4101, +0.9042, +0.0913,
+         +0.5774, +0.2756, +0.2436, -0.6068, -0.2232, -0.1415, -0.5094, -0.1012
+     ],
+     [
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+         -0.4416, -0.3770, +0.0535, +0.3391, -0.7475, +0.5874, -0.0405,
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+         +0.1647, -0.4257, +0.1481, +0.4361, -0.5497, -0.6114, -0.0138, +0.0932
+     ],
+     [
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+         -0.5597, -0.4446, -1.7158, -0.0851, +0.3089, +0.0967, -1.0121, +0.3048
+     ],
+     [
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+         +0.3434, +0.2931, +1.2275, +0.4191, -0.6828, -0.5091, +0.8408,
+         -0.3101, -0.5183, +0.2651, +0.2073, -0.1383, +0.6539, -0.2167,
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+         +0.6605, +0.4599, -0.5327, +0.6610, +0.8387, +0.1887, -0.2593,
+         +0.7904, -0.3567, +0.4121, +0.4378, -0.2935, +0.1291, +0.0021,
+         -0.3416, -0.5920, -0.2895, -0.4610, +0.7380, -0.6322, +0.6738,
+         -0.1378, -0.3304, -0.2894, -0.3582, -0.8311, +0.2660, -0.2079,
+         -0.1765, -0.6825, -0.1754, -0.4455, +0.7202, -0.8177, -0.9900, -0.7425
+     ]])
+
+weights_dense1_b = np.array([
+    +0.0009, -0.2179, -0.0299, +0.2069, -0.0099, +0.0907, +0.0271, +0.1957,
+    +0.0185, +0.1671, -0.0699, -0.0332, -0.0244, +0.0022, +0.1877, -0.0801,
+    +0.0235, -0.0097, -0.0088, +0.1961, -0.1055, +0.0605, -0.0913, +0.0884,
+    -0.0638, +0.0229, -0.1101, +0.1966, -0.0042, +0.0221, -0.0966, +0.1554,
+    +0.1623, -0.0454, +0.1068, +0.0114, +0.0544, +0.0201, +0.0257, +0.0637,
+    +0.0761, +0.2120, +0.0225, +0.2023, -0.0931, +0.0585, -0.2253, -0.0302,
+    +0.0682, +0.0000, -0.1215, -0.1105, +0.1376, +0.0583, -0.1706, -0.0262,
+    -0.0897, +0.0728, +0.0787, +0.0912, -0.0964, -0.0959, -0.0195, +0.0232
+])
 
 weights_dense2_w = np.array([
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+        -0.0503, -0.3082, +0.1056, -0.1658, +0.3225, +0.0727, -0.4463, -0.0153,
+        +0.0195, +0.0962, -0.0483, -0.0484, +0.2464, -0.5537, +0.1422, +0.1233,
+        -0.1036, +0.0864, -0.2107, +0.1319, +0.2002, +0.3051, -0.2054, +0.3069,
+        +0.2754, +0.1618, -0.0593, -0.0373, +0.2155, -0.1157, -0.1199, +0.2342
+    ],
+    [
+        -0.1789, -0.1216, +0.0442, -0.1111, +0.1411, -0.0572, -0.4238, +0.0134,
+        -0.1511, +0.0625, -0.0139, -0.2257, -0.1143, -0.2315, +0.3597, -0.1227,
+        +0.0240, +0.2061, -0.0474, +0.0561, -0.2806, -0.0939, -0.0608, +0.1852,
+        -0.0210, -0.3526, +0.0992, -0.3513, -0.0787, +0.1074, -0.0475, -0.1759
+    ],
+    [
+        -0.0510, +0.0215, +0.1585, -0.0757, +0.0357, -0.0553, -0.1151, -0.1353,
+        +0.1000, -0.2570, -0.0664, -0.1762, +0.0430, -0.0365, +0.0198, +0.1154,
+        -0.5763, +0.0393, -0.0443, +0.0504, +0.0482, +0.1528, +0.1955, -0.0493,
+        +0.2712, -0.0688, -0.1406, +0.1479, +0.0204, +0.0838, -0.2282, +0.2307
+    ],
+    [
+        -0.1682, -0.0467, -0.0758, +0.3832, -0.1471, +0.0612, +0.3901, +0.1065,
+        +0.2009, -0.3104, -0.2998, -0.3175, -0.0722, +0.1549, -0.2472, -0.1729,
+        +0.0841, -0.1691, +0.1407, -0.1969, -0.0491, +0.0103, +0.1179, -0.1327,
+        -0.1275, +0.0368, +0.0953, -0.1660, -0.0245, -0.3851, +0.1340, -0.1417
+    ],
+    [
+        +0.0114, -0.0822, -0.2575, -0.0169, +0.1292, +0.0791, -0.0803, +0.0061,
+        -0.0445, -0.2228, +0.0215, +0.1863, +0.2645, -0.0295, +0.0756, -0.2138,
+        -0.1607, +0.0527, +0.0592, -0.1770, -0.0982, -0.1096, +0.0925, -0.0325,
+        +0.0047, +0.1512, +0.0663, -0.1348, +0.0084, -0.1352, +0.0189, +0.1428
+    ],
+    [
+        +0.0052, +0.1124, +0.1083, +0.1163, +0.0787, +0.0839, +0.0839, +0.0506,
+        +0.0537, +0.1066, +0.1034, -0.1299, -0.1434, +0.0188, +0.1823, +0.1403,
+        -0.4525, +0.0949, -0.0981, +0.0722, -0.1085, -0.2382, +0.1028, +0.0664,
+        +0.1976, +0.1073, -0.2736, +0.2433, -0.3520, -0.0386, -0.2319, -0.0724
+    ],
+    [
+        -0.3279, -0.1491, -0.1409, +0.2056, -0.1464, +0.0543, +0.1842, +0.1104,
+        -0.2819, +0.0769, -0.1159, +0.0228, -0.0988, +0.0026, -0.1204, -0.0780,
+        -0.2018, +0.1755, +0.1574, +0.0222, +0.1662, -0.2193, -0.0718, +0.0010,
+        -0.0123, -0.0120, +0.2587, +0.0358, -0.1435, +0.0017, -0.2620, +0.0965
+    ],
+    [
+        -0.1144, -0.1048, +0.2211, -0.0726, -0.1721, -0.2475, -0.3226, +0.0120,
+        +0.0908, +0.0375, -0.0974, +0.0490, -0.1180, -0.3155, -0.2565, -0.0092,
+        -0.4400, +0.2027, -0.1459, +0.1043, +0.0771, +0.0825, -0.1541, -0.0713,
+        -0.0437, -0.0249, -0.1757, -0.1115, +0.0457, +0.1141, -0.2567, +0.0405
+    ],
+    [
+        +0.0587, +0.1083, +0.0729, +0.2131, -0.1586, +0.2208, -0.1576, -0.0811,
+        -0.0467, +0.2201, -0.1082, -0.2077, +0.0030, -0.1222, +0.2023, +0.1155,
+        -0.1616, +0.0105, +0.1167, -0.1257, +0.4859, +0.1337, -0.0169, -0.0163,
+        +0.2076, +0.0367, -0.0050, -0.2590, -0.0800, -0.2192, +0.0938, +0.1126
+    ],
+    [
+        -0.3834, -0.0180, -0.2714, +0.0303, +0.0784, -0.1242, +0.1105, +0.0237,
+        -0.0085, +0.2615, +0.0189, -0.3734, +0.0088, +0.1211, -0.0838, +0.0067,
+        +0.1956, +0.1577, +0.2132, -0.0044, -0.2748, +0.1417, +0.0201, +0.1002,
+        +0.0311, -0.0052, -0.1695, -0.0750, +0.2200, -0.2848, +0.0438, -0.0442
+    ],
+    [
+        -0.1496, +0.1258, +0.1903, -0.0337, -0.1470, -0.0530, +0.0519, -0.0037,
+        +0.0342, +0.0404, -0.0950, -0.0840, +0.1083, -0.0488, +0.0427, +0.1454,
+        +0.0851, -0.0203, -0.2354, +0.1562, +0.1899, +0.3145, +0.0013, +0.1608,
+        +0.0126, +0.2080, -0.1409, -0.0746, +0.0580, -0.1045, -0.1753, +0.1225
+    ],
+    [
+        -0.0349, +0.1354, -0.1052, -0.1189, +0.0288, -0.0257, +0.0813, -0.1559,
+        +0.1267, +0.0664, +0.2004, +0.1232, +0.2557, -0.1729, -0.0666, +0.1644,
+        +0.1043, -0.2672, +0.0537, +0.0566, -0.1738, +0.0036, +0.1406, -0.0574,
+        -0.0556, +0.3336, -0.0328, -0.1624, +0.0132, -0.0627, -0.1523, +0.0552
+    ],
+    [
+        -0.3105, +0.2681, -0.5462, +0.2785, -0.2453, -0.2965, +0.1436, +0.0786,
+        -0.3242, -0.3518, +0.1025, +0.2219, -0.1324, +0.1681, +0.0701, -0.0938,
+        +0.1574, -0.5157, +0.3574, -0.1100, +0.2647, -0.1698, +0.2684, -0.3876,
+        -0.6240, -0.1013, +0.2920, -0.3569, -0.0008, +0.0974, +0.1444, -0.3349
+    ],
+    [
+        +0.0848, -0.1191, +0.2283, +0.0922, +0.2880, -0.1747, -0.4457, +0.1013,
+        +0.2494, +0.1487, +0.1013, -0.0403, -0.0236, -0.1965, -0.0655, +0.0818,
+        +0.0493, -0.0605, -0.1889, +0.1772, -0.2826, +0.2783, -0.1653, +0.3505,
+        +0.4192, -0.1048, -0.1459, +0.0779, -0.0154, -0.1573, -0.1254, -0.1118
+    ],
+    [
+        -0.1817, +0.0719, +0.1352, +0.3208, +0.2142, -0.1149, +0.0020, +0.1617,
+        +0.1055, +0.0395, -0.1802, -0.0631, -0.3172, +0.1971, +0.0197, +0.1271,
+        -0.2375, -0.1849, -0.0134, +0.1223, +0.2566, +0.0311, -0.2746, +0.0278,
+        +0.1233, +0.0167, -0.0363, +0.2146, -0.0466, +0.0732, -0.1490, +0.1040
+    ],
+    [
+        +0.1008, -0.1501, +0.0264, -0.4661, -0.0553, +0.0431, -0.3076, -0.0461,
+        +0.1393, -0.1225, +0.2811, -0.0363, -0.0403, -0.3370, -0.0865, -0.1179,
+        +0.1106, +0.2035, -0.2432, -0.0859, +0.0600, -0.0890, -0.0749, +0.0483,
+        +0.0615, -0.0239, -0.4674, +0.0199, +0.0669, +0.1410, +0.1846, +0.2626
+    ],
+    [
+        +0.0663, +0.1486, -0.3928, +0.3257, -0.0316, +0.1377, +0.0418, +0.1921,
+        -0.1616, -0.2265, -0.0917, +0.1582, -0.0537, +0.0295, -0.2264, -0.1921,
+        -0.0225, +0.0928, +0.0747, -0.5268, -0.0068, -0.3328, +0.0437, -0.2361,
+        -0.1408, -0.1234, +0.2216, -0.1372, -0.0499, +0.1940, +0.0098, -0.2953
+    ],
+    [
+        +0.0290, -0.1583, -0.0172, -0.1748, -0.0042, -0.0725, -0.2227, -0.1366,
+        -0.1771, +0.1987, +0.3142, +0.1889, +0.0195, -0.5461, +0.0921, +0.1407,
+        -0.1656, +0.1985, +0.0113, +0.2613, +0.2925, +0.1166, -0.1286, +0.1031,
+        -0.2228, -0.0605, -0.2151, +0.2477, +0.1602, -0.0109, +0.0207, +0.1257
+    ],
+    [
+        +0.0630, -0.1688, +0.1662, -0.2327, +0.2832, +0.1350, -0.1658, +0.0504,
+        -0.0502, +0.1736, +0.1002, -0.0051, -0.0311, -0.0628, +0.0039, +0.5085,
+        -0.2191, +0.5105, -0.0927, +0.2833, -0.2828, +0.1078, +0.0406, -0.0392,
+        -0.2372, +0.1508, +0.0556, +0.0313, +0.1296, +0.1315, -0.1143, +0.1632
+    ]
 ])
 
-weights_dense2_b = np.array([ -0.0655, +0.0020, +0.0358, -0.0192, +0.0570, +0.0000, +0.0711, -0.0145, +0.0294, +0.0139, -0.0215, -0.0952, +0.0000, +0.0526, -0.0585, +0.0633, -0.0332, +0.0030, +0.0107, +0.0830, +0.0140, +0.0888, -0.1115, -0.0722, +0.0240, +0.0476, -0.0807, -0.0421, +0.0000, -0.0557, -0.0403, +0.0034])
-
-weights_final_w = np.array([
-[ -0.0230],
-[ +0.0730],
-[ -0.2093],
-[ +0.0463],
-[ -0.1983],
-[ -0.0031],
-[ +0.2101],
-[ -0.0066],
-[ -0.1481],
-[ -0.1615],
-[ -0.1766],
-[ +0.1332],
-[ -0.0012],
-[ +0.2332],
-[ -0.0380],
-[ -0.3066],
-[ -0.1738],
-[ -0.2982],
-[ +0.0285],
-[ -0.1548],
-[ +0.2539],
-[ -0.2544],
-[ +0.2006],
-[ -0.4121],
-[ -0.2084],
-[ -0.0381],
-[ +0.2733],
-[ -0.3076],
-[ +0.0013],
-[ +0.0957],
-[ -0.1298],
-[ -0.1112]
+weights_dense2_b = np.array([
+    -0.0655, +0.0020, +0.0358, -0.0192, +0.0570, +0.0000, +0.0711, -0.0145,
+    +0.0294, +0.0139, -0.0215, -0.0952, +0.0000, +0.0526, -0.0585, +0.0633,
+    -0.0332, +0.0030, +0.0107, +0.0830, +0.0140, +0.0888, -0.1115, -0.0722,
+    +0.0240, +0.0476, -0.0807, -0.0421, +0.0000, -0.0557, -0.0403, +0.0034
 ])
 
-weights_final_b = np.array([ +0.0352])
+weights_final_w = np.array([[-0.0230], [+0.0730], [-0.2093], [+0.0463],
+                            [-0.1983], [-0.0031], [+0.2101], [-0.0066],
+                            [-0.1481], [-0.1615], [-0.1766], [+0.1332],
+                            [-0.0012], [+0.2332], [-0.0380], [-0.3066],
+                            [-0.1738], [-0.2982], [+0.0285], [-0.1548],
+                            [+0.2539], [-0.2544], [+0.2006], [-0.4121],
+                            [-0.2084], [-0.0381], [+0.2733], [-0.3076],
+                            [+0.0013], [+0.0957], [-0.1298], [-0.1112]])
+
+weights_final_b = np.array([+0.0352])
+# yapf: enable
 
-if __name__=="__main__":
-    main()
+if __name__ == "__main__":
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumSwingupBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumSwingupBulletEnv_v0_2017may.py
index 5ce5bae7f..8c1ccd08d 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumSwingupBulletEnv_v0_2017may.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumSwingupBulletEnv_v0_2017may.py
@@ -2,175 +2,538 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 import numpy as np
 import pybullet_envs
 import time
 
+
 def relu(x):
-    return np.maximum(x, 0)
+  return np.maximum(x, 0)
+
 
 class SmallReactivePolicy:
-    "Simple multi-layer perceptron policy, no internal state"
-    def __init__(self, observation_space, action_space):
-        assert weights_dense1_w.shape == (observation_space.shape[0], 64.0)
-        assert weights_dense2_w.shape == (64.0, 32.0)
-        assert weights_final_w.shape  == (32.0, action_space.shape[0])
-
-    def act(self, ob):
-        x = ob
-        x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
-        x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
-        x = np.dot(x, weights_final_w) + weights_final_b
-        return x
+  "Simple multi-layer perceptron policy, no internal state"
+
+  def __init__(self, observation_space, action_space):
+    assert weights_dense1_w.shape == (observation_space.shape[0], 64.0)
+    assert weights_dense2_w.shape == (64.0, 32.0)
+    assert weights_final_w.shape == (32.0, action_space.shape[0])
+
+  def act(self, ob):
+    x = ob
+    x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
+    x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
+    x = np.dot(x, weights_final_w) + weights_final_b
+    return x
+
 
 def main():
-    env = gym.make("InvertedPendulumSwingupBulletEnv-v0")
-    env.render(mode="human")
-   
-    pi = SmallReactivePolicy(env.observation_space, env.action_space)
+  env = gym.make("InvertedPendulumSwingupBulletEnv-v0")
+  env.render(mode="human")
+
+  pi = SmallReactivePolicy(env.observation_space, env.action_space)
+
+  while 1:
+    frame = 0
+    score = 0
+    restart_delay = 0
+    obs = env.reset()
 
     while 1:
-        frame = 0
-        score = 0
-        restart_delay = 0
-        obs = env.reset()
-
-        while 1:
-            time.sleep(1./60.)
-            a = pi.act(obs)
-            obs, r, done, _ = env.step(a)
-            score += r
-            frame += 1
-            still_open = env.render("human")
-            if still_open==False:
-                return
-            if not done: continue
-            if restart_delay==0:
-                print("score=%0.2f in %i frames" % (score, frame))
-                restart_delay = 60*2  # 2 sec at 60 fps
-            else:
-                restart_delay -= 1
-                if restart_delay > 0: continue
-                break
-
-weights_dense1_w = np.array([
-[ +0.5877, -0.5825, -0.5542, -0.2557, -0.4485, +1.4126, +0.2701, -0.6204, -0.2580, +0.2106, -0.2296, +0.7949, +0.6224, -0.0186, +0.4216, +1.0924, -0.1538, -0.2818, +0.4855, -0.2496, +0.7461, -0.6156, +0.0801, +0.7871, -0.4312, -0.9585, +0.1566, -0.2218, -1.0393, +0.6104, -0.5339, +0.8258, +0.4064, +0.0503, +0.4753, -0.8161, +0.0812, +0.2311, -0.9492, -1.1791, +1.2375, +0.2916, +1.2290, +0.2796, -0.8864, -1.1424, -0.5714, +0.1413, +0.7340, -0.4152, +0.2832, -0.3886, +0.4810, -0.7092, -0.5966, +0.1089, +0.1007, +0.5226, -0.3343, +0.1760, +0.4099, -0.9913, -1.1694, -1.0018],
-[ +0.4054, +0.2495, +0.5483, +0.7193, -0.1833, -0.2237, -0.4353, -0.1005, +0.2848, +0.3193, +0.2551, -0.1267, -0.7200, +0.3952, +0.3390, +0.2123, +0.1388, +0.8869, -0.1095, -0.1718, -0.4128, -0.7047, -1.1383, +0.6552, -0.0037, -0.4306, +0.2749, -0.9121, +0.4406, -0.0163, +0.4852, +0.6150, +0.1354, -0.7839, +0.2261, +0.3988, -0.2867, -0.5369, -0.0788, +0.0125, +0.2645, +0.1614, +0.7531, +0.5786, +0.6903, -0.7974, -0.2934, -0.3407, -0.7366, -0.1585, +1.0333, -0.0183, +0.2690, -0.5674, -0.0266, +0.0898, -0.1441, -0.0988, +0.7260, +0.7994, +0.1521, -0.3210, -0.1403, -0.2685],
-[ -0.1050, -0.1826, +0.4717, -0.3515, +0.9648, -0.6372, -0.4686, +0.6959, +0.3540, +0.3515, +0.3239, -1.6177, -0.0651, +0.4653, +0.5058, +0.3465, -0.6693, -0.1118, -0.9582, -1.5053, -0.2256, -0.1989, -0.1901, -0.4282, -1.3479, -0.5629, +0.6828, -0.3515, -0.4724, +0.4618, +0.3008, +0.1280, +0.3720, -0.0545, +0.3104, -0.2527, +0.4614, +0.4994, -0.0099, +0.4597, -0.2667, -0.0374, -0.3393, +0.2675, -0.2635, -0.6062, +0.6404, +0.4500, -0.5105, -1.5838, -0.1396, +0.8804, +0.5794, -0.6823, -0.2125, +0.4510, +0.2424, +0.3407, -0.3354, +0.1306, -1.0006, +0.2358, +0.6479, +0.2027],
-[ +0.7453, +0.8937, -0.9068, +0.2950, +0.4412, -0.6005, -1.3008, -0.0299, -0.6434, +1.4992, +0.7437, +0.4271, -0.0549, +1.2337, +1.6758, -0.7335, +0.2251, -1.1287, -1.0611, -0.4609, -1.6821, -0.3495, -0.5520, +0.2407, -1.0738, +0.9423, -0.6853, -0.0193, +0.6365, +0.3979, -1.8896, -1.1404, +0.4708, -0.2113, +1.3380, +0.6163, +0.5543, +0.4372, -0.3004, +1.0200, -0.4211, +0.5034, -0.1635, +2.0363, +0.1362, -0.2348, +0.7659, -1.6971, -1.3513, -0.2940, +1.2592, -0.3885, +0.5544, +0.8858, +0.0189, -1.8006, +1.3254, +0.6919, +0.3571, -0.5189, -0.0115, -1.7036, -0.8770, +1.2328],
-[ -0.3661, +0.5205, +0.6454, +0.9826, -0.2945, -0.3074, +0.6830, +0.3798, +0.0578, +0.2303, +0.0181, -0.3768, -0.1607, +0.9089, +0.2910, -0.0265, -0.7435, +0.2932, -0.4173, +0.2959, +0.2079, +0.2649, +0.4184, +0.5963, +0.2120, +0.1885, +0.3611, +0.5193, +0.4538, +0.7072, +0.2274, +0.2233, +0.3970, +0.0560, +0.2132, +0.0186, +0.1522, -0.2460, +0.6636, +0.4592, -0.5299, +1.1159, -0.2861, +0.3664, -0.0648, +0.1958, -0.0180, -0.2585, +0.1408, +0.2639, -0.3697, +0.4727, +1.0321, +0.0851, +0.8350, +0.0830, +0.1625, -0.3849, +0.3014, -0.1514, -0.5960, -0.4083, -0.1023, +0.2080]
-])
+      time.sleep(1. / 60.)
+      a = pi.act(obs)
+      obs, r, done, _ = env.step(a)
+      score += r
+      frame += 1
+      still_open = env.render("human")
+      if still_open == False:
+        return
+      if not done: continue
+      if restart_delay == 0:
+        print("score=%0.2f in %i frames" % (score, frame))
+        restart_delay = 60 * 2  # 2 sec at 60 fps
+      else:
+        restart_delay -= 1
+        if restart_delay > 0: continue
+        break
+
 
-weights_dense1_b = np.array([ -0.4441, -0.2462, -0.2997, +0.3283, -0.2751, +0.0474, +0.0720, -0.2133, -0.0770, -0.0053, +0.0138, -0.3554, -0.2999, -0.2340, +0.0054, +0.4380, -0.1461, -0.2035, -0.8094, +0.0909, -0.1714, +0.2412, -0.1519, +0.0391, +0.1525, +0.1798, +0.1041, +0.4503, -0.0088, +0.0323, -0.0414, +0.4621, +0.1720, -0.1793, +0.1734, +0.1588, +0.2802, +0.1220, +0.1011, -0.1334, -0.0663, +0.4778, +0.1110, -0.1536, +0.1873, -0.0090, -0.5979, +0.3604, -0.2515, +0.4471, +0.2444, -0.2565, -0.1102, +0.0982, -0.0625, +0.3902, -0.0248, -0.2240, +0.0894, -0.0671, -0.3344, -0.0089, -0.0793, +0.2673])
+# yapf: disable
+weights_dense1_w = np.array(
+    [[
+        +0.5877, -0.5825, -0.5542, -0.2557, -0.4485, +1.4126, +0.2701, -0.6204,
+        -0.2580, +0.2106, -0.2296, +0.7949, +0.6224, -0.0186, +0.4216, +1.0924,
+        -0.1538, -0.2818, +0.4855, -0.2496, +0.7461, -0.6156, +0.0801, +0.7871,
+        -0.4312, -0.9585, +0.1566, -0.2218, -1.0393, +0.6104, -0.5339, +0.8258,
+        +0.4064, +0.0503, +0.4753, -0.8161, +0.0812, +0.2311, -0.9492, -1.1791,
+        +1.2375, +0.2916, +1.2290, +0.2796, -0.8864, -1.1424, -0.5714, +0.1413,
+        +0.7340, -0.4152, +0.2832, -0.3886, +0.4810, -0.7092, -0.5966, +0.1089,
+        +0.1007, +0.5226, -0.3343, +0.1760, +0.4099, -0.9913, -1.1694, -1.0018
+    ],
+     [
+         +0.4054, +0.2495, +0.5483, +0.7193, -0.1833, -0.2237, -0.4353,
+         -0.1005, +0.2848, +0.3193, +0.2551, -0.1267, -0.7200, +0.3952,
+         +0.3390, +0.2123, +0.1388, +0.8869, -0.1095, -0.1718, -0.4128,
+         -0.7047, -1.1383, +0.6552, -0.0037, -0.4306, +0.2749, -0.9121,
+         +0.4406, -0.0163, +0.4852, +0.6150, +0.1354, -0.7839, +0.2261,
+         +0.3988, -0.2867, -0.5369, -0.0788, +0.0125, +0.2645, +0.1614,
+         +0.7531, +0.5786, +0.6903, -0.7974, -0.2934, -0.3407, -0.7366,
+         -0.1585, +1.0333, -0.0183, +0.2690, -0.5674, -0.0266, +0.0898,
+         -0.1441, -0.0988, +0.7260, +0.7994, +0.1521, -0.3210, -0.1403, -0.2685
+     ],
+     [
+         -0.1050, -0.1826, +0.4717, -0.3515, +0.9648, -0.6372, -0.4686,
+         +0.6959, +0.3540, +0.3515, +0.3239, -1.6177, -0.0651, +0.4653,
+         +0.5058, +0.3465, -0.6693, -0.1118, -0.9582, -1.5053, -0.2256,
+         -0.1989, -0.1901, -0.4282, -1.3479, -0.5629, +0.6828, -0.3515,
+         -0.4724, +0.4618, +0.3008, +0.1280, +0.3720, -0.0545, +0.3104,
+         -0.2527, +0.4614, +0.4994, -0.0099, +0.4597, -0.2667, -0.0374,
+         -0.3393, +0.2675, -0.2635, -0.6062, +0.6404, +0.4500, -0.5105,
+         -1.5838, -0.1396, +0.8804, +0.5794, -0.6823, -0.2125, +0.4510,
+         +0.2424, +0.3407, -0.3354, +0.1306, -1.0006, +0.2358, +0.6479, +0.2027
+     ],
+     [
+         +0.7453, +0.8937, -0.9068, +0.2950, +0.4412, -0.6005, -1.3008,
+         -0.0299, -0.6434, +1.4992, +0.7437, +0.4271, -0.0549, +1.2337,
+         +1.6758, -0.7335, +0.2251, -1.1287, -1.0611, -0.4609, -1.6821,
+         -0.3495, -0.5520, +0.2407, -1.0738, +0.9423, -0.6853, -0.0193,
+         +0.6365, +0.3979, -1.8896, -1.1404, +0.4708, -0.2113, +1.3380,
+         +0.6163, +0.5543, +0.4372, -0.3004, +1.0200, -0.4211, +0.5034,
+         -0.1635, +2.0363, +0.1362, -0.2348, +0.7659, -1.6971, -1.3513,
+         -0.2940, +1.2592, -0.3885, +0.5544, +0.8858, +0.0189, -1.8006,
+         +1.3254, +0.6919, +0.3571, -0.5189, -0.0115, -1.7036, -0.8770, +1.2328
+     ],
+     [
+         -0.3661, +0.5205, +0.6454, +0.9826, -0.2945, -0.3074, +0.6830,
+         +0.3798, +0.0578, +0.2303, +0.0181, -0.3768, -0.1607, +0.9089,
+         +0.2910, -0.0265, -0.7435, +0.2932, -0.4173, +0.2959, +0.2079,
+         +0.2649, +0.4184, +0.5963, +0.2120, +0.1885, +0.3611, +0.5193,
+         +0.4538, +0.7072, +0.2274, +0.2233, +0.3970, +0.0560, +0.2132,
+         +0.0186, +0.1522, -0.2460, +0.6636, +0.4592, -0.5299, +1.1159,
+         -0.2861, +0.3664, -0.0648, +0.1958, -0.0180, -0.2585, +0.1408,
+         +0.2639, -0.3697, +0.4727, +1.0321, +0.0851, +0.8350, +0.0830,
+         +0.1625, -0.3849, +0.3014, -0.1514, -0.5960, -0.4083, -0.1023, +0.2080
+     ]])
+
+weights_dense1_b = np.array([
+    -0.4441, -0.2462, -0.2997, +0.3283, -0.2751, +0.0474, +0.0720, -0.2133,
+    -0.0770, -0.0053, +0.0138, -0.3554, -0.2999, -0.2340, +0.0054, +0.4380,
+    -0.1461, -0.2035, -0.8094, +0.0909, -0.1714, +0.2412, -0.1519, +0.0391,
+    +0.1525, +0.1798, +0.1041, +0.4503, -0.0088, +0.0323, -0.0414, +0.4621,
+    +0.1720, -0.1793, +0.1734, +0.1588, +0.2802, +0.1220, +0.1011, -0.1334,
+    -0.0663, +0.4778, +0.1110, -0.1536, +0.1873, -0.0090, -0.5979, +0.3604,
+    -0.2515, +0.4471, +0.2444, -0.2565, -0.1102, +0.0982, -0.0625, +0.3902,
+    -0.0248, -0.2240, +0.0894, -0.0671, -0.3344, -0.0089, -0.0793, +0.2673
+])
 
 weights_dense2_w = np.array([
-[ +0.1063, +0.2017, +0.0029, -0.2442, -0.1362, +0.2871, +0.2270, -0.1260, +0.5271, -0.1744, -0.4323, +0.3637, -0.0083, -0.0547, +0.4549, -0.0164, +0.0913, -0.1635, +0.3583, +0.3020, +0.2240, -0.3561, +0.0689, +0.0126, +0.0508, -1.2876, -0.0003, -0.0464, -0.2184, -0.2538, -0.5314, +0.5790],
-[ -0.2180, +0.9455, +0.1446, -0.0724, +0.3771, -0.4290, +0.3908, -0.1787, -0.1009, -0.0539, -0.5364, -0.5032, -0.0631, -0.1185, -0.9890, +0.1935, -1.3280, -0.9275, +0.0670, -0.4234, -0.2061, +0.2674, +0.2963, +0.5353, -0.0221, -0.3095, +0.3255, -0.4568, +0.1337, -0.2826, -0.0538, -1.2748],
-[ +0.3038, +0.0690, +0.1495, -0.1801, -0.0140, -0.1370, -0.2094, -1.9336, +0.2150, -0.5506, +0.3097, -0.9412, +0.1507, -0.0708, -0.8874, -0.1183, -0.0580, -0.7503, +0.2276, -0.3497, +0.0067, +0.2541, -0.1207, +0.5209, +0.5381, -1.2157, +0.4692, -0.0536, -0.2078, -0.9902, -1.0954, -1.3646],
-[ +0.0581, -1.0529, -0.0581, +0.0473, -0.1228, +0.0913, -0.7037, +0.0711, +0.2062, -0.2102, +0.0475, -0.5266, +0.1324, -1.7822, -0.2985, +0.0172, +0.0110, -0.1624, -0.3990, -0.3165, +0.1287, -0.5655, +1.3905, -1.5117, +0.1874, -0.5032, -0.3292, +0.3378, -0.4749, +0.0765, +0.4345, -0.1121],
-[ -0.1315, +0.2873, -1.0164, +0.2925, -0.5024, +0.2321, -1.3038, -0.7796, +0.0830, -0.3378, -0.1037, +0.0033, -0.7885, +0.4841, +0.1578, +0.1771, +0.1991, -0.1073, -0.0181, +0.0496, +0.0919, +0.0585, +0.4595, +0.1634, -0.2220, -0.0226, +0.4703, -1.8576, +0.3075, -0.4581, +0.2507, +0.2085],
-[ +0.2704, +0.0379, +0.2313, -0.5561, -0.7413, -0.7693, +0.4787, +0.3033, -1.3572, -0.1323, -0.5202, -0.6937, -0.6824, -0.1782, -1.1647, -0.3461, -0.8537, +0.5416, +0.0638, -0.4208, -0.4464, +0.0009, -0.4284, +0.1806, +0.4172, -0.5477, +0.5549, +0.1937, -0.6029, +0.2084, -0.8289, -0.4554],
-[ +0.3719, +0.4292, +0.2655, -0.1071, -0.1848, -0.0651, -0.4942, +0.0514, -0.1364, -0.1573, -0.0880, -0.4625, -0.0889, +0.2049, -1.2166, -0.2164, -0.3680, -0.7242, -0.1208, -0.3569, +0.0591, +0.3773, -1.2525, +0.4139, -0.1203, -0.2808, -0.2460, -0.3056, -0.2309, +0.1638, +0.1502, -0.2354],
-[ +0.2204, +0.3725, +0.1919, +0.1579, +0.0064, +0.0469, -0.5103, -0.5866, +0.0043, -0.2127, -0.0816, +0.4270, -0.0504, +0.2804, -0.1278, -0.0507, +0.1206, -0.6903, -0.2278, -0.0725, +0.2198, +0.1067, +0.2162, +0.2341, -0.6394, -1.2196, +0.3075, -1.0066, +0.2299, -0.1218, -0.0533, +0.3365],
-[ +0.2458, -0.1112, -0.6971, +0.1730, +0.0093, -0.0066, -0.2500, -1.2508, -0.0108, -0.4091, -0.5608, -0.0239, +0.4287, -0.1187, +0.0476, -0.1859, +0.1335, +0.0564, +0.2657, +0.3620, +0.4023, +0.0518, -0.1151, +0.0172, +0.0270, -0.4894, +0.3967, +0.1362, +0.1078, -1.4673, -0.6417, +0.0105],
-[ -1.2388, -0.5692, +0.2738, -0.8659, +0.1514, +0.0501, -0.3654, -0.9175, +0.1314, -0.4386, +0.1715, +0.2538, +0.1051, -0.1091, +0.1875, -0.0295, -0.4012, -0.5032, -0.6742, -0.1109, +0.1125, -0.5023, -0.2032, -0.2740, -0.9510, +0.9708, -0.0643, -0.5463, -0.0895, -0.7491, +0.6833, +0.1855],
-[ -0.4298, -0.0464, -0.0294, -0.0743, +0.0902, -0.6215, +0.0848, -0.3727, +0.2700, -0.3201, -0.2578, +0.2471, -0.6535, +0.2581, +0.2505, -0.1900, +0.1637, -1.5921, -0.1360, -0.0777, -0.0092, +0.0816, +0.0996, -0.0197, -0.7934, +0.0909, +0.2011, +0.1988, +0.1273, -0.0366, +0.0466, +0.1477],
-[ +0.1492, +0.1446, +0.4695, -0.4109, -0.0883, -0.1199, +0.5098, +0.4549, -0.2600, +0.1315, -0.2831, +0.4905, +0.0915, +0.1289, -1.1824, +0.3743, -0.3307, -0.2300, -0.6317, -0.2493, -1.2151, -0.1466, -0.4567, -1.0819, +0.2878, -1.0267, +0.2882, +0.5518, -0.0761, +0.3592, +0.1387, -0.6827],
-[ +0.1444, -0.1397, +0.1136, +0.0452, -0.3338, +0.1050, +0.2811, -0.1238, -0.4973, -0.0804, +0.0148, +0.1621, -0.8240, +0.2157, +0.1887, +0.0192, -1.2242, +0.2255, -0.2271, -0.4326, -0.0362, +0.0116, -0.2229, +0.4142, +0.3929, -0.2313, +0.1735, +0.0598, +0.1726, +0.1078, +0.1444, +0.3110],
-[ -0.7894, -0.0617, +0.3583, -0.7723, +0.1178, +0.0533, -0.2285, -1.2298, +0.1727, -0.3313, +0.1193, -0.6488, +0.2996, -0.5132, -0.3868, +0.0174, +0.1475, +0.3516, -0.5912, -0.8573, +0.0606, -0.0528, -0.1981, +0.0196, -0.1688, -0.3554, -0.0234, -0.7206, +0.2903, -0.9404, -1.5141, -0.2722],
-[ -1.2703, -0.7481, +0.4099, -0.7886, +0.2750, -0.0530, -0.6855, -0.7960, +0.2931, +0.0986, +0.2574, +0.3546, -0.1636, -0.6219, +0.3183, +0.1384, -0.0684, -1.2439, -0.8255, -0.1529, +0.2066, -0.4686, +0.3696, -0.2439, -1.6751, +0.7610, -0.5769, -0.5236, +0.2315, -0.4293, +0.7452, +0.6290],
-[ +0.1636, -0.0258, -0.0946, +0.5087, -0.2138, -0.5867, -0.1223, +0.0970, -0.5604, -0.3155, +0.2778, +0.2844, -0.6220, +0.0870, +0.1567, -0.8622, +0.2385, +0.3428, +0.1315, +0.2830, -0.3414, -0.1083, -0.0021, +0.4448, -0.1093, +0.5797, +0.5512, +0.0886, +0.2515, -0.1098, -0.5983, -0.1664],
-[ +0.0332, -0.1306, +0.2431, -0.5394, -0.2755, -0.4544, +0.3230, +0.0475, -0.4289, +0.1263, -0.7816, +0.2001, +0.1425, +0.2706, -1.0709, -0.3947, -1.3802, -0.1414, -0.1457, -0.7114, -0.6793, -0.0257, -0.8971, -0.2432, +0.0006, -0.3711, +0.2958, -0.0177, +0.1747, -0.0733, -0.3160, -0.6292],
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+        +0.4021, -0.2582, +0.3381, +0.3950, +0.0503, +0.0106, +0.2930, +0.2948,
+        +0.0231, +0.4963, +0.6190, +0.3516, -0.9469, -0.0323, -0.0254, -0.3314
+    ],
+    [
+        -0.0666, -0.3086, +0.0050, -0.7425, +0.0498, -0.1735, +0.0643, -0.7302,
+        -0.2838, -0.4926, +0.2588, -1.0888, +0.0914, -0.2110, +0.3146, +0.0769,
+        +0.1527, -0.7908, +0.1144, -0.4159, -0.1099, -0.2469, +0.1520, +0.3110,
+        -0.6905, +0.1466, -0.1214, -0.5032, +0.0486, -0.3263, +0.0748, +0.4858
+    ],
+    [
+        +0.3977, -0.0844, +0.0825, -0.0687, -0.8396, +0.2654, -0.0521, -0.1041,
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+        -1.6387, +0.1836, -0.2685, -0.4428, +0.1816, -0.1108, +0.1340, +0.0555,
+        -0.0085, +0.0386, +0.1277, +0.0295, -0.7560, +0.0657, +0.0095, +0.0913
+    ],
+    [
+        -0.3619, +0.2578, +0.3163, +0.1775, +0.1437, -0.1839, +0.1491, -0.4246,
+        +0.3383, -0.5554, +0.2321, +0.2196, -0.2709, -0.0673, +0.0790, +0.0549,
+        +0.0146, -1.4400, -0.3682, -0.4452, +0.1132, -0.0693, +0.4161, -0.0508,
+        -0.2573, +0.3547, +0.2300, -0.0433, +0.3701, -0.0716, +0.0865, +0.3202
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+    [
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+        -0.3198, -0.0513, -0.9552, -0.2212, -0.1091, -0.5052, +0.1874, -0.1514,
+        -0.7181, +0.1132, -0.5173, +0.2874, +0.4601, +0.3317, +0.1209, -0.4715
+    ],
+    [
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+        -0.3284, -0.6936, -0.2142, -0.0067, -0.8245, +0.0604, +0.2082, +0.2818,
+        +0.4094, -1.2403, +0.2902, -0.4497, +0.3492, -0.2630, +0.2257, +0.2616,
+        -0.0756, +0.3950, +0.1607, -0.4299, -0.0042, -0.3791, +0.0144, +0.3923
+    ],
+    [
+        +0.2782, -0.1456, -0.0002, +0.3011, -0.2252, +0.0572, +0.1349, -0.1567,
+        -0.2850, -0.2994, +0.1602, -0.0868, -0.5167, +0.4240, +0.2210, +0.1657,
+        +0.0883, -0.1288, -0.0227, -0.3949, +0.1043, -0.1381, +0.0739, -0.0357,
+        -0.1723, -0.2657, +0.1199, -0.1253, -0.8570, +0.1793, +0.0042, +0.2571
+    ],
+    [
+        +0.1808, +0.0781, -0.0530, +0.3645, -0.0659, -0.0229, +0.0723, -0.2956,
+        +0.0014, +0.0886, -0.2523, -1.1491, +0.1169, +0.1121, -0.8267, +0.0281,
+        -0.1044, -0.2294, +0.0513, -0.9215, +0.2674, +0.0013, -0.0650, +0.2553,
+        +0.0816, -0.7934, +0.2155, -1.3771, -0.1983, -0.3055, +0.2549, +0.0883
+    ],
+    [
+        -0.1989, -0.3779, +0.2484, +0.0978, +0.3002, -0.2595, -0.0993, -0.7726,
+        -0.0245, -0.6115, +0.0579, -0.7989, -0.0208, -0.0149, -1.4722, +0.3503,
+        -0.1758, -0.4039, -1.9504, -0.2489, -0.2551, +0.0146, -0.1026, -0.6208,
+        +0.3920, -0.8281, -0.3682, -0.3127, +0.1773, -0.0195, -0.3558, -0.4386
+    ],
+    [
+        +0.3965, +0.2776, -0.0051, -0.3705, -0.3877, +0.0462, +0.2481, +0.0638,
+        -0.5678, +0.2069, -0.2101, -0.3165, +0.0694, +0.3458, -1.0740, -0.2276,
+        -0.2802, +0.1290, +0.3323, -0.6620, -1.0497, +0.0449, -1.4877, +0.6505,
+        -0.0039, -0.5675, +0.1965, +0.1813, -0.1576, +0.2611, +0.0413, -0.7096
+    ],
+    [
+        -0.2771, -0.2090, +0.3171, -1.1884, +0.0306, -0.0635, -0.3072, +0.1631,
+        -0.3107, -0.4344, +0.0475, -1.1032, +0.0050, -1.6227, -0.2919, +0.1205,
+        +0.2610, -0.8912, -0.0364, -0.0302, +0.2187, -0.3477, -0.2162, +0.0541,
+        -0.1731, -0.5533, -1.1136, -0.3114, -0.0904, +0.0234, -0.1263, -0.4608
+    ],
+    [
+        +0.2534, +0.2506, -0.5988, +0.3239, -0.5094, +0.2584, -0.1520, -0.3674,
+        -0.5281, -0.2938, -0.0664, +0.3468, +0.1871, +0.4229, -1.1005, +0.0895,
+        -0.1058, -0.2018, +0.5277, +0.1065, -2.9736, +0.0834, -0.4339, +0.5220,
+        -0.3065, +0.0976, +0.4859, -0.0876, -0.5134, +0.0273, -0.4311, -0.0629
+    ],
+    [
+        -1.0013, -0.7660, +0.4058, -3.0321, +0.0533, +0.0794, -1.2917, -1.0423,
+        +0.0235, -0.2441, +0.2986, +0.2793, +0.3185, -0.7738, +0.0709, +0.1806,
+        +0.0065, -0.3429, -0.5904, -0.2240, -0.2247, -0.1551, +0.2479, -0.7799,
+        -0.4368, +0.2717, -0.3030, +0.2230, -0.1252, -0.1713, -0.4256, +0.0946
+    ],
+    [
+        -0.5044, -0.3197, -0.0715, -0.0414, -0.2140, -0.2098, +0.3549, +0.0071,
+        +0.2459, +0.0855, -0.4905, +0.4785, -0.0644, -0.0442, -0.5088, +0.1229,
+        +0.3045, +0.0949, +0.5608, +0.0035, +0.2524, -0.1201, +0.4582, -0.9841,
+        -0.2432, -0.4455, +0.1591, -0.0743, +0.1235, +0.1924, -0.2510, -0.0401
+    ],
+    [
+        +0.5910, -0.1650, -0.3341, +0.3136, -0.0819, -0.1846, -0.3609, +0.2772,
+        -0.0841, +0.0612, -1.0691, +0.0500, -0.9307, +0.4375, -0.9497, -0.0597,
+        -0.7687, +0.2086, +0.2169, -0.2657, -0.5765, -0.1814, -1.1223, +0.2315,
+        +0.8662, +0.0936, +0.0851, -1.8539, -0.0759, +0.4064, +0.2069, -0.8922
+    ],
+    [
+        -0.1478, +0.3415, -0.2042, +0.5568, -0.7672, -0.1465, -0.1311, -0.2273,
+        +0.0602, -0.2321, -0.2689, +0.1515, -1.0434, +0.2948, -0.4986, +0.1426,
+        -0.7398, -0.4810, -0.0648, -0.3290, -0.1646, -0.1314, +0.0100, +0.3540,
+        -0.2790, -0.0118, +0.4205, -0.5476, -0.1409, -0.1341, +0.3308, +0.1991
+    ],
+    [
+        +0.1532, -0.2862, -1.0844, +0.2213, -1.5302, -0.1382, -0.3119, -1.5098,
+        -0.5984, -0.8033, +0.0835, -0.5982, -0.9022, +0.0325, -0.0693, -0.7834,
+        +0.2342, +0.2223, +0.3314, +0.1252, +0.2134, -0.1843, -0.2085, +0.3213,
+        +0.2308, +0.4200, +0.0852, -0.1438, -0.4427, +0.2863, -0.6166, -0.2677
+    ],
+    [
+        +0.2118, -0.5590, -0.3589, +0.1854, -1.1902, -0.2337, +0.3533, -0.0890,
+        -0.3013, +0.4551, -0.2130, -1.0383, -0.2290, -0.1976, -1.3175, -0.5657,
+        -0.2857, +0.5560, +0.1437, -0.3211, +0.1788, -0.1494, -1.0336, +0.2679,
+        +0.5221, +0.5256, +0.1761, -0.0573, -0.2895, -0.0307, -0.2395, -0.8144
+    ],
+    [
+        -0.6139, -1.0856, -0.5362, -1.1959, -0.3413, -0.2647, -0.3687, +0.4510,
+        +0.0818, +0.0710, -0.3482, -0.0611, +0.0474, -0.7248, -0.3042, -0.0303,
+        -0.3762, +0.1941, -0.8735, +0.1872, -0.3612, -0.4090, +0.2817, -0.5842,
+        +0.5135, +0.8080, -0.9240, -0.1925, -0.6654, +0.4267, +0.5304, -0.3396
+    ],
+    [
+        +0.1054, -0.1771, -0.1990, +0.3935, -0.1743, +0.0638, +0.3047, -0.0899,
+        +0.4220, +0.1633, +0.1666, +0.1688, +0.0731, -0.0455, +0.2421, +0.4481,
+        +0.5427, -0.1530, -0.1694, +0.4192, -0.3225, +0.1410, +0.2042, -2.2442,
+        -0.4848, -0.9054, -0.2178, +0.3965, +0.4502, +0.1305, -0.0444, +0.1641
+    ],
+    [
+        +0.1336, +0.4509, +0.0056, +0.0940, -0.3145, -0.1580, -0.1152, -0.2864,
+        -0.0145, -0.3372, +0.1072, -0.3662, -0.9957, +0.3660, +0.1886, -0.2086,
+        +0.2193, -0.6053, +0.0879, +0.0350, +0.0216, +0.3407, -0.0691, +0.1355,
+        -0.2493, -1.5064, +0.3744, -0.2654, -0.1921, -0.6361, -0.6030, +0.3290
+    ],
+    [
+        -0.4868, -0.0926, +0.1334, -0.8699, +0.0689, +0.1350, +0.0003, -1.6050,
+        -0.2677, -0.6097, +0.0119, -1.0122, -0.1318, -0.8405, -0.1366, -0.1088,
+        +0.0375, -1.0216, -0.0891, -0.2447, -0.0060, -0.3751, +0.1240, +0.0514,
+        -0.5080, -1.2536, +0.4369, -0.1020, +0.1563, -0.6330, -2.3150, +0.0109
+    ],
+    [
+        -0.5112, +0.0425, -0.1887, -0.0305, +0.0264, +0.3452, -0.0750, +0.4954,
+        +0.4284, -0.0069, +0.2399, +0.9169, -0.3441, +0.0420, -0.3492, +0.4726,
+        -0.1203, -0.0110, -1.1251, -0.1880, -0.2703, -0.1990, +0.8598, -0.0085,
+        +0.4910, -0.1798, +0.1495, -0.4904, -0.3907, +0.5274, +0.4777, +0.4670
+    ],
+    [
+        +0.4048, -0.2962, -0.0535, +0.3467, -0.0456, -0.0875, -0.0220, -0.2064,
+        -0.8052, -0.2940, -0.1660, -1.3446, -0.0124, -0.3980, -0.0199, +0.0871,
+        -0.4781, -1.0247, +0.2848, -0.2992, -0.1778, -0.0626, -0.0618, +0.0792,
+        -0.7679, -1.4193, +0.0787, -0.3910, -0.1448, -0.2650, -0.3079, -1.2104
+    ],
+    [
+        +0.4581, -0.6689, -0.1144, -0.1282, -2.0230, -0.1221, -0.2954, -1.2605,
+        -1.0560, -0.8669, +0.2610, -0.3799, -0.2883, -0.2970, +0.1364, -1.5987,
+        +0.2303, +0.6106, +0.3841, +0.0955, -0.3148, -0.2655, +0.0052, +0.2312,
+        +0.1658, +0.4766, +0.1847, -0.1055, -0.8075, -0.1123, -0.6706, -0.6556
+    ],
+    [
+        +0.1192, -0.1971, +0.4472, +0.1296, +0.0370, -0.1341, -0.7736, -0.1778,
+        -0.0172, -0.2200, +0.1248, -0.4126, -0.3722, -0.2830, +0.0294, +0.2753,
+        -0.2527, -1.0083, -0.7886, -1.5356, +0.0627, -0.2736, +0.4009, -0.4766,
+        -0.2815, +0.8060, -0.0681, -0.8295, +0.4980, -0.0494, +0.4414, +0.4709
+    ],
+    [
+        -0.2893, -0.0060, +0.1617, +0.3636, -0.0534, -0.1653, +0.2161, -0.2260,
+        +0.0668, -0.3423, +0.0087, +0.3678, -0.1448, +0.3106, +0.2831, +0.0889,
+        -0.1325, -0.0667, -0.1139, +0.1482, +0.1164, -0.1613, +0.0733, +0.0005,
+        -0.0419, -0.0656, +0.0986, -0.1560, -0.1506, -0.1254, -0.0902, +0.2643
+    ],
+    [
+        -0.2274, -0.5965, -0.0342, -0.6827, -0.1276, +0.0802, -1.2401, +0.2169,
+        +0.0531, -0.0964, +0.2187, +0.0299, +0.2797, -0.7842, -0.5032, +0.1321,
+        -0.2005, -0.3383, -0.3343, +0.1237, -0.0915, -0.6670, +0.0473, -0.6602,
+        +0.1260, -1.2568, -0.2235, +0.1255, +0.3263, +0.1078, -0.0685, -0.0085
+    ],
+    [
+        +0.3530, -0.3798, -0.5576, +0.1040, -0.1875, +0.1399, -0.1539, -1.3570,
+        -0.1105, +0.0370, -0.4067, +0.2991, +0.2811, +0.1082, -0.0573, +0.2104,
+        -0.1550, +0.3365, +0.5019, +0.4842, +0.4671, +0.2578, -0.0029, +0.0016,
+        -0.1533, -0.2459, +0.1866, +0.0699, -0.1873, -1.1082, -0.9151, -0.1758
+    ],
+    [
+        +0.2397, +0.2045, -0.2370, -0.3293, -0.3153, -0.2131, +0.1407, -0.0721,
+        +0.0723, -0.0019, -0.3940, +0.1340, +0.3550, +0.1190, -0.6068, -0.0747,
+        -0.7712, +0.1922, +0.6519, -0.0651, -0.4332, +0.0494, -0.7192, +0.4279,
+        -0.1762, -0.5548, +0.1749, -0.2149, -0.6916, -0.0448, -0.1025, +0.0212
+    ],
+    [
+        -0.1101, -0.2853, -0.3405, +0.3059, -0.5009, +0.1139, +0.0602, -0.5256,
+        -0.9340, +0.1189, -0.9900, -0.5092, -1.9114, +0.1249, -0.7890, -1.1437,
+        -0.4686, +0.3687, -0.2993, -0.1058, +0.0966, -0.0284, -0.4845, -0.1683,
+        +0.3489, -0.0173, -0.0521, -0.1265, -0.0182, -0.2870, +0.0246, -1.0009
+    ],
+    [
+        -0.1411, +0.1840, -0.3968, +0.2893, -0.9532, -0.2235, -0.1156, -0.7018,
+        -0.2859, -0.1742, -0.6094, -0.0247, -1.0472, +0.1916, -0.4825, -0.4209,
+        +0.2371, +0.0900, +0.0646, -0.1665, +0.5168, +0.0670, -0.1779, +0.3494,
+        +0.3035, +0.0548, +0.2939, -0.3871, -0.0828, -0.5370, +0.0804, -0.2175
+    ],
+    [
+        +0.4992, +0.1187, -0.0464, +0.7284, +0.1106, -0.0542, -0.3548, +0.3451,
+        +0.0281, -0.4796, -0.2282, -0.3789, -0.1253, -0.0824, -0.3919, +0.1890,
+        -0.1683, -2.1362, -0.9594, -0.6882, +0.6158, -0.2412, +0.2336, -0.0142,
+        -0.9257, +0.3819, -0.1836, -0.7676, +0.3713, -0.1364, +0.3317, +0.3696
+    ]
 ])
 
-weights_dense2_b = np.array([ -0.0528, +0.0930, -0.3614, +0.2145, -0.3644, -0.0033, -0.0702, -0.0928, -0.1018, +0.0424, +0.0130, +0.2634, -0.1167, +0.2412, +0.0852, +0.0047, +0.1958, -0.1322, +0.0218, +0.2207, +0.1946, +0.0936, +0.2900, +0.2404, -0.1711, +0.1214, +0.2968, -0.2935, -0.0390, +0.1330, +0.0325, +0.2185])
-
-weights_final_w = np.array([
-[ -0.2378],
-[ +0.1955],
-[ -0.2006],
-[ -0.5372],
-[ -0.3298],
-[ +0.0891],
-[ -0.3930],
-[ +0.8978],
-[ +0.3177],
-[ +0.5357],
-[ +0.2878],
-[ +0.4998],
-[ +0.2550],
-[ -0.2619],
-[ +1.1990],
-[ +0.3115],
-[ +0.3655],
-[ +0.5774],
-[ -0.4641],
-[ +0.2613],
-[ +0.1928],
-[ +0.1458],
-[ +0.4138],
-[ -0.4969],
-[ +0.4147],
-[ +1.0689],
-[ -0.1562],
-[ -0.3669],
-[ -0.3073],
-[ +0.3354],
-[ +0.9354],
-[ +0.8831]
+weights_dense2_b = np.array([
+    -0.0528, +0.0930, -0.3614, +0.2145, -0.3644, -0.0033, -0.0702, -0.0928,
+    -0.1018, +0.0424, +0.0130, +0.2634, -0.1167, +0.2412, +0.0852, +0.0047,
+    +0.1958, -0.1322, +0.0218, +0.2207, +0.1946, +0.0936, +0.2900, +0.2404,
+    -0.1711, +0.1214, +0.2968, -0.2935, -0.0390, +0.1330, +0.0325, +0.2185
 ])
 
-weights_final_b = np.array([ +0.2753])
+weights_final_w = np.array([[-0.2378], [+0.1955], [-0.2006], [-0.5372],
+                            [-0.3298], [+0.0891], [-0.3930], [+0.8978],
+                            [+0.3177], [+0.5357], [+0.2878], [+0.4998],
+                            [+0.2550], [-0.2619], [+1.1990], [+0.3115],
+                            [+0.3655], [+0.5774], [-0.4641], [+0.2613],
+                            [+0.1928], [+0.1458], [+0.4138], [-0.4969],
+                            [+0.4147], [+1.0689], [-0.1562], [-0.3669],
+                            [-0.3073], [+0.3354], [+0.9354], [+0.8831]])
+
+weights_final_b = np.array([+0.2753])
+# yapf: enable
 
-if __name__=="__main__":
-    main()
+if __name__ == "__main__":
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_Walker2DBulletEnv_v0_2017may.py b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_Walker2DBulletEnv_v0_2017may.py
index 952af87ce..b88e19f51 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_Walker2DBulletEnv_v0_2017may.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_Walker2DBulletEnv_v0_2017may.py
@@ -2,289 +2,2036 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 import numpy as np
 import pybullet_envs
 import time
 
+
 def relu(x):
-    return np.maximum(x, 0)
+  return np.maximum(x, 0)
+
 
 class SmallReactivePolicy:
-    "Simple multi-layer perceptron policy, no internal state"
-    def __init__(self, observation_space, action_space):
-        assert weights_dense1_w.shape == (observation_space.shape[0], 128)
-        assert weights_dense2_w.shape == (128, 64)
-        assert weights_final_w.shape  == (64, action_space.shape[0])
+  "Simple multi-layer perceptron policy, no internal state"
+
+  def __init__(self, observation_space, action_space):
+    assert weights_dense1_w.shape == (observation_space.shape[0], 128)
+    assert weights_dense2_w.shape == (128, 64)
+    assert weights_final_w.shape == (64, action_space.shape[0])
+
+  def act(self, ob):
+    x = ob
+    x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
+    x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
+    x = np.dot(x, weights_final_w) + weights_final_b
+    return x
 
-    def act(self, ob):
-        x = ob
-        x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
-        x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
-        x = np.dot(x, weights_final_w) + weights_final_b
-        return x
 
 def main():
-    env = gym.make("Walker2DBulletEnv-v0")
-    env.render(mode="human")
-    pi = SmallReactivePolicy(env.observation_space, env.action_space)
+  env = gym.make("Walker2DBulletEnv-v0")
+  env.render(mode="human")
+  pi = SmallReactivePolicy(env.observation_space, env.action_space)
 
-    env.reset()
+  env.reset()
 
-    while 1:
-        frame = 0
-        score = 0
-        restart_delay = 0
-        obs = env.reset()
+  while 1:
+    frame = 0
+    score = 0
+    restart_delay = 0
+    obs = env.reset()
 
-        while 1:
-            time.sleep(1./60.)
-            a = pi.act(obs)
-            obs, r, done, _ = env.step(a)
-            score += r
-            frame += 1
+    while 1:
+      time.sleep(1. / 60.)
+      a = pi.act(obs)
+      obs, r, done, _ = env.step(a)
+      score += r
+      frame += 1
 
-            still_open = env.render("human")
-            if still_open==False:
-                return
-            if not done: continue
-            if restart_delay==0:
-                print("score=%0.2f in %i frames" % (score, frame))
-                restart_delay = 60*2  # 2 sec at 60 fps
-            else:
-                restart_delay -= 1
-                if restart_delay==0: break
+      still_open = env.render("human")
+      if still_open == False:
+        return
+      if not done: continue
+      if restart_delay == 0:
+        print("score=%0.2f in %i frames" % (score, frame))
+        restart_delay = 60 * 2  # 2 sec at 60 fps
+      else:
+        restart_delay -= 1
+        if restart_delay == 0: break
 
-weights_dense1_w = np.array([
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-])
 
-weights_dense1_b = np.array([ +0.0945, +0.0221, +0.0891, +0.0491, +0.0117, -0.0476, -0.0928, -0.1916, -0.0470, +0.2310, +0.0341, +0.0667, -0.0911, +0.1326, -0.1565, -0.1634, -0.0695, -0.0235, -0.2292, -0.1396, -0.2046, +0.0967, -0.0569, +0.0423, -0.0863, +0.0138, -0.2055, -0.1253, -0.1153, -0.2407, +0.1039, -0.2110, -0.0743, -0.0020, +0.0149, -0.2198, +0.0150, -0.2298, -0.1317, +0.0990, -0.0897, +0.0430, -0.0985, +0.0082, -0.2348, -0.0832, -0.1534, -0.1548, -0.2317, +0.0414, -0.0891, -0.2233, -0.0865, -0.0298, -0.1339, +0.0339, +0.1492, -0.1300, -0.4555, -0.0238, +0.0225, -0.1978, +0.0416, -0.0089, -0.0476, +0.0720, -0.1214, +0.1386, +0.1151, -0.1887, -0.1368, -0.1987, -0.0415, +0.0236, -0.2552, -0.1052, -0.0978, +0.0162, -0.0876, +0.0537, -0.1436, -0.2076, -0.0873, -0.1789, -0.0524, -0.1163, -0.0832, -0.1528, -0.0416, -0.1003, -0.0970, -0.1769, -0.1527, -0.0028, +0.1150, +0.0467, -0.0537, -0.2437, -0.1143, -0.1589, +0.1136, +0.0611, -0.1218, +0.0358, +0.1195, -0.1023, +0.0665, -0.0816, -0.1308, -0.1215, +0.0528, -0.0858, +0.1087, -0.0314, -0.2404, +0.0233, -0.0644, -0.1083, -0.0198, -0.0531, -0.0808, -0.0256, +0.0324, -0.0287, -0.0536, -0.1525, +0.0821, +0.1315])
+# yapf: disable
+weights_dense1_w = np.array(
+    [[
+        -0.5923, +0.0708, -0.2868, -0.5138, -0.1376, +0.3921, +0.3474, +0.2376,
+        +0.6664, +0.1282, +0.1778, +0.2879, -0.3030, +0.1678, +0.1248, +0.1665,
+        -0.1129, -0.5741, +0.1318, +0.4056, +0.2123, +0.1856, +0.3895, +0.0076,
+        +0.4030, +0.1592, +0.2418, +0.0302, -0.2642, -0.0772, -0.5000, -0.0241,
+        +0.3221, +0.1268, -0.1013, +0.1987, +0.2850, -0.3257, -0.1115, -0.3003,
+        +0.0450, -0.2232, -0.1471, +0.5303, +0.6448, +0.0623, +0.4801, +0.1554,
+        -0.0681, -0.4085, +0.6602, +0.1064, +0.1389, +0.6386, +0.5123, -0.3210,
+        -0.0565, +0.2629, +0.5662, -0.2477, -0.1244, +0.0125, +0.1585, +0.0328,
+        +0.0098, +0.0898, +0.4162, -0.0758, -0.4449, +0.4196, +0.0923, +0.4323,
+        -0.2452, -0.1646, +0.8115, +0.3922, +0.2736, -0.2587, +0.6535, +0.1253,
+        +0.0194, +0.3765, +0.2678, +0.4038, -0.1404, -0.2698, +0.1081, +0.0031,
+        +0.4622, -0.1806, -0.2798, -0.2216, +0.2180, -0.0131, -0.3375, +0.2901,
+        +0.2382, -0.0402, +0.4257, -0.2084, -0.1102, +0.6300, +0.0378, +0.5702,
+        +0.0085, -0.4068, +0.5516, +0.1101, +0.4330, +0.0053, +0.0656, +0.6463,
+        +0.2717, -0.5689, -0.2151, +0.1876, +0.6149, +0.1463, +0.2377, +0.4734,
+        -0.5056, +0.5846, +0.1847, +0.4174, -0.0182, -0.0434, +0.4932, +0.1675
+    ],
+     [
+         -0.0323, +0.1990, +0.0035, +0.1374, -0.0162, +0.1237, -0.2231,
+         +0.3492, -0.0100, +0.1316, -0.4408, -0.3253, +0.0478, +0.1452,
+         +0.1131, +0.0822, +0.3286, +0.0443, -0.1344, -0.0785, -0.4665,
+         -0.1749, -0.2719, +0.1309, +0.2622, -0.1787, +0.0132, +0.0169,
+         +0.1424, -0.1192, -0.0486, +0.0994, +0.2762, -0.2210, -0.3417,
+         -0.0453, +0.2207, +0.1077, -0.2448, +0.1683, +0.0924, +0.1232,
+         -0.1016, +0.2795, +0.3878, -0.3440, -0.0133, -0.1766, -0.2442,
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+weights_dense2_w = np.array(
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-[ -0.0991, +0.4950, +0.0633, +0.2891, -0.5150, +0.1257],
-[ -0.0267, -0.5339, -0.2769, -0.0796, +0.0055, -0.2304],
-[ +0.2994, +0.3536, -0.3556, +0.3602, -0.1715, -0.3168],
-[ +0.1544, -0.5746, -0.1126, -0.4654, +0.1188, -0.0478],
-[ +0.4130, -0.7358, +0.0469, +0.0414, -0.0352, -0.5242],
-[ -0.3941, +0.6807, +0.0670, +0.6347, +0.0394, +0.2293],
-[ -0.0101, +0.1363, +0.6806, -0.1179, +0.1445, -0.0643],
-[ -0.5794, +0.1603, -0.3438, -0.5360, -0.0372, +0.1549],
-[ -0.0897, +1.1163, +0.4080, +0.1319, +0.1437, +0.0088],
-[ +0.2344, +0.4274, -0.1515, +0.0379, -0.2166, -0.0852]
+weights_dense2_b = np.array([
+    +0.3157, -0.0138, +0.1347, +0.2792, -0.0769, +0.0847, -0.0819, +0.2974,
+    +0.1350, -0.0025, +0.0698, -0.2427, +0.2004, +0.2309, +0.1715, +0.0219,
+    +0.1258, +0.1784, +0.3261, +0.1711, -0.0779, +0.1550, +0.1433, +0.0774,
+    -0.1676, -0.0443, -0.1684, +0.1624, +0.0597, +0.0358, +0.1652, +0.1075,
+    +0.0770, +0.1979, +0.1490, -0.0719, +0.1036, +0.0766, +0.2915, -0.1048,
+    +0.1366, +0.0610, +0.2238, +0.1660, +0.0433, +0.3754, -0.0812, -0.0851,
+    +0.2820, +0.2110, +0.0232, -0.0167, -0.2433, -0.0042, +0.2591, -0.0215,
+    +0.0927, +0.1470, -0.0324, +0.2150, -0.1883, +0.0956, +0.1331, +0.2330
 ])
 
-weights_final_b = np.array([ +0.0090, +0.2404, +0.1022, +0.1035, +0.0621, -0.0160])
+weights_final_w = np.array(
+    [[+0.0101, -0.1948, +0.2128, -0.4291, +0.4975, +0.0586],
+     [+0.1211, -0.3286, -0.5959, +0.2402, +0.2022, +0.0405],
+     [-0.0318, +0.3342, +0.1579, -0.4639, -0.4456, -0.2342],
+     [+0.0856, +0.0638, +0.3259, +0.2046, -0.4004, +0.4999],
+     [-0.6462, -0.0369, -1.1351, +0.9906, +0.0352, -0.6781],
+     [-0.3719, -0.1071, +0.3581, +0.5559, +0.1965, -0.3970],
+     [+0.0816, -0.1766, -0.6672, +0.0872, -0.5773, -0.3138],
+     [-0.0914, +0.1054, +0.0728, +0.5491, +0.4499, +0.1875],
+     [+0.2443, +0.0085, -0.2355, +0.3129, -0.1762, -0.0077],
+     [-0.1181, -0.6764, +0.4602, +0.1629, +0.0420, +0.2435],
+     [+0.0605, +0.0850, -0.6874, -0.0096, +0.4599, -0.2271],
+     [+0.5245, -0.2946, -0.1789, +0.5927, +0.0829, +0.5251],
+     [+0.4504, -0.4831, +0.0163, +0.0061, +0.5226, -0.4811],
+     [+0.5086, -0.2366, +0.0043, -0.1165, -0.2790, -0.5078],
+     [+0.1296, +0.0196, +0.1396, -0.1705, +0.2290, -0.2673],
+     [-0.0323, -0.1554, +0.4492, -0.6847, +0.0767, +0.0235],
+     [-0.0142, +0.3304, -0.2269, +0.0771, +0.2548, -0.0215],
+     [-0.3389, +0.0605, -0.1767, +0.1649, +0.3932, +0.4187],
+     [+0.0447, -0.2753, +0.7056, -0.3268, -0.4054, +0.3856],
+     [+0.3863, +0.0820, +0.1836, -0.1025, -0.0837, +0.3728],
+     [+0.1172, +0.2514, -0.2447, -0.0147, +0.3737, -0.1232],
+     [+0.4822, -0.1587, +0.0189, +0.1085, -0.2107, +0.1819],
+     [-0.3112, +0.2384, -0.0983, +0.3085, +0.3343, -0.1824],
+     [+0.1329, -0.7842, +0.5067, -0.1467, +0.6028, -0.1696],
+     [+0.0913, -0.2866, +0.1866, +0.0102, -0.2893, +0.1818],
+     [-0.0842, +0.0903, +0.0537, +0.2681, +0.6005, -0.3763],
+     [+0.4081, -0.5070, -0.2256, +0.4282, -0.2310, +0.0178],
+     [+0.1979, +0.1552, +0.7038, -0.0276, +0.1008, +0.4311],
+     [+0.0105, +0.0500, -0.5326, -0.1960, -0.0029, +0.7599],
+     [+0.0132, +0.1029, +0.0586, +0.2432, -0.0545, -0.3576],
+     [-0.1861, +0.2730, -0.1026, +0.2567, +0.1899, +0.0994],
+     [+0.2804, +0.4976, +0.0234, -0.0693, +0.1044, -0.1782],
+     [+0.1651, -0.0445, -0.1732, -0.1439, +0.1910, -0.2900],
+     [+0.0575, +0.0506, -0.5541, -0.0837, +0.1331, +0.0596],
+     [-0.5366, -0.2095, +0.2158, +0.1478, +0.1552, -0.2847],
+     [-0.2239, -0.5064, +0.2553, +0.6997, -0.2814, -0.5053],
+     [-0.5875, -0.2929, -0.3054, +0.4141, +0.0547, +0.2617],
+     [+0.4712, +0.1022, +0.2665, +0.1481, +0.2837, +0.3440],
+     [+0.3937, +0.3063, +0.4430, -0.5654, +0.0932, -0.1030],
+     [+0.0561, +0.7068, +0.1140, -0.0612, +0.1575, -0.4454],
+     [-0.4082, -0.3756, -0.0447, +0.5955, +0.2170, +0.2459],
+     [+0.3779, +0.1477, -0.2120, -0.3360, -0.3584, +0.1333],
+     [-0.1877, +0.1041, -0.2141, -0.0261, +0.0121, +0.5809],
+     [+0.5229, +0.3220, +0.4367, -0.1934, +0.0125, +0.3250],
+     [-0.0197, -0.0964, +0.6821, +0.0876, +0.1429, -0.2392],
+     [-0.3900, +0.3254, -0.1975, +0.2021, -0.4072, +0.2072],
+     [+0.2660, -0.2046, +0.2172, +0.2520, +0.1637, -0.0179],
+     [-0.0649, -0.0451, +0.0873, +0.0077, -0.3684, -0.0634],
+     [+0.1304, +0.2106, +0.0804, +0.0554, -0.2718, -0.4081],
+     [+0.3119, +0.1979, -0.2498, +0.0328, +0.2566, +0.2054],
+     [+0.0622, +0.2239, -0.7600, -0.2464, -0.3213, -0.4771],
+     [-0.3352, +0.4833, +0.0830, +0.3854, -0.0733, -0.0983],
+     [+0.2465, +0.0722, -0.0776, +0.2516, +0.5810, +0.5918],
+     [-0.2167, -0.2138, +0.2410, -0.3535, -0.4878, -0.4168],
+     [-0.0991, +0.4950, +0.0633, +0.2891, -0.5150, +0.1257],
+     [-0.0267, -0.5339, -0.2769, -0.0796, +0.0055, -0.2304],
+     [+0.2994, +0.3536, -0.3556, +0.3602, -0.1715, -0.3168],
+     [+0.1544, -0.5746, -0.1126, -0.4654, +0.1188, -0.0478],
+     [+0.4130, -0.7358, +0.0469, +0.0414, -0.0352, -0.5242],
+     [-0.3941, +0.6807, +0.0670, +0.6347, +0.0394, +0.2293],
+     [-0.0101, +0.1363, +0.6806, -0.1179, +0.1445, -0.0643],
+     [-0.5794, +0.1603, -0.3438, -0.5360, -0.0372, +0.1549],
+     [-0.0897, +1.1163, +0.4080, +0.1319, +0.1437, +0.0088],
+     [+0.2344, +0.4274, -0.1515, +0.0379, -0.2166, -0.0852]])
+
+weights_final_b = np.array(
+    [+0.0090, +0.2404, +0.1022, +0.1035, +0.0621, -0.0160])
+# yapf: enable
 
-if __name__=="__main__":
-    main()
+if __name__ == "__main__":
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/kukaCamGymEnvTest.py b/examples/pybullet/gym/pybullet_envs/examples/kukaCamGymEnvTest.py
index 00fe0d6b3..6cf9909cb 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/kukaCamGymEnvTest.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/kukaCamGymEnvTest.py
@@ -2,39 +2,40 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 from pybullet_envs.bullet.kukaCamGymEnv import KukaCamGymEnv
 import time
 
+
 def main():
 
-	environment = KukaCamGymEnv(renders=True,isDiscrete=False)
-	
-	  
-	motorsIds=[]
-	#motorsIds.append(environment._p.addUserDebugParameter("posX",0.4,0.75,0.537))
-	#motorsIds.append(environment._p.addUserDebugParameter("posY",-.22,.3,0.0))
-	#motorsIds.append(environment._p.addUserDebugParameter("posZ",0.1,1,0.2))
-	#motorsIds.append(environment._p.addUserDebugParameter("yaw",-3.14,3.14,0))
-	#motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3))
-	
-	dv = 1 
-	motorsIds.append(environment._p.addUserDebugParameter("posX",-dv,dv,0))
-	motorsIds.append(environment._p.addUserDebugParameter("posY",-dv,dv,0))
-	motorsIds.append(environment._p.addUserDebugParameter("posZ",-dv,dv,0))
-	motorsIds.append(environment._p.addUserDebugParameter("yaw",-dv,dv,0))
-	motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3))
-	
-	done = False
-	while (not done):
-	    
-	  action=[]
-	  for motorId in motorsIds:
-	    action.append(environment._p.readUserDebugParameter(motorId))
-	  
-	  state, reward, done, info = environment.step(action)
-	  obs = environment.getExtendedObservation()
-	  
-if __name__=="__main__":
-    main()
+  environment = KukaCamGymEnv(renders=True, isDiscrete=False)
+
+  motorsIds = []
+  #motorsIds.append(environment._p.addUserDebugParameter("posX",0.4,0.75,0.537))
+  #motorsIds.append(environment._p.addUserDebugParameter("posY",-.22,.3,0.0))
+  #motorsIds.append(environment._p.addUserDebugParameter("posZ",0.1,1,0.2))
+  #motorsIds.append(environment._p.addUserDebugParameter("yaw",-3.14,3.14,0))
+  #motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3))
+
+  dv = 1
+  motorsIds.append(environment._p.addUserDebugParameter("posX", -dv, dv, 0))
+  motorsIds.append(environment._p.addUserDebugParameter("posY", -dv, dv, 0))
+  motorsIds.append(environment._p.addUserDebugParameter("posZ", -dv, dv, 0))
+  motorsIds.append(environment._p.addUserDebugParameter("yaw", -dv, dv, 0))
+  motorsIds.append(environment._p.addUserDebugParameter("fingerAngle", 0, 0.3, .3))
+
+  done = False
+  while (not done):
+
+    action = []
+    for motorId in motorsIds:
+      action.append(environment._p.readUserDebugParameter(motorId))
+
+    state, reward, done, info = environment.step(action)
+    obs = environment.getExtendedObservation()
+
+
+if __name__ == "__main__":
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest.py b/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest.py
index 3917f08cf..818bdd0ab 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest.py
@@ -2,39 +2,40 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 from pybullet_envs.bullet.kukaGymEnv import KukaGymEnv
 import time
 
+
 def main():
 
-	environment = KukaGymEnv(renders=True,isDiscrete=False, maxSteps = 10000000)
-	
-	  
-	motorsIds=[]
-	#motorsIds.append(environment._p.addUserDebugParameter("posX",0.4,0.75,0.537))
-	#motorsIds.append(environment._p.addUserDebugParameter("posY",-.22,.3,0.0))
-	#motorsIds.append(environment._p.addUserDebugParameter("posZ",0.1,1,0.2))
-	#motorsIds.append(environment._p.addUserDebugParameter("yaw",-3.14,3.14,0))
-	#motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3))
-	
-	dv = 0.01 
-	motorsIds.append(environment._p.addUserDebugParameter("posX",-dv,dv,0))
-	motorsIds.append(environment._p.addUserDebugParameter("posY",-dv,dv,0))
-	motorsIds.append(environment._p.addUserDebugParameter("posZ",-dv,dv,0))
-	motorsIds.append(environment._p.addUserDebugParameter("yaw",-dv,dv,0))
-	motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3))
-	
-	done = False
-	while (not done):
-	    
-	  action=[]
-	  for motorId in motorsIds:
-	    action.append(environment._p.readUserDebugParameter(motorId))
-	  
-	  state, reward, done, info = environment.step2(action)
-	  obs = environment.getExtendedObservation()
-	  
-if __name__=="__main__":
-    main()
+  environment = KukaGymEnv(renders=True, isDiscrete=False, maxSteps=10000000)
+
+  motorsIds = []
+  #motorsIds.append(environment._p.addUserDebugParameter("posX",0.4,0.75,0.537))
+  #motorsIds.append(environment._p.addUserDebugParameter("posY",-.22,.3,0.0))
+  #motorsIds.append(environment._p.addUserDebugParameter("posZ",0.1,1,0.2))
+  #motorsIds.append(environment._p.addUserDebugParameter("yaw",-3.14,3.14,0))
+  #motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3))
+
+  dv = 0.01
+  motorsIds.append(environment._p.addUserDebugParameter("posX", -dv, dv, 0))
+  motorsIds.append(environment._p.addUserDebugParameter("posY", -dv, dv, 0))
+  motorsIds.append(environment._p.addUserDebugParameter("posZ", -dv, dv, 0))
+  motorsIds.append(environment._p.addUserDebugParameter("yaw", -dv, dv, 0))
+  motorsIds.append(environment._p.addUserDebugParameter("fingerAngle", 0, 0.3, .3))
+
+  done = False
+  while (not done):
+
+    action = []
+    for motorId in motorsIds:
+      action.append(environment._p.readUserDebugParameter(motorId))
+
+    state, reward, done, info = environment.step2(action)
+    obs = environment.getExtendedObservation()
+
+
+if __name__ == "__main__":
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest2.py b/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest2.py
index 5c7f245c3..d94702cf0 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest2.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/kukaGymEnvTest2.py
@@ -2,37 +2,38 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 from pybullet_envs.bullet.kukaGymEnv import KukaGymEnv
 import time
 
+
 def main():
 
-	environment = KukaGymEnv(renders=True,isDiscrete=False, maxSteps = 10000000)
-	
-	  
-	motorsIds=[]
-	#motorsIds.append(environment._p.addUserDebugParameter("posX",-1,1,0))
-	#motorsIds.append(environment._p.addUserDebugParameter("posY",-.22,.3,0.0))
-	#motorsIds.append(environment._p.addUserDebugParameter("posZ",0.1,1,0.2))
-	#motorsIds.append(environment._p.addUserDebugParameter("yaw",-3.14,3.14,0))
-	#motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3))
-	
-	dv = 1 
-	motorsIds.append(environment._p.addUserDebugParameter("posX",-dv,dv,0))
-	motorsIds.append(environment._p.addUserDebugParameter("posY",-dv,dv,0))
-	motorsIds.append(environment._p.addUserDebugParameter("yaw",-dv,dv,0))
-	
-	done = False
-	while (not done):
-	    
-	  action=[]
-	  for motorId in motorsIds:
-	    action.append(environment._p.readUserDebugParameter(motorId))
-	  
-	  state, reward, done, info = environment.step(action)
-	  obs = environment.getExtendedObservation()
-	  
-if __name__=="__main__":
-    main()
+  environment = KukaGymEnv(renders=True, isDiscrete=False, maxSteps=10000000)
+
+  motorsIds = []
+  #motorsIds.append(environment._p.addUserDebugParameter("posX",-1,1,0))
+  #motorsIds.append(environment._p.addUserDebugParameter("posY",-.22,.3,0.0))
+  #motorsIds.append(environment._p.addUserDebugParameter("posZ",0.1,1,0.2))
+  #motorsIds.append(environment._p.addUserDebugParameter("yaw",-3.14,3.14,0))
+  #motorsIds.append(environment._p.addUserDebugParameter("fingerAngle",0,0.3,.3))
+
+  dv = 1
+  motorsIds.append(environment._p.addUserDebugParameter("posX", -dv, dv, 0))
+  motorsIds.append(environment._p.addUserDebugParameter("posY", -dv, dv, 0))
+  motorsIds.append(environment._p.addUserDebugParameter("yaw", -dv, dv, 0))
+
+  done = False
+  while (not done):
+
+    action = []
+    for motorId in motorsIds:
+      action.append(environment._p.readUserDebugParameter(motorId))
+
+    state, reward, done, info = environment.step(action)
+    obs = environment.getExtendedObservation()
+
+
+if __name__ == "__main__":
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/kuka_setup.py b/examples/pybullet/gym/pybullet_envs/examples/kuka_setup.py
index 38d32b844..1fcda0832 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/kuka_setup.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/kuka_setup.py
@@ -5,88 +5,165 @@ import pybullet_data
 
 #cid = p.connect(p.UDP,"192.168.86.100")
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	p.connect(p.GUI)
+if (cid < 0):
+  p.connect(p.GUI)
 p.setAdditionalSearchPath(pybullet_data.getDataPath())
 p.resetSimulation()
 
-objects = [p.loadURDF("plane.urdf", 0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("samurai.urdf", 0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("pr2_gripper.urdf", 0.500000,0.300006,0.700000,-0.000000,-0.000000,-0.000031,1.000000)]
+objects = [
+    p.loadURDF("plane.urdf", 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000)
+]
+objects = [
+    p.loadURDF("samurai.urdf", 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("pr2_gripper.urdf", 0.500000, 0.300006, 0.700000, -0.000000, -0.000000, -0.000031,
+               1.000000)
+]
 pr2_gripper = objects[0]
-print ("pr2_gripper=")
-print (pr2_gripper)
-
-jointPositions=[ 0.550569, 0.000000, 0.549657, 0.000000 ]
-for jointIndex in range (p.getNumJoints(pr2_gripper)):
-	p.resetJointState(pr2_gripper,jointIndex,jointPositions[jointIndex])
-
-pr2_cid = p.createConstraint(pr2_gripper,-1,-1,-1,p.JOINT_FIXED,[0,0,0],[0.2,0,0],[0.500000,0.300006,0.700000])
-print ("pr2_cid")
-print (pr2_cid)
-
-objects = [p.loadURDF("kuka_iiwa/model_vr_limits.urdf", 1.400000,-0.200000,0.600000,0.000000,0.000000,0.000000,1.000000)]
+print("pr2_gripper=")
+print(pr2_gripper)
+
+jointPositions = [0.550569, 0.000000, 0.549657, 0.000000]
+for jointIndex in range(p.getNumJoints(pr2_gripper)):
+  p.resetJointState(pr2_gripper, jointIndex, jointPositions[jointIndex])
+
+pr2_cid = p.createConstraint(pr2_gripper, -1, -1, -1, p.JOINT_FIXED, [0, 0, 0], [0.2, 0, 0],
+                             [0.500000, 0.300006, 0.700000])
+print("pr2_cid")
+print(pr2_cid)
+
+objects = [
+    p.loadURDF("kuka_iiwa/model_vr_limits.urdf", 1.400000, -0.200000, 0.600000, 0.000000, 0.000000,
+               0.000000, 1.000000)
+]
 kuka = objects[0]
-jointPositions=[ -0.000000, -0.000000, 0.000000, 1.570793, 0.000000, -1.036725, 0.000001 ]
-for jointIndex in range (p.getNumJoints(kuka)):
-	p.resetJointState(kuka,jointIndex,jointPositions[jointIndex])
-	p.setJointMotorControl2(kuka,jointIndex,p.POSITION_CONTROL,jointPositions[jointIndex],0)
-
-objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.700000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.800000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("lego/lego.urdf", 1.000000,-0.200000,0.900000,0.000000,0.000000,0.000000,1.000000)]
+jointPositions = [-0.000000, -0.000000, 0.000000, 1.570793, 0.000000, -1.036725, 0.000001]
+for jointIndex in range(p.getNumJoints(kuka)):
+  p.resetJointState(kuka, jointIndex, jointPositions[jointIndex])
+  p.setJointMotorControl2(kuka, jointIndex, p.POSITION_CONTROL, jointPositions[jointIndex], 0)
+
+objects = [
+    p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.700000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.800000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("lego/lego.urdf", 1.000000, -0.200000, 0.900000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
 objects = p.loadSDF("gripper/wsg50_one_motor_gripper_new_free_base.sdf")
 kuka_gripper = objects[0]
-print ("kuka gripper=")
+print("kuka gripper=")
 print(kuka_gripper)
 
-p.resetBasePositionAndOrientation(kuka_gripper,[0.923103,-0.200000,1.250036],[-0.000000,0.964531,-0.000002,-0.263970])
-jointPositions=[ 0.000000, -0.011130, -0.206421, 0.205143, -0.009999, 0.000000, -0.010055, 0.000000 ]
-for jointIndex in range (p.getNumJoints(kuka_gripper)):
-	p.resetJointState(kuka_gripper,jointIndex,jointPositions[jointIndex])
-	p.setJointMotorControl2(kuka_gripper,jointIndex,p.POSITION_CONTROL,jointPositions[jointIndex],0)
-
-
-kuka_cid = p.createConstraint(kuka,   6,  kuka_gripper,0,p.JOINT_FIXED, [0,0,0], [0,0,0.05],[0,0,0])
-
-objects = [p.loadURDF("jenga/jenga.urdf", 1.300000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.200000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.100000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 1.000000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 0.900000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("jenga/jenga.urdf", 0.800000,-0.700000,0.750000,0.000000,0.707107,0.000000,0.707107)]
-objects = [p.loadURDF("table/table.urdf", 1.000000,-0.200000,0.000000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("teddy_vhacd.urdf", 1.050000,-0.500000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("cube_small.urdf", 0.950000,-0.100000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("sphere_small.urdf", 0.850000,-0.400000,0.700000,0.000000,0.000000,0.707107,0.707107)]
-objects = [p.loadURDF("duck_vhacd.urdf", 0.850000,-0.400000,0.900000,0.000000,0.000000,0.707107,0.707107)]
+p.resetBasePositionAndOrientation(kuka_gripper, [0.923103, -0.200000, 1.250036],
+                                  [-0.000000, 0.964531, -0.000002, -0.263970])
+jointPositions = [
+    0.000000, -0.011130, -0.206421, 0.205143, -0.009999, 0.000000, -0.010055, 0.000000
+]
+for jointIndex in range(p.getNumJoints(kuka_gripper)):
+  p.resetJointState(kuka_gripper, jointIndex, jointPositions[jointIndex])
+  p.setJointMotorControl2(kuka_gripper, jointIndex, p.POSITION_CONTROL, jointPositions[jointIndex],
+                          0)
+
+kuka_cid = p.createConstraint(kuka, 6, kuka_gripper, 0, p.JOINT_FIXED, [0, 0, 0], [0, 0, 0.05],
+                              [0, 0, 0])
+
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.300000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.200000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.100000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 1.000000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 0.900000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("jenga/jenga.urdf", 0.800000, -0.700000, 0.750000, 0.000000, 0.707107, 0.000000,
+               0.707107)
+]
+objects = [
+    p.loadURDF("table/table.urdf", 1.000000, -0.200000, 0.000000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("teddy_vhacd.urdf", 1.050000, -0.500000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("cube_small.urdf", 0.950000, -0.100000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("sphere_small.urdf", 0.850000, -0.400000, 0.700000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
+objects = [
+    p.loadURDF("duck_vhacd.urdf", 0.850000, -0.400000, 0.900000, 0.000000, 0.000000, 0.707107,
+               0.707107)
+]
 objects = p.loadSDF("kiva_shelf/model.sdf")
 ob = objects[0]
-p.resetBasePositionAndOrientation(ob,[0.000000,1.000000,1.204500],[0.000000,0.000000,0.000000,1.000000])
-objects = [p.loadURDF("teddy_vhacd.urdf", -0.100000,0.600000,0.850000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("sphere_small.urdf", -0.100000,0.955006,1.169706,0.633232,-0.000000,-0.000000,0.773962)]
-objects = [p.loadURDF("cube_small.urdf", 0.300000,0.600000,0.850000,0.000000,0.000000,0.000000,1.000000)]
-objects = [p.loadURDF("table_square/table_square.urdf", -1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000)]
+p.resetBasePositionAndOrientation(ob, [0.000000, 1.000000, 1.204500],
+                                  [0.000000, 0.000000, 0.000000, 1.000000])
+objects = [
+    p.loadURDF("teddy_vhacd.urdf", -0.100000, 0.600000, 0.850000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("sphere_small.urdf", -0.100000, 0.955006, 1.169706, 0.633232, -0.000000, -0.000000,
+               0.773962)
+]
+objects = [
+    p.loadURDF("cube_small.urdf", 0.300000, 0.600000, 0.850000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
+objects = [
+    p.loadURDF("table_square/table_square.urdf", -1.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+               0.000000, 1.000000)
+]
 ob = objects[0]
-jointPositions=[ 0.000000 ]
-for jointIndex in range (p.getNumJoints(ob)):
-	p.resetJointState(ob,jointIndex,jointPositions[jointIndex])
-
-objects = [p.loadURDF("husky/husky.urdf", 2.000000,-5.000000,1.000000,0.000000,0.000000,0.000000,1.000000)]
+jointPositions = [0.000000]
+for jointIndex in range(p.getNumJoints(ob)):
+  p.resetJointState(ob, jointIndex, jointPositions[jointIndex])
+
+objects = [
+    p.loadURDF("husky/husky.urdf", 2.000000, -5.000000, 1.000000, 0.000000, 0.000000, 0.000000,
+               1.000000)
+]
 ob = objects[0]
-jointPositions=[ 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000 ]
-for jointIndex in range (p.getNumJoints(ob)):
-	p.resetJointState(ob,jointIndex,jointPositions[jointIndex])
+jointPositions = [
+    0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+    0.000000
+]
+for jointIndex in range(p.getNumJoints(ob)):
+  p.resetJointState(ob, jointIndex, jointPositions[jointIndex])
 
-p.setGravity(0.000000,0.000000,0.000000)
-p.setGravity(0,0,-10)
+p.setGravity(0.000000, 0.000000, 0.000000)
+p.setGravity(0, 0, -10)
 
 p.setRealTimeSimulation(1)
 ref_time = time.time()
 
-running_time = 60 # seconds
-while (time.time() < ref_time+running_time):
-	p.setGravity(0,0,-10)
-	#p.stepSimulation()
+running_time = 60  # seconds
+while (time.time() < ref_time + running_time):
+  p.setGravity(0, 0, -10)
+  #p.stepSimulation()
 
 p.disconnect()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/laikago.py b/examples/pybullet/gym/pybullet_envs/examples/laikago.py
index b256f2a04..081573cc0 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/laikago.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/laikago.py
@@ -7,58 +7,61 @@ p.connect(p.GUI)
 p.setAdditionalSearchPath(pd.getDataPath())

 

 plane = p.loadURDF("plane.urdf")

-p.setGravity(0,0,-9.8)

-p.setTimeStep(1./500)

+p.setGravity(0, 0, -9.8)

+p.setTimeStep(1. / 500)

 #p.setDefaultContactERP(0)

 #urdfFlags = p.URDF_USE_SELF_COLLISION+p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS

 urdfFlags = p.URDF_USE_SELF_COLLISION

-quadruped = p.loadURDF("laikago/laikago.urdf",[0,0,.5],[0,0.5,0.5,0], flags = urdfFlags,useFixedBase=False)

+quadruped = p.loadURDF("laikago/laikago.urdf", [0, 0, .5], [0, 0.5, 0.5, 0],

+                       flags=urdfFlags,

+                       useFixedBase=False)

 

 #enable collision between lower legs

 

-for j in range (p.getNumJoints(quadruped)):

-    print(p.getJointInfo(quadruped,j))

+for j in range(p.getNumJoints(quadruped)):

+  print(p.getJointInfo(quadruped, j))

 

 #2,5,8 and 11 are the lower legs

-lower_legs = [2,5,8,11]

+lower_legs = [2, 5, 8, 11]

 for l0 in lower_legs:

   for l1 in lower_legs:

-    if (l1>l0):

+    if (l1 > l0):

       enableCollision = 1

-      print("collision for pair",l0,l1, p.getJointInfo(quadruped,l0)[12],p.getJointInfo(quadruped,l1)[12], "enabled=",enableCollision)

-      p.setCollisionFilterPair(quadruped, quadruped, 2,5,enableCollision)

-

-jointIds=[]

-paramIds=[]

-jointOffsets=[]

-jointDirections=[-1,1,1,1,1,1,-1,1,1,1,1,1]

-jointAngles=[0,0,0,0,0,0,0,0,0,0,0,0]

-

-for i in range (4):

+      print("collision for pair", l0, l1,

+            p.getJointInfo(quadruped, l0)[12],

+            p.getJointInfo(quadruped, l1)[12], "enabled=", enableCollision)

+      p.setCollisionFilterPair(quadruped, quadruped, 2, 5, enableCollision)

+

+jointIds = []

+paramIds = []

+jointOffsets = []

+jointDirections = [-1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1]

+jointAngles = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

+

+for i in range(4):

   jointOffsets.append(0)

   jointOffsets.append(-0.7)

   jointOffsets.append(0.7)

 

-maxForceId = p.addUserDebugParameter("maxForce",0,100,20)

+maxForceId = p.addUserDebugParameter("maxForce", 0, 100, 20)

 

-for j in range (p.getNumJoints(quadruped)):

-        p.changeDynamics(quadruped,j,linearDamping=0, angularDamping=0)

-        info = p.getJointInfo(quadruped,j)

-        #print(info)

-        jointName = info[1]

-        jointType = info[2]

-        if (jointType==p.JOINT_PRISMATIC or jointType==p.JOINT_REVOLUTE):

-                jointIds.append(j)

+for j in range(p.getNumJoints(quadruped)):

+  p.changeDynamics(quadruped, j, linearDamping=0, angularDamping=0)

+  info = p.getJointInfo(quadruped, j)

+  #print(info)

+  jointName = info[1]

+  jointType = info[2]

+  if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE):

+    jointIds.append(j)

 

-

-p.getCameraImage(480,320)

+p.getCameraImage(480, 320)

 p.setRealTimeSimulation(0)

 

-joints=[]

+joints = []

 

-with open(pd.getDataPath()+"/laikago/data1.txt","r") as filestream:

+with open(pd.getDataPath() + "/laikago/data1.txt", "r") as filestream:

   for line in filestream:

-    print("line=",line)

+    print("line=", line)

     maxForce = p.readUserDebugParameter(maxForceId)

     currentline = line.split(",")

     #print (currentline)

@@ -66,31 +69,35 @@ with open(pd.getDataPath()+"/laikago/data1.txt","r") as filestream:
     frame = currentline[0]

     t = currentline[1]

     #print("frame[",frame,"]")

-    joints=currentline[2:14]

+    joints = currentline[2:14]

     #print("joints=",joints)

-    for j in range (12):

+    for j in range(12):

       targetPos = float(joints[j])

-      p.setJointMotorControl2(quadruped,jointIds[j],p.POSITION_CONTROL,jointDirections[j]*targetPos+jointOffsets[j], force=maxForce)

+      p.setJointMotorControl2(quadruped,

+                              jointIds[j],

+                              p.POSITION_CONTROL,

+                              jointDirections[j] * targetPos + jointOffsets[j],

+                              force=maxForce)

     p.stepSimulation()

     for lower_leg in lower_legs:

       #print("points for ", quadruped, " link: ", lower_leg)

-      pts = p.getContactPoints(quadruped,-1, lower_leg)

+      pts = p.getContactPoints(quadruped, -1, lower_leg)

       #print("num points=",len(pts))

       #for pt in pts:

       # print(pt[9])

-    time.sleep(1./500.)

-

-

-for j in range (p.getNumJoints(quadruped)):

-        p.changeDynamics(quadruped,j,linearDamping=0, angularDamping=0)

-        info = p.getJointInfo(quadruped,j)

-        js = p.getJointState(quadruped,j)

-        #print(info)

-        jointName = info[1]

-        jointType = info[2]

-        if (jointType==p.JOINT_PRISMATIC or jointType==p.JOINT_REVOLUTE):

-                paramIds.append(p.addUserDebugParameter(jointName.decode("utf-8"),-4,4,(js[0]-jointOffsets[j])/jointDirections[j]))

-

+    time.sleep(1. / 500.)

+

+for j in range(p.getNumJoints(quadruped)):

+  p.changeDynamics(quadruped, j, linearDamping=0, angularDamping=0)

+  info = p.getJointInfo(quadruped, j)

+  js = p.getJointState(quadruped, j)

+  #print(info)

+  jointName = info[1]

+  jointType = info[2]

+  if (jointType == p.JOINT_PRISMATIC or jointType == p.JOINT_REVOLUTE):

+    paramIds.append(

+        p.addUserDebugParameter(jointName.decode("utf-8"), -4, 4,

+                                (js[0] - jointOffsets[j]) / jointDirections[j]))

 

 p.setRealTimeSimulation(1)

 

@@ -100,5 +107,8 @@ while (1):
     c = paramIds[i]

     targetPos = p.readUserDebugParameter(c)

     maxForce = p.readUserDebugParameter(maxForceId)

-    p.setJointMotorControl2(quadruped,jointIds[i],p.POSITION_CONTROL,jointDirections[i]*targetPos+jointOffsets[i], force=maxForce)

-

+    p.setJointMotorControl2(quadruped,

+                            jointIds[i],

+                            p.POSITION_CONTROL,

+                            jointDirections[i] * targetPos + jointOffsets[i],

+                            force=maxForce)

diff --git a/examples/pybullet/gym/pybullet_envs/examples/minitaur_gym_env_example.py b/examples/pybullet/gym/pybullet_envs/examples/minitaur_gym_env_example.py
index 8057b2dc5..06337da40 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/minitaur_gym_env_example.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/minitaur_gym_env_example.py
@@ -5,7 +5,7 @@ import os
 import inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import math
 import numpy as np
@@ -13,19 +13,19 @@ from pybullet_envs.bullet import minitaur_gym_env
 import argparse
 from pybullet_envs.bullet import minitaur_env_randomizer
 
+
 def ResetPoseExample():
   """An example that the minitaur stands still using the reset pose."""
   steps = 1000
   randomizer = (minitaur_env_randomizer.MinitaurEnvRandomizer())
-  environment = minitaur_gym_env.MinitaurBulletEnv(
-      render=True,
-      leg_model_enabled=False,
-      motor_velocity_limit=np.inf,
-      pd_control_enabled=True,
-      accurate_motor_model_enabled=True,
-      motor_overheat_protection=True,
-      env_randomizer =  randomizer,
-      hard_reset=False)
+  environment = minitaur_gym_env.MinitaurBulletEnv(render=True,
+                                                   leg_model_enabled=False,
+                                                   motor_velocity_limit=np.inf,
+                                                   pd_control_enabled=True,
+                                                   accurate_motor_model_enabled=True,
+                                                   motor_overheat_protection=True,
+                                                   env_randomizer=randomizer,
+                                                   hard_reset=False)
   action = [math.pi / 2] * 8
   for _ in range(steps):
     _, _, done, _ = environment.step(action)
@@ -33,6 +33,7 @@ def ResetPoseExample():
       break
   environment.reset()
 
+
 def MotorOverheatExample():
   """An example of minitaur motor overheat protection is triggered.
 
@@ -40,15 +41,14 @@ def MotorOverheatExample():
   torques. The overheat protection will be triggered in ~1 sec.
   """
 
-  environment = minitaur_gym_env.MinitaurBulletEnv(
-      render=True,
-      leg_model_enabled=False,
-      motor_velocity_limit=np.inf,
-      motor_overheat_protection=True,
-      accurate_motor_model_enabled=True,
-      motor_kp=1.20,
-      motor_kd=0.00,
-      on_rack=False)
+  environment = minitaur_gym_env.MinitaurBulletEnv(render=True,
+                                                   leg_model_enabled=False,
+                                                   motor_velocity_limit=np.inf,
+                                                   motor_overheat_protection=True,
+                                                   accurate_motor_model_enabled=True,
+                                                   motor_kp=1.20,
+                                                   motor_kd=0.00,
+                                                   on_rack=False)
 
   action = [.0] * 8
   for i in range(8):
@@ -68,6 +68,7 @@ def MotorOverheatExample():
     actions_and_observations.append(current_row)
   environment.reset()
 
+
 def SineStandExample():
   """An example of minitaur standing and squatting on the floor.
 
@@ -75,15 +76,14 @@ def SineStandExample():
   periodically in both simulation and experiment. We compare the measured motor
   trajectories, torques and gains.
   """
-  environment = minitaur_gym_env.MinitaurBulletEnv(
-      render=True,
-      leg_model_enabled=False,
-      motor_velocity_limit=np.inf,
-      motor_overheat_protection=True,
-      accurate_motor_model_enabled=True,
-      motor_kp=1.20,
-      motor_kd=0.02,
-      on_rack=False)
+  environment = minitaur_gym_env.MinitaurBulletEnv(render=True,
+                                                   leg_model_enabled=False,
+                                                   motor_velocity_limit=np.inf,
+                                                   motor_overheat_protection=True,
+                                                   accurate_motor_model_enabled=True,
+                                                   motor_kp=1.20,
+                                                   motor_kd=0.02,
+                                                   on_rack=False)
   steps = 1000
   amplitude = 0.5
   speed = 3
@@ -102,20 +102,19 @@ def SineStandExample():
     observation, _, _, _ = environment.step(action)
     current_row.extend(observation.tolist())
     actions_and_observations.append(current_row)
-  
+
   environment.reset()
 
 
 def SinePolicyExample():
   """An example of minitaur walking with a sine gait."""
   randomizer = (minitaur_env_randomizer.MinitaurEnvRandomizer())
-  environment = minitaur_gym_env.MinitaurBulletEnv(
-      render=True,
-      motor_velocity_limit=np.inf,
-      pd_control_enabled=True,
-      hard_reset=False,
-      env_randomizer =  randomizer,
-      on_rack=False)
+  environment = minitaur_gym_env.MinitaurBulletEnv(render=True,
+                                                   motor_velocity_limit=np.inf,
+                                                   pd_control_enabled=True,
+                                                   hard_reset=False,
+                                                   env_randomizer=randomizer,
+                                                   on_rack=False)
   sum_reward = 0
   steps = 20000
   amplitude_1_bound = 0.1
@@ -150,21 +149,23 @@ def SinePolicyExample():
 
 
 def main():
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--env', help='environment ID (0==sine, 1==stand, 2=reset, 3=overheat)',type=int,  default=0)
-    args = parser.parse_args()
-    print("--env=" + str(args.env))
-      
-    if (args.env == 0):
-      SinePolicyExample()
-    if (args.env == 1):
-      SineStandExample()
-    if (args.env == 2):
-      ResetPoseExample()
-    if (args.env == 3):
-      MotorOverheatExample()
-
-if __name__ == '__main__':
-    main()
+  parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument('--env',
+                      help='environment ID (0==sine, 1==stand, 2=reset, 3=overheat)',
+                      type=int,
+                      default=0)
+  args = parser.parse_args()
+  print("--env=" + str(args.env))
+
+  if (args.env == 0):
+    SinePolicyExample()
+  if (args.env == 1):
+    SineStandExample()
+  if (args.env == 2):
+    ResetPoseExample()
+  if (args.env == 3):
+    MotorOverheatExample()
 
 
+if __name__ == '__main__':
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/racecarGymEnvTest.py b/examples/pybullet/gym/pybullet_envs/examples/racecarGymEnvTest.py
index d640951b0..3e4488e30 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/racecarGymEnvTest.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/racecarGymEnvTest.py
@@ -2,43 +2,45 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 from pybullet_envs.bullet.racecarGymEnv import RacecarGymEnv
 isDiscrete = False
 
+
 def main():
-	
-	environment = RacecarGymEnv(renders=True, isDiscrete=isDiscrete)
-	environment.reset()
-	
-	targetVelocitySlider = environment._p.addUserDebugParameter("wheelVelocity",-1,1,0)
-	steeringSlider = environment._p.addUserDebugParameter("steering",-1,1,0)
-	
-	while (True):
-	  targetVelocity = environment._p.readUserDebugParameter(targetVelocitySlider)
-	  steeringAngle = environment._p.readUserDebugParameter(steeringSlider)
-	  if (isDiscrete):
-	    discreteAction = 0
-	    if (targetVelocity<-0.33):
-	      discreteAction=0
-	    else:
-	      if (targetVelocity>0.33):
-	        discreteAction=6
-	      else:
-	        discreteAction=3
-	    if (steeringAngle>-0.17):
-	      if (steeringAngle>0.17):
-	        discreteAction=discreteAction+2
-	      else:
-	        discreteAction=discreteAction+1
-	    action=discreteAction
-	  else:
-	    action=[targetVelocity,steeringAngle]
-	  state, reward, done, info = environment.step(action)
-	  obs = environment.getExtendedObservation()
-	  print("obs")
-	  print(obs)
-
-if __name__=="__main__":
-    main()
+
+  environment = RacecarGymEnv(renders=True, isDiscrete=isDiscrete)
+  environment.reset()
+
+  targetVelocitySlider = environment._p.addUserDebugParameter("wheelVelocity", -1, 1, 0)
+  steeringSlider = environment._p.addUserDebugParameter("steering", -1, 1, 0)
+
+  while (True):
+    targetVelocity = environment._p.readUserDebugParameter(targetVelocitySlider)
+    steeringAngle = environment._p.readUserDebugParameter(steeringSlider)
+    if (isDiscrete):
+      discreteAction = 0
+      if (targetVelocity < -0.33):
+        discreteAction = 0
+      else:
+        if (targetVelocity > 0.33):
+          discreteAction = 6
+        else:
+          discreteAction = 3
+      if (steeringAngle > -0.17):
+        if (steeringAngle > 0.17):
+          discreteAction = discreteAction + 2
+        else:
+          discreteAction = discreteAction + 1
+      action = discreteAction
+    else:
+      action = [targetVelocity, steeringAngle]
+    state, reward, done, info = environment.step(action)
+    obs = environment.getExtendedObservation()
+    print("obs")
+    print(obs)
+
+
+if __name__ == "__main__":
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/racecarZEDGymEnvTest.py b/examples/pybullet/gym/pybullet_envs/examples/racecarZEDGymEnvTest.py
index cee459e70..4d529c476 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/racecarZEDGymEnvTest.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/racecarZEDGymEnvTest.py
@@ -2,43 +2,45 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 isDiscrete = False
 
 from pybullet_envs.bullet.racecarZEDGymEnv import RacecarZEDGymEnv
 
+
 def main():
-	
-	environment = RacecarZEDGymEnv(renders=True, isDiscrete=isDiscrete)
-	
-	targetVelocitySlider = environment._p.addUserDebugParameter("wheelVelocity",-1,1,0)
-	steeringSlider = environment._p.addUserDebugParameter("steering",-1,1,0)
-	
-	while (True):
-	  targetVelocity = environment._p.readUserDebugParameter(targetVelocitySlider)
-	  steeringAngle = environment._p.readUserDebugParameter(steeringSlider)
-	  if (isDiscrete):
-	    discreteAction = 0
-	    if (targetVelocity<-0.33):
-	      discreteAction=0
-	    else:
-	      if (targetVelocity>0.33):
-	        discreteAction=6
-	      else:
-	        discreteAction=3
-	    if (steeringAngle>-0.17):
-	      if (steeringAngle>0.17):
-	        discreteAction=discreteAction+2
-	      else:
-	        discreteAction=discreteAction+1
-	    action=discreteAction
-	  else:
-	    action=[targetVelocity,steeringAngle]
-
-	  state, reward, done, info = environment.step(action)
-	  obs = environment.getExtendedObservation()
-	  #print("obs")
-	  #print(obs)
-
-if __name__=="__main__":
-    main()
+
+  environment = RacecarZEDGymEnv(renders=True, isDiscrete=isDiscrete)
+
+  targetVelocitySlider = environment._p.addUserDebugParameter("wheelVelocity", -1, 1, 0)
+  steeringSlider = environment._p.addUserDebugParameter("steering", -1, 1, 0)
+
+  while (True):
+    targetVelocity = environment._p.readUserDebugParameter(targetVelocitySlider)
+    steeringAngle = environment._p.readUserDebugParameter(steeringSlider)
+    if (isDiscrete):
+      discreteAction = 0
+      if (targetVelocity < -0.33):
+        discreteAction = 0
+      else:
+        if (targetVelocity > 0.33):
+          discreteAction = 6
+        else:
+          discreteAction = 3
+      if (steeringAngle > -0.17):
+        if (steeringAngle > 0.17):
+          discreteAction = discreteAction + 2
+        else:
+          discreteAction = discreteAction + 1
+      action = discreteAction
+    else:
+      action = [targetVelocity, steeringAngle]
+
+    state, reward, done, info = environment.step(action)
+    obs = environment.getExtendedObservation()
+    #print("obs")
+    #print(obs)
+
+
+if __name__ == "__main__":
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/runServer.py b/examples/pybullet/gym/pybullet_envs/examples/runServer.py
index bcd95f812..054fe66a4 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/runServer.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/runServer.py
@@ -3,7 +3,7 @@ import os
 import inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import pybullet_data
 import pybullet as p
@@ -12,8 +12,7 @@ import time
 p.connect(p.GUI_SERVER)
 p.setAdditionalSearchPath(pybullet_data.getDataPath())
 
-while(1):
-	#this is a no-op command, to allow GUI updates on Mac OSX (main thread)
-	p.setPhysicsEngineParameter()
-	time.sleep(0.01)
-	
+while (1):
+  #this is a no-op command, to allow GUI updates on Mac OSX (main thread)
+  p.setPhysicsEngineParameter()
+  time.sleep(0.01)
diff --git a/examples/pybullet/gym/pybullet_envs/examples/testBike.py b/examples/pybullet/gym/pybullet_envs/examples/testBike.py
index e36922fdc..62475c60d 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/testBike.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/testBike.py
@@ -2,7 +2,7 @@ import os
 import inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import pybullet as p
 import math
@@ -12,31 +12,31 @@ import pybullet_data
 p.connect(p.GUI)
 #p.loadURDF("wheel.urdf",[0,0,3])
 p.setAdditionalSearchPath(pybullet_data.getDataPath())
-plane=p.loadURDF("plane100.urdf",[0,0,0])
-timestep = 1./240.
-
-bike=-1
-for i in range (1):
-
-	bike=p.loadURDF("bicycle/bike.urdf",[0,0+3*i,1.5], [0,0,0,1],	useFixedBase=False)
-	p.setJointMotorControl2(bike,0,p.VELOCITY_CONTROL,targetVelocity=0,force=0.05)
-	#p.setJointMotorControl2(bike,1,p.VELOCITY_CONTROL,targetVelocity=5, force=1000)
-	p.setJointMotorControl2(bike,1,p.VELOCITY_CONTROL,targetVelocity=5, force=0)
-	p.setJointMotorControl2(bike,2,p.VELOCITY_CONTROL,targetVelocity=15, force=20)
-
-	p.changeDynamics(plane,-1, mass=0,lateralFriction=1, linearDamping=0, angularDamping=0)
-	p.changeDynamics(bike,1,lateralFriction=1,linearDamping=0, angularDamping=0)
-	p.changeDynamics(bike,2,lateralFriction=1,linearDamping=0, angularDamping=0)
-	#p.resetJointState(bike,1,0,100)
-	#p.resetJointState(bike,2,0,100)
-	#p.resetBaseVelocity(bike,[0,0,0],[0,0,0])
+plane = p.loadURDF("plane100.urdf", [0, 0, 0])
+timestep = 1. / 240.
+
+bike = -1
+for i in range(1):
+
+  bike = p.loadURDF("bicycle/bike.urdf", [0, 0 + 3 * i, 1.5], [0, 0, 0, 1], useFixedBase=False)
+  p.setJointMotorControl2(bike, 0, p.VELOCITY_CONTROL, targetVelocity=0, force=0.05)
+  #p.setJointMotorControl2(bike,1,p.VELOCITY_CONTROL,targetVelocity=5, force=1000)
+  p.setJointMotorControl2(bike, 1, p.VELOCITY_CONTROL, targetVelocity=5, force=0)
+  p.setJointMotorControl2(bike, 2, p.VELOCITY_CONTROL, targetVelocity=15, force=20)
+
+  p.changeDynamics(plane, -1, mass=0, lateralFriction=1, linearDamping=0, angularDamping=0)
+  p.changeDynamics(bike, 1, lateralFriction=1, linearDamping=0, angularDamping=0)
+  p.changeDynamics(bike, 2, lateralFriction=1, linearDamping=0, angularDamping=0)
+  #p.resetJointState(bike,1,0,100)
+  #p.resetJointState(bike,2,0,100)
+  #p.resetBaseVelocity(bike,[0,0,0],[0,0,0])
 #p.setPhysicsEngineParameter(numSubSteps=0)
 #bike=p.loadURDF("frame.urdf",useFixedBase=True)
 #bike = p.loadURDF("handlebar.urdf", useFixedBase=True)
 #p.loadURDF("handlebar.urdf",[0,2,1])
 #coord	=	p.loadURDF("handlebar.urdf", [0.7700000000000005,	0, 0.22000000000000006],useFixedBase=True)#	p.loadURDF("coordinateframe.urdf",[-2,0,1],useFixedBase=True)
 #coord	=	p.loadURDF("coordinateframe.urdf",[-2,0,1],useFixedBase=True)
-p.setGravity(0,0,-10)
+p.setGravity(0, 0, -10)
 p.setRealTimeSimulation(0)
 #coordPos	=	[0,0,0]
 #coordOrnEuler = [0,0,0]
@@ -44,100 +44,96 @@ p.setRealTimeSimulation(0)
 #coordPos= [0.7000000000000004, 0, 0.22000000000000006]
 #coordOrnEuler= [0, -0.2617993877991496, 0]
 
-coordPos= [0.07, 0, -0.6900000000000004]
-coordOrnEuler= [0, 0, 0]
+coordPos = [0.07, 0, -0.6900000000000004]
+coordOrnEuler = [0, 0, 0]
 
-coordPos2= [0, 0, 0 ]
-coordOrnEuler2= [0, 0, 0]
+coordPos2 = [0, 0, 0]
+coordOrnEuler2 = [0, 0, 0]
 
-invPos,invOrn=p.invertTransform(coordPos,p.getQuaternionFromEuler(coordOrnEuler))
-mPos,mOrn = p.multiplyTransforms(invPos,invOrn, coordPos2,p.getQuaternionFromEuler(coordOrnEuler2))
+invPos, invOrn = p.invertTransform(coordPos, p.getQuaternionFromEuler(coordOrnEuler))
+mPos, mOrn = p.multiplyTransforms(invPos, invOrn, coordPos2,
+                                  p.getQuaternionFromEuler(coordOrnEuler2))
 eul = p.getEulerFromQuaternion(mOrn)
-print("rpy=\"",eul[0],eul[1],eul[2],"\" xyz=\"", mPos[0],mPos[1], mPos[2])
+print("rpy=\"", eul[0], eul[1], eul[2], "\" xyz=\"", mPos[0], mPos[1], mPos[2])
 
-
-shift=0
+shift = 0
 gui = 1
 
-
-frame=0
-states=[]
+frame = 0
+states = []
 states.append(p.saveState())
 #observations=[]
 #observations.append(obs)
-				
+
 running = True
 reverting = False
-p.getCameraImage(320,200)#,renderer=p.ER_BULLET_HARDWARE_OPENGL )
-				
-while	(1):
-
-	updateCam = 0
-	keys = p.getKeyboardEvents()
-	for k,v in keys.items():
-		if (reverting or (k == p.B3G_LEFT_ARROW and (v&p.KEY_WAS_TRIGGERED))):
-			reverting=True
-			stateIndex = len(states)-1
-			#print("prestateIndex=",stateIndex)
-			time.sleep(timestep)
-			updateCam=1
-			if (stateIndex>0):
-				stateIndex-=1
-				states=states[:stateIndex+1]
-				#observations=observations[:stateIndex+1]
-				
-				
-				#print("states=",states)
-			#print("stateIndex =",stateIndex )
-			p.restoreState(states[stateIndex])
-			#obs=observations[stateIndex]
-			
-		
-			#obs, r, done, _ = env.step(a)
-		if (k == p.B3G_LEFT_ARROW and (v&p.KEY_WAS_RELEASED)):
-			reverting = False
-
-		if (k == ord('1') and (v&p.KEY_WAS_TRIGGERED)):
-			gui = not gui
-		
-		
-		
-		if (k == p.B3G_RIGHT_ARROW and (v&p.KEY_WAS_RELEASED)):
-			running=False
-			
-		if (running or (k == p.B3G_RIGHT_ARROW and (v&p.KEY_WAS_TRIGGERED))):
-			running = True
-				
-	if (running):
-		
-		p.stepSimulation()
-		
-		updateCam=1
-		time.sleep(timestep)
-		pts = p.getContactPoints()
-		#print("numPoints=",len(pts))
-		#for point in pts:
-		#	print("Point:bodyA=", point[1],"bodyB=",point[2],"linkA=",point[3],"linkB=",point[4],"dist=",point[8],"force=",point[9])
-		
-		states.append(p.saveState())
-		#observations.append(obs)
-		stateIndex = len(states)
-		#print("stateIndex =",stateIndex )
-		frame += 1
-	if (updateCam):
-		distance=5
-		yaw = 0
-		humanPos, humanOrn = p.getBasePositionAndOrientation(bike)
-		humanBaseVel = p.getBaseVelocity(bike)
-		#print("frame",frame, "humanPos=",humanPos, "humanVel=",humanBaseVel)
-		if (gui):
-			
-			camInfo = p.getDebugVisualizerCamera()
-			curTargetPos = camInfo[11]
-			distance=camInfo[10]
-			yaw = camInfo[8]
-			pitch=camInfo[9]
-			targetPos = [0.95*curTargetPos[0]+0.05*humanPos[0],0.95*curTargetPos[1]+0.05*humanPos[1],curTargetPos[2]]
-			p.resetDebugVisualizerCamera(distance,yaw,pitch,targetPos);
-
-
+p.getCameraImage(320, 200)  #,renderer=p.ER_BULLET_HARDWARE_OPENGL )
+
+while (1):
+
+  updateCam = 0
+  keys = p.getKeyboardEvents()
+  for k, v in keys.items():
+    if (reverting or (k == p.B3G_LEFT_ARROW and (v & p.KEY_WAS_TRIGGERED))):
+      reverting = True
+      stateIndex = len(states) - 1
+      #print("prestateIndex=",stateIndex)
+      time.sleep(timestep)
+      updateCam = 1
+      if (stateIndex > 0):
+        stateIndex -= 1
+        states = states[:stateIndex + 1]
+        #observations=observations[:stateIndex+1]
+
+        #print("states=",states)
+      #print("stateIndex =",stateIndex )
+      p.restoreState(states[stateIndex])
+      #obs=observations[stateIndex]
+
+      #obs, r, done, _ = env.step(a)
+    if (k == p.B3G_LEFT_ARROW and (v & p.KEY_WAS_RELEASED)):
+      reverting = False
+
+    if (k == ord('1') and (v & p.KEY_WAS_TRIGGERED)):
+      gui = not gui
+
+    if (k == p.B3G_RIGHT_ARROW and (v & p.KEY_WAS_RELEASED)):
+      running = False
+
+    if (running or (k == p.B3G_RIGHT_ARROW and (v & p.KEY_WAS_TRIGGERED))):
+      running = True
+
+  if (running):
+
+    p.stepSimulation()
+
+    updateCam = 1
+    time.sleep(timestep)
+    pts = p.getContactPoints()
+    #print("numPoints=",len(pts))
+    #for point in pts:
+    #	print("Point:bodyA=", point[1],"bodyB=",point[2],"linkA=",point[3],"linkB=",point[4],"dist=",point[8],"force=",point[9])
+
+    states.append(p.saveState())
+    #observations.append(obs)
+    stateIndex = len(states)
+    #print("stateIndex =",stateIndex )
+    frame += 1
+  if (updateCam):
+    distance = 5
+    yaw = 0
+    humanPos, humanOrn = p.getBasePositionAndOrientation(bike)
+    humanBaseVel = p.getBaseVelocity(bike)
+    #print("frame",frame, "humanPos=",humanPos, "humanVel=",humanBaseVel)
+    if (gui):
+
+      camInfo = p.getDebugVisualizerCamera()
+      curTargetPos = camInfo[11]
+      distance = camInfo[10]
+      yaw = camInfo[8]
+      pitch = camInfo[9]
+      targetPos = [
+          0.95 * curTargetPos[0] + 0.05 * humanPos[0], 0.95 * curTargetPos[1] + 0.05 * humanPos[1],
+          curTargetPos[2]
+      ]
+      p.resetDebugVisualizerCamera(distance, yaw, pitch, targetPos)
diff --git a/examples/pybullet/gym/pybullet_envs/examples/testEnv.py b/examples/pybullet/gym/pybullet_envs/examples/testEnv.py
index 61f9bd0f7..5d30d27af 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/testEnv.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/testEnv.py
@@ -2,7 +2,7 @@ import os
 import inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 import pybullet_envs
 import gym
 import argparse
@@ -10,48 +10,52 @@ import pybullet as p
 
 
 def test(args):
-	count = 0
-	env = gym.make(args.env)
-	env.env.configure(args)
-	print("args.render=",args.render)
-	if (args.render==1):
-		env.render(mode="human")
-	env.reset()
-	if (args.resetbenchmark):
-		while (1):
-			env.reset()
-			print("p.getNumBodies()=",p.getNumBodies())
-			print("count=",count)
-			count+=1
-	print("action space:")
-	sample = env.action_space.sample()
-	action = sample*0.0
-	print("action=")
-	print(action)
-	for i in range(args.steps):
-		obs,rewards,done,_ =env.step(action)
-		if (args.rgb):
-			print(env.render(mode="rgb_array"))
-		print("obs=")
-		print(obs)
-		print("rewards")
-		print (rewards)
-		print ("done")
-		print(done)
+  count = 0
+  env = gym.make(args.env)
+  env.env.configure(args)
+  print("args.render=", args.render)
+  if (args.render == 1):
+    env.render(mode="human")
+  env.reset()
+  if (args.resetbenchmark):
+    while (1):
+      env.reset()
+      print("p.getNumBodies()=", p.getNumBodies())
+      print("count=", count)
+      count += 1
+  print("action space:")
+  sample = env.action_space.sample()
+  action = sample * 0.0
+  print("action=")
+  print(action)
+  for i in range(args.steps):
+    obs, rewards, done, _ = env.step(action)
+    if (args.rgb):
+      print(env.render(mode="rgb_array"))
+    print("obs=")
+    print(obs)
+    print("rewards")
+    print(rewards)
+    print("done")
+    print(done)
 
 
 def main():
-    import argparse
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--env', help='environment ID', default='AntBulletEnv-v0')
-    parser.add_argument('--seed', help='RNG seed', type=int, default=0)
-    parser.add_argument('--render', help='OpenGL Visualizer', type=int, default=0)
-    parser.add_argument('--rgb',help='rgb_array gym rendering',type=int, default=0)
-    parser.add_argument('--resetbenchmark',help='Repeat reset to show reset performance',type=int, default=0)
-    parser.add_argument('--steps', help='Number of steps', type=int, default=1)
-    
-    args = parser.parse_args()
-    test(args)
+  import argparse
+  parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument('--env', help='environment ID', default='AntBulletEnv-v0')
+  parser.add_argument('--seed', help='RNG seed', type=int, default=0)
+  parser.add_argument('--render', help='OpenGL Visualizer', type=int, default=0)
+  parser.add_argument('--rgb', help='rgb_array gym rendering', type=int, default=0)
+  parser.add_argument('--resetbenchmark',
+                      help='Repeat reset to show reset performance',
+                      type=int,
+                      default=0)
+  parser.add_argument('--steps', help='Number of steps', type=int, default=1)
+
+  args = parser.parse_args()
+  test(args)
+
 
 if __name__ == '__main__':
-    main()
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/examples/testMJCF.py b/examples/pybullet/gym/pybullet_envs/examples/testMJCF.py
index ebdef3b38..76a889c6a 100644
--- a/examples/pybullet/gym/pybullet_envs/examples/testMJCF.py
+++ b/examples/pybullet/gym/pybullet_envs/examples/testMJCF.py
@@ -2,27 +2,29 @@ import os
 import inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import pybullet as p
 import pybullet_data
 import time
 
-def test(args):	
-	p.connect(p.GUI)
-	p.setAdditionalSearchPath(pybullet_data.getDataPath())
-	fileName = os.path.join("mjcf", args.mjcf)
-	print("fileName")
-	print(fileName)
-	p.loadMJCF(fileName)
-	while (1):
-		p.stepSimulation()
-		p.getCameraImage(320,240)
-		time.sleep(0.01)
-	
+
+def test(args):
+  p.connect(p.GUI)
+  p.setAdditionalSearchPath(pybullet_data.getDataPath())
+  fileName = os.path.join("mjcf", args.mjcf)
+  print("fileName")
+  print(fileName)
+  p.loadMJCF(fileName)
+  while (1):
+    p.stepSimulation()
+    p.getCameraImage(320, 240)
+    time.sleep(0.01)
+
+
 if __name__ == '__main__':
-	import argparse
-	parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-	parser.add_argument('--mjcf', help='MJCF filename', default="humanoid.xml")
-	args = parser.parse_args()
-	test(args)
\ No newline at end of file
+  import argparse
+  parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument('--mjcf', help='MJCF filename', default="humanoid.xml")
+  args = parser.parse_args()
+  test(args)
diff --git a/examples/pybullet/gym/pybullet_envs/gym_locomotion_envs.py b/examples/pybullet/gym/pybullet_envs/gym_locomotion_envs.py
index 1ece4b4e5..d601c130b 100644
--- a/examples/pybullet/gym/pybullet_envs/gym_locomotion_envs.py
+++ b/examples/pybullet/gym/pybullet_envs/gym_locomotion_envs.py
@@ -6,175 +6,192 @@ from robot_locomotors import Hopper, Walker2D, HalfCheetah, Ant, Humanoid, Human
 
 
 class WalkerBaseBulletEnv(MJCFBaseBulletEnv):
-	def __init__(self, robot, render=False):
-		# print("WalkerBase::__init__ start")
-		MJCFBaseBulletEnv.__init__(self, robot, render)
-
-		self.camera_x = 0
-		self.walk_target_x = 1e3  # kilometer away
-		self.walk_target_y = 0
-		self.stateId=-1
-
-
-	def create_single_player_scene(self, bullet_client):
-		self.stadium_scene = SinglePlayerStadiumScene(bullet_client, gravity=9.8, timestep=0.0165/4, frame_skip=4)
-		return self.stadium_scene
-
-	def reset(self):
-		if (self.stateId>=0):
-			#print("restoreState self.stateId:",self.stateId)
-			self._p.restoreState(self.stateId)
-
-		r = MJCFBaseBulletEnv.reset(self)
-		self._p.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING,0)
-
-		self.parts, self.jdict, self.ordered_joints, self.robot_body = self.robot.addToScene(self._p,
-			self.stadium_scene.ground_plane_mjcf)
-		self.ground_ids = set([(self.parts[f].bodies[self.parts[f].bodyIndex], self.parts[f].bodyPartIndex) for f in
-							   self.foot_ground_object_names])
-		self._p.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING,1)
-		if (self.stateId<0):
-			self.stateId=self._p.saveState()
-			#print("saving state self.stateId:",self.stateId)
-
-
-		return r
-
-	def _isDone(self):
-		return self._alive < 0
-
-	def move_robot(self, init_x, init_y, init_z):
-		"Used by multiplayer stadium to move sideways, to another running lane."
-		self.cpp_robot.query_position()
-		pose = self.cpp_robot.root_part.pose()
-		pose.move_xyz(init_x, init_y, init_z)  # Works because robot loads around (0,0,0), and some robots have z != 0 that is left intact
-		self.cpp_robot.set_pose(pose)
-
-	electricity_cost	 = -2.0	# cost for using motors -- this parameter should be carefully tuned against reward for making progress, other values less improtant
-	stall_torque_cost	= -0.1	# cost for running electric current through a motor even at zero rotational speed, small
-	foot_collision_cost  = -1.0	# touches another leg, or other objects, that cost makes robot avoid smashing feet into itself
-	foot_ground_object_names = set(["floor"])  # to distinguish ground and other objects
-	joints_at_limit_cost = -0.1	# discourage stuck joints
-
-	def step(self, a):
-		if not self.scene.multiplayer:  # if multiplayer, action first applied to all robots, then global step() called, then _step() for all robots with the same actions
-			self.robot.apply_action(a)
-			self.scene.global_step()
-
-		state = self.robot.calc_state()  # also calculates self.joints_at_limit
-
-		self._alive = float(self.robot.alive_bonus(state[0]+self.robot.initial_z, self.robot.body_rpy[1]))   # state[0] is body height above ground, body_rpy[1] is pitch
-		done = self._isDone()
-		if not np.isfinite(state).all():
-			print("~INF~", state)
-			done = True
-
-		potential_old = self.potential
-		self.potential = self.robot.calc_potential()
-		progress = float(self.potential - potential_old)
-
-		feet_collision_cost = 0.0
-		for i,f in enumerate(self.robot.feet): # TODO: Maybe calculating feet contacts could be done within the robot code
-			contact_ids = set((x[2], x[4]) for x in f.contact_list())
-			#print("CONTACT OF '%d' WITH %d" % (contact_ids, ",".join(contact_names)) )
-			if (self.ground_ids & contact_ids):
-                        	#see Issue 63: https://github.com/openai/roboschool/issues/63
-				#feet_collision_cost += self.foot_collision_cost
-				self.robot.feet_contact[i] = 1.0
-			else:
-				self.robot.feet_contact[i] = 0.0
-
-
-		electricity_cost  = self.electricity_cost  * float(np.abs(a*self.robot.joint_speeds).mean())  # let's assume we have DC motor with controller, and reverse current braking
-		electricity_cost += self.stall_torque_cost * float(np.square(a).mean())
-
-		joints_at_limit_cost = float(self.joints_at_limit_cost * self.robot.joints_at_limit)
-		debugmode=0
-		if(debugmode):
-			print("alive=")
-			print(self._alive)
-			print("progress")
-			print(progress)
-			print("electricity_cost")
-			print(electricity_cost)
-			print("joints_at_limit_cost")
-			print(joints_at_limit_cost)
-			print("feet_collision_cost")
-			print(feet_collision_cost)
-
-		self.rewards = [
-			self._alive,
-			progress,
-			electricity_cost,
-			joints_at_limit_cost,
-			feet_collision_cost
-			]
-		if (debugmode):
-			print("rewards=")
-			print(self.rewards)
-			print("sum rewards")
-			print(sum(self.rewards))
-		self.HUD(state, a, done)
-		self.reward += sum(self.rewards)
-
-		return state, sum(self.rewards), bool(done), {}
-
-	def camera_adjust(self):
-		x, y, z = self.body_xyz
-		self.camera_x = 0.98*self.camera_x + (1-0.98)*x
-		self.camera.move_and_look_at(self.camera_x, y-2.0, 1.4, x, y, 1.0)
+
+  def __init__(self, robot, render=False):
+    # print("WalkerBase::__init__ start")
+    MJCFBaseBulletEnv.__init__(self, robot, render)
+
+    self.camera_x = 0
+    self.walk_target_x = 1e3  # kilometer away
+    self.walk_target_y = 0
+    self.stateId = -1
+
+  def create_single_player_scene(self, bullet_client):
+    self.stadium_scene = SinglePlayerStadiumScene(bullet_client,
+                                                  gravity=9.8,
+                                                  timestep=0.0165 / 4,
+                                                  frame_skip=4)
+    return self.stadium_scene
+
+  def reset(self):
+    if (self.stateId >= 0):
+      #print("restoreState self.stateId:",self.stateId)
+      self._p.restoreState(self.stateId)
+
+    r = MJCFBaseBulletEnv.reset(self)
+    self._p.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING, 0)
+
+    self.parts, self.jdict, self.ordered_joints, self.robot_body = self.robot.addToScene(
+        self._p, self.stadium_scene.ground_plane_mjcf)
+    self.ground_ids = set([(self.parts[f].bodies[self.parts[f].bodyIndex],
+                            self.parts[f].bodyPartIndex) for f in self.foot_ground_object_names])
+    self._p.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING, 1)
+    if (self.stateId < 0):
+      self.stateId = self._p.saveState()
+      #print("saving state self.stateId:",self.stateId)
+
+    return r
+
+  def _isDone(self):
+    return self._alive < 0
+
+  def move_robot(self, init_x, init_y, init_z):
+    "Used by multiplayer stadium to move sideways, to another running lane."
+    self.cpp_robot.query_position()
+    pose = self.cpp_robot.root_part.pose()
+    pose.move_xyz(
+        init_x, init_y, init_z
+    )  # Works because robot loads around (0,0,0), and some robots have z != 0 that is left intact
+    self.cpp_robot.set_pose(pose)
+
+  electricity_cost = -2.0  # cost for using motors -- this parameter should be carefully tuned against reward for making progress, other values less improtant
+  stall_torque_cost = -0.1  # cost for running electric current through a motor even at zero rotational speed, small
+  foot_collision_cost = -1.0  # touches another leg, or other objects, that cost makes robot avoid smashing feet into itself
+  foot_ground_object_names = set(["floor"])  # to distinguish ground and other objects
+  joints_at_limit_cost = -0.1  # discourage stuck joints
+
+  def step(self, a):
+    if not self.scene.multiplayer:  # if multiplayer, action first applied to all robots, then global step() called, then _step() for all robots with the same actions
+      self.robot.apply_action(a)
+      self.scene.global_step()
+
+    state = self.robot.calc_state()  # also calculates self.joints_at_limit
+
+    self._alive = float(
+        self.robot.alive_bonus(
+            state[0] + self.robot.initial_z,
+            self.robot.body_rpy[1]))  # state[0] is body height above ground, body_rpy[1] is pitch
+    done = self._isDone()
+    if not np.isfinite(state).all():
+      print("~INF~", state)
+      done = True
+
+    potential_old = self.potential
+    self.potential = self.robot.calc_potential()
+    progress = float(self.potential - potential_old)
+
+    feet_collision_cost = 0.0
+    for i, f in enumerate(
+        self.robot.feet
+    ):  # TODO: Maybe calculating feet contacts could be done within the robot code
+      contact_ids = set((x[2], x[4]) for x in f.contact_list())
+      #print("CONTACT OF '%d' WITH %d" % (contact_ids, ",".join(contact_names)) )
+      if (self.ground_ids & contact_ids):
+        #see Issue 63: https://github.com/openai/roboschool/issues/63
+        #feet_collision_cost += self.foot_collision_cost
+        self.robot.feet_contact[i] = 1.0
+      else:
+        self.robot.feet_contact[i] = 0.0
+
+    electricity_cost = self.electricity_cost * float(np.abs(a * self.robot.joint_speeds).mean(
+    ))  # let's assume we have DC motor with controller, and reverse current braking
+    electricity_cost += self.stall_torque_cost * float(np.square(a).mean())
+
+    joints_at_limit_cost = float(self.joints_at_limit_cost * self.robot.joints_at_limit)
+    debugmode = 0
+    if (debugmode):
+      print("alive=")
+      print(self._alive)
+      print("progress")
+      print(progress)
+      print("electricity_cost")
+      print(electricity_cost)
+      print("joints_at_limit_cost")
+      print(joints_at_limit_cost)
+      print("feet_collision_cost")
+      print(feet_collision_cost)
+
+    self.rewards = [
+        self._alive, progress, electricity_cost, joints_at_limit_cost, feet_collision_cost
+    ]
+    if (debugmode):
+      print("rewards=")
+      print(self.rewards)
+      print("sum rewards")
+      print(sum(self.rewards))
+    self.HUD(state, a, done)
+    self.reward += sum(self.rewards)
+
+    return state, sum(self.rewards), bool(done), {}
+
+  def camera_adjust(self):
+    x, y, z = self.body_xyz
+    self.camera_x = 0.98 * self.camera_x + (1 - 0.98) * x
+    self.camera.move_and_look_at(self.camera_x, y - 2.0, 1.4, x, y, 1.0)
+
 
 class HopperBulletEnv(WalkerBaseBulletEnv):
-	def __init__(self, render=False):
-		self.robot = Hopper()
-		WalkerBaseBulletEnv.__init__(self, self.robot, render)
+
+  def __init__(self, render=False):
+    self.robot = Hopper()
+    WalkerBaseBulletEnv.__init__(self, self.robot, render)
+
 
 class Walker2DBulletEnv(WalkerBaseBulletEnv):
-	def __init__(self, render=False):
-		self.robot = Walker2D()
-		WalkerBaseBulletEnv.__init__(self, self.robot, render)
+
+  def __init__(self, render=False):
+    self.robot = Walker2D()
+    WalkerBaseBulletEnv.__init__(self, self.robot, render)
+
 
 class HalfCheetahBulletEnv(WalkerBaseBulletEnv):
-	def __init__(self, render=False):
-		self.robot = HalfCheetah()
-		WalkerBaseBulletEnv.__init__(self, self.robot, render)
 
-	def _isDone(self):
-		return False
+  def __init__(self, render=False):
+    self.robot = HalfCheetah()
+    WalkerBaseBulletEnv.__init__(self, self.robot, render)
+
+  def _isDone(self):
+    return False
+
 
 class AntBulletEnv(WalkerBaseBulletEnv):
-	def __init__(self, render=False):
-		self.robot = Ant()
-		WalkerBaseBulletEnv.__init__(self, self.robot, render)
+
+  def __init__(self, render=False):
+    self.robot = Ant()
+    WalkerBaseBulletEnv.__init__(self, self.robot, render)
+
 
 class HumanoidBulletEnv(WalkerBaseBulletEnv):
-	def __init__(self, robot=Humanoid(), render=False):
-		self.robot = robot
-		WalkerBaseBulletEnv.__init__(self, self.robot, render)
-		self.electricity_cost  = 4.25*WalkerBaseBulletEnv.electricity_cost
-		self.stall_torque_cost = 4.25*WalkerBaseBulletEnv.stall_torque_cost
+
+  def __init__(self, robot=Humanoid(), render=False):
+    self.robot = robot
+    WalkerBaseBulletEnv.__init__(self, self.robot, render)
+    self.electricity_cost = 4.25 * WalkerBaseBulletEnv.electricity_cost
+    self.stall_torque_cost = 4.25 * WalkerBaseBulletEnv.stall_torque_cost
+
 
 class HumanoidFlagrunBulletEnv(HumanoidBulletEnv):
-	random_yaw = True
+  random_yaw = True
+
+  def __init__(self, render=False):
+    self.robot = HumanoidFlagrun()
+    HumanoidBulletEnv.__init__(self, self.robot, render)
 
-	def __init__(self, render=False):
-		self.robot = HumanoidFlagrun()
-		HumanoidBulletEnv.__init__(self, self.robot, render)
+  def create_single_player_scene(self, bullet_client):
+    s = HumanoidBulletEnv.create_single_player_scene(self, bullet_client)
+    s.zero_at_running_strip_start_line = False
+    return s
 
-	def create_single_player_scene(self, bullet_client):
-		s = HumanoidBulletEnv.create_single_player_scene(self, bullet_client)
-		s.zero_at_running_strip_start_line = False
-		return s
 
 class HumanoidFlagrunHarderBulletEnv(HumanoidBulletEnv):
-	random_lean = True  # can fall on start
+  random_lean = True  # can fall on start
 
-	def __init__(self, render=False):
-		self.robot = HumanoidFlagrunHarder()
-		self.electricity_cost /= 4   # don't care that much about electricity, just stand up!
-		HumanoidBulletEnv.__init__(self, self.robot, render)
+  def __init__(self, render=False):
+    self.robot = HumanoidFlagrunHarder()
+    self.electricity_cost /= 4  # don't care that much about electricity, just stand up!
+    HumanoidBulletEnv.__init__(self, self.robot, render)
 
-	def create_single_player_scene(self, bullet_client):
-		s = HumanoidBulletEnv.create_single_player_scene(self, bullet_client)
-		s.zero_at_running_strip_start_line = False
-		return s
+  def create_single_player_scene(self, bullet_client):
+    s = HumanoidBulletEnv.create_single_player_scene(self, bullet_client)
+    s.zero_at_running_strip_start_line = False
+    return s
diff --git a/examples/pybullet/gym/pybullet_envs/gym_manipulator_envs.py b/examples/pybullet/gym/pybullet_envs/gym_manipulator_envs.py
index 8ac4f8ad2..9fef7e615 100644
--- a/examples/pybullet/gym/pybullet_envs/gym_manipulator_envs.py
+++ b/examples/pybullet/gym/pybullet_envs/gym_manipulator_envs.py
@@ -5,229 +5,250 @@ from robot_manipulators import Reacher, Pusher, Striker, Thrower
 
 
 class ReacherBulletEnv(MJCFBaseBulletEnv):
-	def __init__(self, render=False):
-		self.robot = Reacher()
-		MJCFBaseBulletEnv.__init__(self, self.robot, render)
 
-	def create_single_player_scene(self, bullet_client):
-		return SingleRobotEmptyScene(bullet_client, gravity=0.0, timestep=0.0165, frame_skip=1)
+  def __init__(self, render=False):
+    self.robot = Reacher()
+    MJCFBaseBulletEnv.__init__(self, self.robot, render)
 
-	def step(self, a):
-		assert (not self.scene.multiplayer)
-		self.robot.apply_action(a)
-		self.scene.global_step()
+  def create_single_player_scene(self, bullet_client):
+    return SingleRobotEmptyScene(bullet_client, gravity=0.0, timestep=0.0165, frame_skip=1)
 
-		state = self.robot.calc_state()  # sets self.to_target_vec
+  def step(self, a):
+    assert (not self.scene.multiplayer)
+    self.robot.apply_action(a)
+    self.scene.global_step()
 
-		potential_old = self.potential
-		self.potential = self.robot.calc_potential()
+    state = self.robot.calc_state()  # sets self.to_target_vec
 
-		electricity_cost = (
-			-0.10 * (np.abs(a[0] * self.robot.theta_dot) + np.abs(a[1] * self.robot.gamma_dot))  # work torque*angular_velocity
-			- 0.01 * (np.abs(a[0]) + np.abs(a[1]))  # stall torque require some energy
-		)
-		stuck_joint_cost = -0.1 if np.abs(np.abs(self.robot.gamma) - 1) < 0.01 else 0.0
-		self.rewards = [float(self.potential - potential_old), float(electricity_cost), float(stuck_joint_cost)]
-		self.HUD(state, a, False)
-		return state, sum(self.rewards), False, {}
+    potential_old = self.potential
+    self.potential = self.robot.calc_potential()
 
-	def camera_adjust(self):
-		x, y, z = self.robot.fingertip.pose().xyz()
-		x *= 0.5
-		y *= 0.5
-		self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z)
+    electricity_cost = (
+        -0.10 * (np.abs(a[0] * self.robot.theta_dot) + np.abs(a[1] * self.robot.gamma_dot)
+                )  # work torque*angular_velocity
+        - 0.01 * (np.abs(a[0]) + np.abs(a[1]))  # stall torque require some energy
+    )
+    stuck_joint_cost = -0.1 if np.abs(np.abs(self.robot.gamma) - 1) < 0.01 else 0.0
+    self.rewards = [
+        float(self.potential - potential_old),
+        float(electricity_cost),
+        float(stuck_joint_cost)
+    ]
+    self.HUD(state, a, False)
+    return state, sum(self.rewards), False, {}
 
+  def camera_adjust(self):
+    x, y, z = self.robot.fingertip.pose().xyz()
+    x *= 0.5
+    y *= 0.5
+    self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z)
 
-class PusherBulletEnv(MJCFBaseBulletEnv):
-	def __init__(self, render=False):
-		self.robot = Pusher()
-		MJCFBaseBulletEnv.__init__(self, self.robot, render)
-
-	def create_single_player_scene(self, bullet_client):
-		return SingleRobotEmptyScene(bullet_client, gravity=9.81, timestep=0.0020, frame_skip=5)
-
-	def step(self, a):
-		self.robot.apply_action(a)
-		self.scene.global_step()
-
-		state = self.robot.calc_state()  # sets self.to_target_vec
-
-		potential_old = self.potential
-		self.potential = self.robot.calc_potential()
-
-		joint_vel = np.array([
-			self.robot.shoulder_pan_joint.get_velocity(),
-			self.robot.shoulder_lift_joint.get_velocity(),
-			self.robot.upper_arm_roll_joint.get_velocity(),
-			self.robot.elbow_flex_joint.get_velocity(),
-			self.robot.upper_arm_roll_joint.get_velocity(),
-			self.robot.wrist_flex_joint.get_velocity(),
-			self.robot.wrist_roll_joint.get_velocity()
-		])
-
-		action_product = np.matmul(np.abs(a), np.abs(joint_vel))
-		action_sum = np.sum(a)
-
-		electricity_cost = (
-			-0.10 * action_product  # work torque*angular_velocity
-			- 0.01 * action_sum  # stall torque require some energy
-		)
-
-		stuck_joint_cost = 0
-		for j in self.robot.ordered_joints:
-			if np.abs(j.current_relative_position()[0]) - 1 < 0.01:
-				stuck_joint_cost += -0.1
 
-		self.rewards = [float(self.potential - potential_old), float(electricity_cost), float(stuck_joint_cost)]
-		self.HUD(state, a, False)
-		return state, sum(self.rewards), False, {}
-
-	def calc_potential(self):
-		return -100 * np.linalg.norm(self.to_target_vec)
-
-	def camera_adjust(self):
-		x, y, z = self.robot.fingertip.pose().xyz()
-		x *= 0.5
-		y *= 0.5
-		self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z)
-
-
-class StrikerBulletEnv(MJCFBaseBulletEnv):
-	def __init__(self, render=False):
-		self.robot = Striker()
-		MJCFBaseBulletEnv.__init__(self, self.robot, render)
-		self._striked = False
-		self._min_strike_dist = np.inf
-		self.strike_threshold = 0.1
-
-	def create_single_player_scene(self, bullet_client):
-		return SingleRobotEmptyScene(bullet_client, gravity=9.81, timestep=0.0020, frame_skip=5)
+class PusherBulletEnv(MJCFBaseBulletEnv):
 
-	def step(self, a):
-		self.robot.apply_action(a)
-		self.scene.global_step()
+  def __init__(self, render=False):
+    self.robot = Pusher()
+    MJCFBaseBulletEnv.__init__(self, self.robot, render)
 
-		state = self.robot.calc_state()  # sets self.to_target_vec
+  def create_single_player_scene(self, bullet_client):
+    return SingleRobotEmptyScene(bullet_client, gravity=9.81, timestep=0.0020, frame_skip=5)
 
-		potential_old = self.potential
-		self.potential = self.robot.calc_potential()
+  def step(self, a):
+    self.robot.apply_action(a)
+    self.scene.global_step()
 
-		joint_vel = np.array([
-			self.robot.shoulder_pan_joint.get_velocity(),
-			self.robot.shoulder_lift_joint.get_velocity(),
-			self.robot.upper_arm_roll_joint.get_velocity(),
-			self.robot.elbow_flex_joint.get_velocity(),
-			self.robot.upper_arm_roll_joint.get_velocity(),
-			self.robot.wrist_flex_joint.get_velocity(),
-			self.robot.wrist_roll_joint.get_velocity()
-		])
+    state = self.robot.calc_state()  # sets self.to_target_vec
 
-		action_product = np.matmul(np.abs(a), np.abs(joint_vel))
-		action_sum = np.sum(a)
+    potential_old = self.potential
+    self.potential = self.robot.calc_potential()
 
-		electricity_cost = (
-			-0.10 * action_product  # work torque*angular_velocity
-			- 0.01 * action_sum  # stall torque require some energy
-		)
+    joint_vel = np.array([
+        self.robot.shoulder_pan_joint.get_velocity(),
+        self.robot.shoulder_lift_joint.get_velocity(),
+        self.robot.upper_arm_roll_joint.get_velocity(),
+        self.robot.elbow_flex_joint.get_velocity(),
+        self.robot.upper_arm_roll_joint.get_velocity(),
+        self.robot.wrist_flex_joint.get_velocity(),
+        self.robot.wrist_roll_joint.get_velocity()
+    ])
 
-		stuck_joint_cost = 0
-		for j in self.robot.ordered_joints:
-			if np.abs(j.current_relative_position()[0]) - 1 < 0.01:
-				stuck_joint_cost += -0.1
+    action_product = np.matmul(np.abs(a), np.abs(joint_vel))
+    action_sum = np.sum(a)
 
-		dist_object_finger = self.robot.object.pose().xyz() - self.robot.fingertip.pose().xyz()
-		reward_dist_vec = self.robot.object.pose().xyz() - self.robot.target.pose().xyz()		# TODO: Should the object and target really belong to the robot? Maybe split this off
+    electricity_cost = (
+        -0.10 * action_product  # work torque*angular_velocity
+        - 0.01 * action_sum  # stall torque require some energy
+    )
 
-		self._min_strike_dist = min(self._min_strike_dist, np.linalg.norm(reward_dist_vec))
+    stuck_joint_cost = 0
+    for j in self.robot.ordered_joints:
+      if np.abs(j.current_relative_position()[0]) - 1 < 0.01:
+        stuck_joint_cost += -0.1
 
-		if np.linalg.norm(dist_object_finger) < self.strike_threshold:
-			self._striked = True
-			self._strike_pos = self.robot.fingertip.pose().xyz()
+    self.rewards = [
+        float(self.potential - potential_old),
+        float(electricity_cost),
+        float(stuck_joint_cost)
+    ]
+    self.HUD(state, a, False)
+    return state, sum(self.rewards), False, {}
 
-		if self._striked:
-			reward_near_vec = self.robot.object.pose().xyz() - self._strike_pos
-		else:
-			reward_near_vec = self.robot.object.pose().xyz() - self.robot.fingertip.pose().xyz()
+  def calc_potential(self):
+    return -100 * np.linalg.norm(self.to_target_vec)
 
-		reward_near = - np.linalg.norm(reward_near_vec)
+  def camera_adjust(self):
+    x, y, z = self.robot.fingertip.pose().xyz()
+    x *= 0.5
+    y *= 0.5
+    self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z)
 
-		reward_dist = - np.linalg.norm(self._min_strike_dist)
-		reward_ctrl = - np.square(a).sum()
-		self.rewards = [float(self.potential - potential_old), float(electricity_cost), float(stuck_joint_cost),
-						3 * reward_dist, 0.1 * reward_ctrl, 0.5 * reward_near]
-		self.HUD(state, a, False)
-		return state, sum(self.rewards), False, {}
 
-	def calc_potential(self):
-		return -100 * np.linalg.norm(self.to_target_vec)
+class StrikerBulletEnv(MJCFBaseBulletEnv):
 
-	def camera_adjust(self):
-		x, y, z = self.robot.fingertip.pose().xyz()
-		x *= 0.5
-		y *= 0.5
-		self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z)
+  def __init__(self, render=False):
+    self.robot = Striker()
+    MJCFBaseBulletEnv.__init__(self, self.robot, render)
+    self._striked = False
+    self._min_strike_dist = np.inf
+    self.strike_threshold = 0.1
+
+  def create_single_player_scene(self, bullet_client):
+    return SingleRobotEmptyScene(bullet_client, gravity=9.81, timestep=0.0020, frame_skip=5)
+
+  def step(self, a):
+    self.robot.apply_action(a)
+    self.scene.global_step()
+
+    state = self.robot.calc_state()  # sets self.to_target_vec
+
+    potential_old = self.potential
+    self.potential = self.robot.calc_potential()
+
+    joint_vel = np.array([
+        self.robot.shoulder_pan_joint.get_velocity(),
+        self.robot.shoulder_lift_joint.get_velocity(),
+        self.robot.upper_arm_roll_joint.get_velocity(),
+        self.robot.elbow_flex_joint.get_velocity(),
+        self.robot.upper_arm_roll_joint.get_velocity(),
+        self.robot.wrist_flex_joint.get_velocity(),
+        self.robot.wrist_roll_joint.get_velocity()
+    ])
+
+    action_product = np.matmul(np.abs(a), np.abs(joint_vel))
+    action_sum = np.sum(a)
+
+    electricity_cost = (
+        -0.10 * action_product  # work torque*angular_velocity
+        - 0.01 * action_sum  # stall torque require some energy
+    )
+
+    stuck_joint_cost = 0
+    for j in self.robot.ordered_joints:
+      if np.abs(j.current_relative_position()[0]) - 1 < 0.01:
+        stuck_joint_cost += -0.1
+
+    dist_object_finger = self.robot.object.pose().xyz() - self.robot.fingertip.pose().xyz()
+    reward_dist_vec = self.robot.object.pose().xyz() - self.robot.target.pose().xyz(
+    )  # TODO: Should the object and target really belong to the robot? Maybe split this off
+
+    self._min_strike_dist = min(self._min_strike_dist, np.linalg.norm(reward_dist_vec))
+
+    if np.linalg.norm(dist_object_finger) < self.strike_threshold:
+      self._striked = True
+      self._strike_pos = self.robot.fingertip.pose().xyz()
+
+    if self._striked:
+      reward_near_vec = self.robot.object.pose().xyz() - self._strike_pos
+    else:
+      reward_near_vec = self.robot.object.pose().xyz() - self.robot.fingertip.pose().xyz()
+
+    reward_near = -np.linalg.norm(reward_near_vec)
+
+    reward_dist = -np.linalg.norm(self._min_strike_dist)
+    reward_ctrl = -np.square(a).sum()
+    self.rewards = [
+        float(self.potential - potential_old),
+        float(electricity_cost),
+        float(stuck_joint_cost), 3 * reward_dist, 0.1 * reward_ctrl, 0.5 * reward_near
+    ]
+    self.HUD(state, a, False)
+    return state, sum(self.rewards), False, {}
+
+  def calc_potential(self):
+    return -100 * np.linalg.norm(self.to_target_vec)
+
+  def camera_adjust(self):
+    x, y, z = self.robot.fingertip.pose().xyz()
+    x *= 0.5
+    y *= 0.5
+    self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z)
 
 
 class ThrowerBulletEnv(MJCFBaseBulletEnv):
-	def __init__(self, render=False):
-		self.robot = Thrower()
-		MJCFBaseBulletEnv.__init__(self, self.robot, render)
-
-	def create_single_player_scene(self, bullet_client):
-		return SingleRobotEmptyScene(bullet_client, gravity=0.0, timestep=0.0020, frame_skip=5)
-
-	def step(self, a):
-		self.robot.apply_action(a)
-		self.scene.global_step()
-		state = self.robot.calc_state()  # sets self.to_target_vec
-
-		potential_old = self.potential
-		self.potential = self.robot.calc_potential()
-
-		joint_vel = np.array([
-			self.robot.shoulder_pan_joint.get_velocity(),
-			self.robot.shoulder_lift_joint.get_velocity(),
-			self.robot.upper_arm_roll_joint.get_velocity(),
-			self.robot.elbow_flex_joint.get_velocity(),
-			self.robot.upper_arm_roll_joint.get_velocity(),
-			self.robot.wrist_flex_joint.get_velocity(),
-			self.robot.wrist_roll_joint.get_velocity()
-		])
-
-		action_product = np.matmul(np.abs(a), np.abs(joint_vel))
-		action_sum = np.sum(a)
-
-		electricity_cost = (
-			-0.10 * action_product  # work torque*angular_velocity
-			- 0.01 * action_sum  # stall torque require some energy
-		)
-
-		stuck_joint_cost = 0
-		for j in self.robot.ordered_joints:
-			if np.abs(j.current_relative_position()[0]) - 1 < 0.01:
-				stuck_joint_cost += -0.1
-
-		object_xy = self.robot.object.pose().xyz()[:2]
-		target_xy = self.robot.target.pose().xyz()[:2]
-
-		if not self.robot._object_hit_ground and self.robot.object.pose().xyz()[2] < -0.25:						# TODO: Should the object and target really belong to the robot? Maybe split this off
-			self.robot._object_hit_ground = True
-			self.robot._object_hit_location = self.robot.object.pose().xyz()
-
-		if self.robot._object_hit_ground:
-			object_hit_xy = self.robot._object_hit_location[:2]
-			reward_dist = -np.linalg.norm(object_hit_xy - target_xy)
-		else:
-			reward_dist = -np.linalg.norm(object_xy - target_xy)
-		reward_ctrl = - np.square(a).sum()
-
-		self.rewards = [float(self.potential - potential_old), float(electricity_cost), float(stuck_joint_cost),
-						reward_dist, 0.002 * reward_ctrl]
-		self.HUD(state, a, False)
-		return state, sum(self.rewards), False, {}
-
-	def camera_adjust(self):
-		x, y, z = self.robot.fingertip.pose().xyz()
-		x *= 0.5
-		y *= 0.5
-		self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z)
+
+  def __init__(self, render=False):
+    self.robot = Thrower()
+    MJCFBaseBulletEnv.__init__(self, self.robot, render)
+
+  def create_single_player_scene(self, bullet_client):
+    return SingleRobotEmptyScene(bullet_client, gravity=0.0, timestep=0.0020, frame_skip=5)
+
+  def step(self, a):
+    self.robot.apply_action(a)
+    self.scene.global_step()
+    state = self.robot.calc_state()  # sets self.to_target_vec
+
+    potential_old = self.potential
+    self.potential = self.robot.calc_potential()
+
+    joint_vel = np.array([
+        self.robot.shoulder_pan_joint.get_velocity(),
+        self.robot.shoulder_lift_joint.get_velocity(),
+        self.robot.upper_arm_roll_joint.get_velocity(),
+        self.robot.elbow_flex_joint.get_velocity(),
+        self.robot.upper_arm_roll_joint.get_velocity(),
+        self.robot.wrist_flex_joint.get_velocity(),
+        self.robot.wrist_roll_joint.get_velocity()
+    ])
+
+    action_product = np.matmul(np.abs(a), np.abs(joint_vel))
+    action_sum = np.sum(a)
+
+    electricity_cost = (
+        -0.10 * action_product  # work torque*angular_velocity
+        - 0.01 * action_sum  # stall torque require some energy
+    )
+
+    stuck_joint_cost = 0
+    for j in self.robot.ordered_joints:
+      if np.abs(j.current_relative_position()[0]) - 1 < 0.01:
+        stuck_joint_cost += -0.1
+
+    object_xy = self.robot.object.pose().xyz()[:2]
+    target_xy = self.robot.target.pose().xyz()[:2]
+
+    if not self.robot._object_hit_ground and self.robot.object.pose().xyz(
+    )[2] < -0.25:  # TODO: Should the object and target really belong to the robot? Maybe split this off
+      self.robot._object_hit_ground = True
+      self.robot._object_hit_location = self.robot.object.pose().xyz()
+
+    if self.robot._object_hit_ground:
+      object_hit_xy = self.robot._object_hit_location[:2]
+      reward_dist = -np.linalg.norm(object_hit_xy - target_xy)
+    else:
+      reward_dist = -np.linalg.norm(object_xy - target_xy)
+    reward_ctrl = -np.square(a).sum()
+
+    self.rewards = [
+        float(self.potential - potential_old),
+        float(electricity_cost),
+        float(stuck_joint_cost), reward_dist, 0.002 * reward_ctrl
+    ]
+    self.HUD(state, a, False)
+    return state, sum(self.rewards), False, {}
+
+  def camera_adjust(self):
+    x, y, z = self.robot.fingertip.pose().xyz()
+    x *= 0.5
+    y *= 0.5
+    self.camera.move_and_look_at(0.3, 0.3, 0.3, x, y, z)
diff --git a/examples/pybullet/gym/pybullet_envs/gym_pendulum_envs.py b/examples/pybullet/gym/pybullet_envs/gym_pendulum_envs.py
index 07924c453..310874de8 100644
--- a/examples/pybullet/gym/pybullet_envs/gym_pendulum_envs.py
+++ b/examples/pybullet/gym/pybullet_envs/gym_pendulum_envs.py
@@ -3,83 +3,90 @@ from .env_bases import MJCFBaseBulletEnv
 from robot_pendula import InvertedPendulum, InvertedPendulumSwingup, InvertedDoublePendulum
 import gym, gym.spaces, gym.utils, gym.utils.seeding
 import numpy as np
-import pybullet 
+import pybullet
 import os, sys
 
+
 class InvertedPendulumBulletEnv(MJCFBaseBulletEnv):
-	def __init__(self):
-		self.robot = InvertedPendulum()
-		MJCFBaseBulletEnv.__init__(self, self.robot)
-		self.stateId=-1
-
-	def create_single_player_scene(self, bullet_client):
-		return SingleRobotEmptyScene(bullet_client, gravity=9.8, timestep=0.0165, frame_skip=1)
-
-	def reset(self):
-		if (self.stateId>=0):
-			#print("InvertedPendulumBulletEnv reset p.restoreState(",self.stateId,")")
-			self._p.restoreState(self.stateId)
-		r = MJCFBaseBulletEnv.reset(self)
-		if (self.stateId<0):
-			self.stateId = self._p.saveState()
-			#print("InvertedPendulumBulletEnv reset self.stateId=",self.stateId)
-		return r
-	
-	def step(self, a):
-		self.robot.apply_action(a)
-		self.scene.global_step()
-		state = self.robot.calc_state()  # sets self.pos_x self.pos_y
-		vel_penalty = 0
-		if self.robot.swingup:
-			reward = np.cos(self.robot.theta)
-			done = False
-		else:
-			reward = 1.0
-			done = np.abs(self.robot.theta) > .2
-		self.rewards = [float(reward)]
-		self.HUD(state, a, done)
-		return state, sum(self.rewards), done, {}
-
-	def camera_adjust(self):
-		self.camera.move_and_look_at(0,1.2,1.0, 0,0,0.5)
+
+  def __init__(self):
+    self.robot = InvertedPendulum()
+    MJCFBaseBulletEnv.__init__(self, self.robot)
+    self.stateId = -1
+
+  def create_single_player_scene(self, bullet_client):
+    return SingleRobotEmptyScene(bullet_client, gravity=9.8, timestep=0.0165, frame_skip=1)
+
+  def reset(self):
+    if (self.stateId >= 0):
+      #print("InvertedPendulumBulletEnv reset p.restoreState(",self.stateId,")")
+      self._p.restoreState(self.stateId)
+    r = MJCFBaseBulletEnv.reset(self)
+    if (self.stateId < 0):
+      self.stateId = self._p.saveState()
+      #print("InvertedPendulumBulletEnv reset self.stateId=",self.stateId)
+    return r
+
+  def step(self, a):
+    self.robot.apply_action(a)
+    self.scene.global_step()
+    state = self.robot.calc_state()  # sets self.pos_x self.pos_y
+    vel_penalty = 0
+    if self.robot.swingup:
+      reward = np.cos(self.robot.theta)
+      done = False
+    else:
+      reward = 1.0
+      done = np.abs(self.robot.theta) > .2
+    self.rewards = [float(reward)]
+    self.HUD(state, a, done)
+    return state, sum(self.rewards), done, {}
+
+  def camera_adjust(self):
+    self.camera.move_and_look_at(0, 1.2, 1.0, 0, 0, 0.5)
+
 
 class InvertedPendulumSwingupBulletEnv(InvertedPendulumBulletEnv):
-	def __init__(self):
-		self.robot = InvertedPendulumSwingup()
-		MJCFBaseBulletEnv.__init__(self, self.robot)
-		self.stateId=-1
+
+  def __init__(self):
+    self.robot = InvertedPendulumSwingup()
+    MJCFBaseBulletEnv.__init__(self, self.robot)
+    self.stateId = -1
+
 
 class InvertedDoublePendulumBulletEnv(MJCFBaseBulletEnv):
-	def __init__(self):
-		self.robot = InvertedDoublePendulum()
-		MJCFBaseBulletEnv.__init__(self, self.robot)
-		self.stateId = -1
-	def create_single_player_scene(self, bullet_client):
-		return SingleRobotEmptyScene(bullet_client, gravity=9.8, timestep=0.0165, frame_skip=1)
-
-	def reset(self):
-		if (self.stateId>=0):
-			self._p.restoreState(self.stateId)
-		r = MJCFBaseBulletEnv.reset(self)
-		if (self.stateId<0):
-			self.stateId = self._p.saveState()
-		return r
-	
-	def step(self, a):
-		self.robot.apply_action(a)
-		self.scene.global_step()
-		state = self.robot.calc_state()  # sets self.pos_x self.pos_y
-		# upright position: 0.6 (one pole) + 0.6 (second pole) * 0.5 (middle of second pole) = 0.9
-		# using <site> tag in original xml, upright position is 0.6 + 0.6 = 1.2, difference +0.3
-		dist_penalty = 0.01 * self.robot.pos_x ** 2 + (self.robot.pos_y + 0.3 - 2) ** 2
-		# v1, v2 = self.model.data.qvel[1:3]   TODO when this fixed https://github.com/bulletphysics/bullet3/issues/1040
-		#vel_penalty = 1e-3 * v1**2 + 5e-3 * v2**2
-		vel_penalty = 0
-		alive_bonus = 10
-		done = self.robot.pos_y + 0.3 <= 1
-		self.rewards = [float(alive_bonus), float(-dist_penalty), float(-vel_penalty)]
-		self.HUD(state, a, done)
-		return state, sum(self.rewards), done, {}
-
-	def camera_adjust(self):
-		self.camera.move_and_look_at(0,1.2,1.2, 0,0,0.5)
+
+  def __init__(self):
+    self.robot = InvertedDoublePendulum()
+    MJCFBaseBulletEnv.__init__(self, self.robot)
+    self.stateId = -1
+
+  def create_single_player_scene(self, bullet_client):
+    return SingleRobotEmptyScene(bullet_client, gravity=9.8, timestep=0.0165, frame_skip=1)
+
+  def reset(self):
+    if (self.stateId >= 0):
+      self._p.restoreState(self.stateId)
+    r = MJCFBaseBulletEnv.reset(self)
+    if (self.stateId < 0):
+      self.stateId = self._p.saveState()
+    return r
+
+  def step(self, a):
+    self.robot.apply_action(a)
+    self.scene.global_step()
+    state = self.robot.calc_state()  # sets self.pos_x self.pos_y
+    # upright position: 0.6 (one pole) + 0.6 (second pole) * 0.5 (middle of second pole) = 0.9
+    # using <site> tag in original xml, upright position is 0.6 + 0.6 = 1.2, difference +0.3
+    dist_penalty = 0.01 * self.robot.pos_x**2 + (self.robot.pos_y + 0.3 - 2)**2
+    # v1, v2 = self.model.data.qvel[1:3]   TODO when this fixed https://github.com/bulletphysics/bullet3/issues/1040
+    #vel_penalty = 1e-3 * v1**2 + 5e-3 * v2**2
+    vel_penalty = 0
+    alive_bonus = 10
+    done = self.robot.pos_y + 0.3 <= 1
+    self.rewards = [float(alive_bonus), float(-dist_penalty), float(-vel_penalty)]
+    self.HUD(state, a, done)
+    return state, sum(self.rewards), done, {}
+
+  def camera_adjust(self):
+    self.camera.move_and_look_at(0, 1.2, 1.2, 0, 0, 0.5)
diff --git a/examples/pybullet/gym/pybullet_envs/kerasrl_utils.py b/examples/pybullet/gym/pybullet_envs/kerasrl_utils.py
index 1ccedce5c..8045fff6c 100644
--- a/examples/pybullet/gym/pybullet_envs/kerasrl_utils.py
+++ b/examples/pybullet/gym/pybullet_envs/kerasrl_utils.py
@@ -1,28 +1,32 @@
-
 import re
 from gym import error
 import glob
-							#  checkpoints/KerasDDPG-InvertedPendulum-v0-20170701190920_actor.h5
+#  checkpoints/KerasDDPG-InvertedPendulum-v0-20170701190920_actor.h5
 weight_save_re = re.compile(r'^(?:\w+\/)+?(\w+-v\d+)-(\w+-v\d+)-(\d+)(?:_\w+)?\.(\w+)$')
 
+
 def get_fields(weight_save_name):
-	match = weight_save_re.search(weight_save_name)
-	if not match:
-		raise error.Error('Attempted to read a malformed weight save: {}. (Currently all weight saves must be of the form {}.)'.format(id,weight_save_re.pattern))
-	return match.group(1), match.group(2), int(match.group(3))
+  match = weight_save_re.search(weight_save_name)
+  if not match:
+    raise error.Error(
+        'Attempted to read a malformed weight save: {}. (Currently all weight saves must be of the form {}.)'
+        .format(id, weight_save_re.pattern))
+  return match.group(1), match.group(2), int(match.group(3))
+
 
 def get_latest_save(file_folder, agent_name, env_name, version_number):
-	"""
+  """
 	Returns the properties of the latest weight save. The information can be used to generate the loading path
 	:return:
 	"""
-	path = "%s%s"% (file_folder, "*.h5")
-	file_list = glob.glob(path)
-	latest_file_properties = []
-	file_properties = []
-	for f in file_list:
-		file_properties = get_fields(f)
-		if file_properties[0] == agent_name and file_properties[1] == env_name and (latest_file_properties == [] or file_properties[2] > latest_file_properties[2]):
-			latest_file_properties = file_properties
+  path = "%s%s" % (file_folder, "*.h5")
+  file_list = glob.glob(path)
+  latest_file_properties = []
+  file_properties = []
+  for f in file_list:
+    file_properties = get_fields(f)
+    if file_properties[0] == agent_name and file_properties[1] == env_name and (
+        latest_file_properties == [] or file_properties[2] > latest_file_properties[2]):
+      latest_file_properties = file_properties
 
-	return latest_file_properties
+  return latest_file_properties
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/__init__.py
index b28b04f64..8b1378917 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/__init__.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/__init__.py
@@ -1,3 +1 @@
 
-
-
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/__init__.py
index 139597f9c..8b1378917 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/__init__.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/__init__.py
@@ -1,2 +1 @@
 
-
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras.py b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras.py
index 016fee5ea..57b27068f 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras.py
@@ -1,4 +1,3 @@
-
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -22,9 +21,7 @@ def main(unused_argv):
   # fix random seed for reproducibility
   numpy.random.seed(7)
   # load pima indians dataset
-  dataset = numpy.loadtxt(
-      FLAGS.input_filename,
-      delimiter=",")
+  dataset = numpy.loadtxt(FLAGS.input_filename, delimiter=",")
   # split into input (X) and output (Y) variables
   x = dataset[:, 0:3]
   y = dataset[:, 3]
@@ -38,28 +35,27 @@ def main(unused_argv):
   model.add(Dense(1, activation="linear"))
 
   # Compile model (use adam or sgd)
-  model.compile(
-      loss="mean_squared_error",
-      optimizer="adam",
-      metrics=["mean_squared_error"])
+  model.compile(loss="mean_squared_error", optimizer="adam", metrics=["mean_squared_error"])
 
   # checkpoint
   filepath = "/tmp/keras/weights-improvement-{epoch:02d}-{val_loss:.2f}.hdf5"
-  checkpoint = ModelCheckpoint(
-      filepath, monitor="val_loss", verbose=1, save_best_only=True, mode="min")
+  checkpoint = ModelCheckpoint(filepath,
+                               monitor="val_loss",
+                               verbose=1,
+                               save_best_only=True,
+                               mode="min")
   callbacks_list = [checkpoint]
 
   # Fit the model
   # model.fit(X, Y, epochs=150, batch_size=10)
   # model.fit(X, Y, epochs=150, batch_size=10, callbacks=callbacks_list)
-  model.fit(
-      x,
-      y,
-      validation_split=0.34,
-      epochs=4500,
-      batch_size=1024,
-      callbacks=callbacks_list,
-      verbose=0)
+  model.fit(x,
+            y,
+            validation_split=0.34,
+            epochs=4500,
+            batch_size=1024,
+            callbacks=callbacks_list,
+            verbose=0)
 
   # evaluate the model
   scores = model.evaluate(x, y)
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras_lstm.py b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras_lstm.py
index f3a45a1dd..35f7a16a1 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras_lstm.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras_lstm.py
@@ -75,8 +75,8 @@ reframed = series_to_supervised(scaled, lag_steps, 1)
 print("reframed before drop=", reframed)
 
 # drop columns we don't want to predict
-reframed.drop(reframed.columns[[3,7,11,15,19]], axis=1, inplace=True)
-print("after drop=",reframed.head())
+reframed.drop(reframed.columns[[3, 7, 11, 15, 19]], axis=1, inplace=True)
+print("after drop=", reframed.head())
 
 #dummy = scaler.inverse_transform(reframed)
 #print(dummy)
@@ -104,17 +104,17 @@ test = values[n_train_hours:, :]
 train_X, train_y = train[:, :-1], train[:, -1]
 test_X, test_y = test[:, :-1], test[:, -1]
 
-print("train_X.shape[1]=",train_X.shape[1])
-
+print("train_X.shape[1]=", train_X.shape[1])
 
 # design network
-useLSTM=True
+useLSTM = True
 if useLSTM:
   # reshape input to be 3D [samples, timesteps, features]
-  train_X = train_X.reshape((train_X.shape[0], lag_steps+1, int(train_X.shape[1]/(lag_steps+1))))
-  test_X = test_X.reshape((test_X.shape[0], lag_steps+1, int(test_X.shape[1]/(lag_steps+1))))
+  train_X = train_X.reshape(
+      (train_X.shape[0], lag_steps + 1, int(train_X.shape[1] / (lag_steps + 1))))
+  test_X = test_X.reshape((test_X.shape[0], lag_steps + 1, int(test_X.shape[1] / (lag_steps + 1))))
   model = Sequential()
-  model.add(LSTM(40,  input_shape=(train_X.shape[1], train_X.shape[2])))
+  model.add(LSTM(40, input_shape=(train_X.shape[1], train_X.shape[2])))
   model.add(Dropout(0.05))
   model.add(Dense(8, activation='sigmoid'))
   model.add(Dense(8, activation='sigmoid'))
@@ -128,39 +128,37 @@ else:
   model.add(Dense(1, activation="linear"))
 
 #model.compile(loss='mae', optimizer='adam')
-model.compile(
-    loss='mean_squared_error', optimizer='adam', metrics=['mean_squared_error'])
+model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mean_squared_error'])
 
 # checkpoint
 filepath = '/tmp/keras/weights-improvement-{epoch:02d}-{val_loss:.2f}.hdf5'
-checkpoint = ModelCheckpoint(
-    filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
+checkpoint = ModelCheckpoint(filepath,
+                             monitor='val_loss',
+                             verbose=1,
+                             save_best_only=True,
+                             mode='min')
 callbacks_list = [checkpoint]
 
 # fit network
-history = model.fit(
-    train_X,
-    train_y,
-    epochs=1500,
-    batch_size=32,
-    callbacks=callbacks_list,
-    validation_data=(test_X, test_y),
-    verbose=2,
-    shuffle=False)
+history = model.fit(train_X,
+                    train_y,
+                    epochs=1500,
+                    batch_size=32,
+                    callbacks=callbacks_list,
+                    validation_data=(test_X, test_y),
+                    verbose=2,
+                    shuffle=False)
 # plot history
 
-
-data = np.array([[[1.513535008329887299,3.234624992847829894e-01,1.731481043119239782,1.741165415165205399,
-1.534267104753672228e+00,1.071354965017878635e+00,1.712386127673626302e+00]]])
-
+data = np.array([[[
+    1.513535008329887299, 3.234624992847829894e-01, 1.731481043119239782, 1.741165415165205399,
+    1.534267104753672228e+00, 1.071354965017878635e+00, 1.712386127673626302e+00
+]]])
 
 #prediction = model.predict(data)
 #print("prediction=",prediction)
 
-
 pyplot.plot(history.history['loss'], label='train')
 pyplot.plot(history.history['val_loss'], label='test')
 pyplot.legend()
 pyplot.show()
-
-
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/minitaur_raibert_controller_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/minitaur_raibert_controller_example.py
index 0be299a75..40e6b4799 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/minitaur_raibert_controller_example.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/minitaur_raibert_controller_example.py
@@ -15,9 +15,8 @@ FLAGS = tf.app.flags.FLAGS
 
 flags.DEFINE_float("motor_kp", 1.0, "The position gain of the motor.")
 flags.DEFINE_float("motor_kd", 0.015, "The speed gain of the motor.")
-flags.DEFINE_float(
-    "control_latency", 0.006, "The latency between sensor measurement and action"
-    " execution the robot.")
+flags.DEFINE_float("control_latency", 0.006, "The latency between sensor measurement and action"
+                   " execution the robot.")
 flags.DEFINE_string("log_path", ".", "The directory to write the log file.")
 
 
@@ -31,37 +30,34 @@ def speed(t):
 
 
 def main(argv):
-    del argv
-    env = minitaur_gym_env.MinitaurGymEnv(
-        urdf_version=minitaur_gym_env.RAINBOW_DASH_V0_URDF_VERSION,
-        control_time_step=0.006,
-        action_repeat=6,
-        pd_latency=0.0,
-        control_latency=FLAGS.control_latency,
-        motor_kp=FLAGS.motor_kp,
-        motor_kd=FLAGS.motor_kd,
-        remove_default_joint_damping=True,
-        leg_model_enabled=False,
-        render=True,
-        on_rack=False,
-        accurate_motor_model_enabled=True,
-        log_path=FLAGS.log_path)
-    env.reset()
+  del argv
+  env = minitaur_gym_env.MinitaurGymEnv(urdf_version=minitaur_gym_env.RAINBOW_DASH_V0_URDF_VERSION,
+                                        control_time_step=0.006,
+                                        action_repeat=6,
+                                        pd_latency=0.0,
+                                        control_latency=FLAGS.control_latency,
+                                        motor_kp=FLAGS.motor_kp,
+                                        motor_kd=FLAGS.motor_kd,
+                                        remove_default_joint_damping=True,
+                                        leg_model_enabled=False,
+                                        render=True,
+                                        on_rack=False,
+                                        accurate_motor_model_enabled=True,
+                                        log_path=FLAGS.log_path)
+  env.reset()
+
+  controller = minitaur_raibert_controller.MinitaurRaibertTrottingController(env.minitaur)
+
+  tstart = env.minitaur.GetTimeSinceReset()
+  for _ in range(1000):
+    t = env.minitaur.GetTimeSinceReset() - tstart
+    controller.behavior_parameters = (minitaur_raibert_controller.BehaviorParameters(
+        desired_forward_speed=speed(t)))
+    controller.update(t)
+    env.step(controller.get_action())
+
+  #env.close()
 
-    controller = minitaur_raibert_controller.MinitaurRaibertTrottingController(
-        env.minitaur)
-
-    tstart = env.minitaur.GetTimeSinceReset()
-    for _ in range(1000):
-      t = env.minitaur.GetTimeSinceReset() - tstart
-      controller.behavior_parameters = (
-          minitaur_raibert_controller.BehaviorParameters(
-              desired_forward_speed=speed(t)))
-      controller.update(t)
-      env.step(controller.get_action())
-
-    #env.close()
 
 if __name__ == "__main__":
   tf.app.run(main)
-
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/proto2csv.py b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/proto2csv.py
index 893c831e0..2e04313e8 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/proto2csv.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/proto2csv.py
@@ -35,12 +35,11 @@ def main(argv):
       #print("motorState.velocity=",motorState.velocity)
       #print("motorState.action=",motorState.action)
       #print("motorState.torque=",motorState.torque)
-      recs.append([motorState.angle,motorState.velocity,motorState.action,motorState.torque])
-		
+      recs.append([motorState.angle, motorState.velocity, motorState.action, motorState.torque])
+
   a = numpy.array(recs)
   numpy.savetxt(FLAGS.csv_file, a, delimiter=",")
 
+
 if __name__ == "__main__":
   tf.app.run(main)
-
-
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/__init__.py
index 8d1c8b69c..8b1378917 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/__init__.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/__init__.py
@@ -1 +1 @@
- 
+
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/__init__.py
index 26a87baf9..6f4c222dd 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/__init__.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/__init__.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Proximal Policy Optimization algorithm."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/algorithm.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/algorithm.py
index 81b18b7b4..032eac760 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/algorithm.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/algorithm.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Proximal Policy Optimization algorithm.
 
 Based on John Schulman's implementation in Python and Theano:
@@ -30,9 +29,7 @@ from . import memory
 from . import normalize
 from . import utility
 
-
-_NetworkOutput = collections.namedtuple(
-    'NetworkOutput', 'policy, mean, logstd, value, state')
+_NetworkOutput = collections.namedtuple('NetworkOutput', 'policy, mean, logstd, value, state')
 
 
 class PPOAlgorithm(object):
@@ -53,44 +50,46 @@ class PPOAlgorithm(object):
     self._is_training = is_training
     self._should_log = should_log
     self._config = config
-    self._observ_filter = normalize.StreamingNormalize(
-        self._batch_env.observ[0], center=True, scale=True, clip=5,
-        name='normalize_observ')
-    self._reward_filter = normalize.StreamingNormalize(
-        self._batch_env.reward[0], center=False, scale=True, clip=10,
-        name='normalize_reward')
+    self._observ_filter = normalize.StreamingNormalize(self._batch_env.observ[0],
+                                                       center=True,
+                                                       scale=True,
+                                                       clip=5,
+                                                       name='normalize_observ')
+    self._reward_filter = normalize.StreamingNormalize(self._batch_env.reward[0],
+                                                       center=False,
+                                                       scale=True,
+                                                       clip=10,
+                                                       name='normalize_reward')
     # Memory stores tuple of observ, action, mean, logstd, reward.
-    template = (
-        self._batch_env.observ[0], self._batch_env.action[0],
-        self._batch_env.action[0], self._batch_env.action[0],
-        self._batch_env.reward[0])
-    self._memory = memory.EpisodeMemory(
-        template, config.update_every, config.max_length, 'memory')
+    template = (self._batch_env.observ[0], self._batch_env.action[0], self._batch_env.action[0],
+                self._batch_env.action[0], self._batch_env.reward[0])
+    self._memory = memory.EpisodeMemory(template, config.update_every, config.max_length, 'memory')
     self._memory_index = tf.Variable(0, False)
     use_gpu = self._config.use_gpu and utility.available_gpus()
     with tf.device('/gpu:0' if use_gpu else '/cpu:0'):
       # Create network variables for later calls to reuse.
-      self._network(
-          tf.zeros_like(self._batch_env.observ)[:, None],
-          tf.ones(len(self._batch_env)), reuse=None)
+      self._network(tf.zeros_like(self._batch_env.observ)[:, None],
+                    tf.ones(len(self._batch_env)),
+                    reuse=None)
       cell = self._config.network(self._batch_env.action.shape[1].value)
       with tf.variable_scope('ppo_temporary'):
-        self._episodes = memory.EpisodeMemory(
-            template, len(batch_env), config.max_length, 'episodes')
-        self._last_state = utility.create_nested_vars(
-            cell.zero_state(len(batch_env), tf.float32))
-        self._last_action = tf.Variable(
-            tf.zeros_like(self._batch_env.action), False, name='last_action')
-        self._last_mean = tf.Variable(
-            tf.zeros_like(self._batch_env.action), False, name='last_mean')
-        self._last_logstd = tf.Variable(
-            tf.zeros_like(self._batch_env.action), False, name='last_logstd')
-    self._penalty = tf.Variable(
-        self._config.kl_init_penalty, False, dtype=tf.float32)
-    self._policy_optimizer = self._config.policy_optimizer(
-        self._config.policy_lr, name='policy_optimizer')
-    self._value_optimizer = self._config.value_optimizer(
-        self._config.value_lr, name='value_optimizer')
+        self._episodes = memory.EpisodeMemory(template, len(batch_env), config.max_length,
+                                              'episodes')
+        self._last_state = utility.create_nested_vars(cell.zero_state(len(batch_env), tf.float32))
+        self._last_action = tf.Variable(tf.zeros_like(self._batch_env.action),
+                                        False,
+                                        name='last_action')
+        self._last_mean = tf.Variable(tf.zeros_like(self._batch_env.action),
+                                      False,
+                                      name='last_mean')
+        self._last_logstd = tf.Variable(tf.zeros_like(self._batch_env.action),
+                                        False,
+                                        name='last_logstd')
+    self._penalty = tf.Variable(self._config.kl_init_penalty, False, dtype=tf.float32)
+    self._policy_optimizer = self._config.policy_optimizer(self._config.policy_lr,
+                                                           name='policy_optimizer')
+    self._value_optimizer = self._config.value_optimizer(self._config.value_lr,
+                                                         name='value_optimizer')
 
   def begin_episode(self, agent_indices):
     """Reset the recurrent states and stored episode.
@@ -118,23 +117,24 @@ class PPOAlgorithm(object):
     """
     with tf.name_scope('perform/'):
       observ = self._observ_filter.transform(observ)
-      network = self._network(
-          observ[:, None], tf.ones(observ.shape[0]), self._last_state)
-      action = tf.cond(
-          self._is_training, network.policy.sample, lambda: network.mean)
+      network = self._network(observ[:, None], tf.ones(observ.shape[0]), self._last_state)
+      action = tf.cond(self._is_training, network.policy.sample, lambda: network.mean)
       logprob = network.policy.log_prob(action)[:, 0]
       # pylint: disable=g-long-lambda
-      summary = tf.cond(self._should_log, lambda: tf.summary.merge([
-          tf.summary.histogram('mean', network.mean[:, 0]),
-          tf.summary.histogram('std', tf.exp(network.logstd[:, 0])),
-          tf.summary.histogram('action', action[:, 0]),
-          tf.summary.histogram('logprob', logprob)]), str)
+      summary = tf.cond(
+          self._should_log, lambda: tf.summary.merge([
+              tf.summary.histogram('mean', network.mean[:, 0]),
+              tf.summary.histogram('std', tf.exp(network.logstd[:, 0])),
+              tf.summary.histogram('action', action[:, 0]),
+              tf.summary.histogram('logprob', logprob)
+          ]), str)
       # Remember current policy to append to memory in the experience callback.
       with tf.control_dependencies([
           utility.assign_nested_vars(self._last_state, network.state),
           self._last_action.assign(action[:, 0]),
           self._last_mean.assign(network.mean[:, 0]),
-          self._last_logstd.assign(network.logstd[:, 0])]):
+          self._last_logstd.assign(network.logstd[:, 0])
+      ]):
         return tf.check_numerics(action[:, 0], 'action'), tf.identity(summary)
 
   def experience(self, observ, action, reward, unused_done, unused_nextob):
@@ -155,15 +155,14 @@ class PPOAlgorithm(object):
       Summary tensor.
     """
     with tf.name_scope('experience/'):
-      return tf.cond(
-          self._is_training,
-          lambda: self._define_experience(observ, action, reward), str)
+      return tf.cond(self._is_training, lambda: self._define_experience(observ, action, reward),
+                     str)
 
   def _define_experience(self, observ, action, reward):
     """Implement the branch of experience() entered during training."""
-    update_filters = tf.summary.merge([
-        self._observ_filter.update(observ),
-        self._reward_filter.update(reward)])
+    update_filters = tf.summary.merge(
+        [self._observ_filter.update(observ),
+         self._reward_filter.update(reward)])
     with tf.control_dependencies([update_filters]):
       if self._config.train_on_agent_action:
         # NOTE: Doesn't seem to change much.
@@ -174,14 +173,16 @@ class PPOAlgorithm(object):
       norm_observ = self._observ_filter.transform(observ)
       norm_reward = tf.reduce_mean(self._reward_filter.transform(reward))
       # pylint: disable=g-long-lambda
-      summary = tf.cond(self._should_log, lambda: tf.summary.merge([
-          update_filters,
-          self._observ_filter.summary(),
-          self._reward_filter.summary(),
-          tf.summary.scalar('memory_size', self._memory_index),
-          tf.summary.histogram('normalized_observ', norm_observ),
-          tf.summary.histogram('action', self._last_action),
-          tf.summary.scalar('normalized_reward', norm_reward)]), str)
+      summary = tf.cond(
+          self._should_log, lambda: tf.summary.merge([
+              update_filters,
+              self._observ_filter.summary(),
+              self._reward_filter.summary(),
+              tf.summary.scalar('memory_size', self._memory_index),
+              tf.summary.histogram('normalized_observ', norm_observ),
+              tf.summary.histogram('action', self._last_action),
+              tf.summary.scalar('normalized_reward', norm_reward)
+          ]), str)
       return summary
 
   def end_episode(self, agent_indices):
@@ -199,20 +200,16 @@ class PPOAlgorithm(object):
        Summary tensor.
     """
     with tf.name_scope('end_episode/'):
-      return tf.cond(
-          self._is_training,
-          lambda: self._define_end_episode(agent_indices), str)
+      return tf.cond(self._is_training, lambda: self._define_end_episode(agent_indices), str)
 
   def _define_end_episode(self, agent_indices):
     """Implement the branch of end_episode() entered during training."""
     episodes, length = self._episodes.data(agent_indices)
     space_left = self._config.update_every - self._memory_index
-    use_episodes = tf.range(tf.minimum(
-        tf.shape(agent_indices)[0], space_left))
+    use_episodes = tf.range(tf.minimum(tf.shape(agent_indices)[0], space_left))
     episodes = [tf.gather(elem, use_episodes) for elem in episodes]
-    append = self._memory.replace(
-        episodes, tf.gather(length, use_episodes),
-        use_episodes + self._memory_index)
+    append = self._memory.replace(episodes, tf.gather(length, use_episodes),
+                                  use_episodes + self._memory_index)
     with tf.control_dependencies([append]):
       inc_index = self._memory_index.assign_add(tf.shape(use_episodes)[0])
     with tf.control_dependencies([inc_index]):
@@ -229,8 +226,7 @@ class PPOAlgorithm(object):
       Summary tensor.
     """
     with tf.name_scope('training'):
-      assert_full = tf.assert_equal(
-          self._memory_index, self._config.update_every)
+      assert_full = tf.assert_equal(self._memory_index, self._config.update_every)
       with tf.control_dependencies([assert_full]):
         data = self._memory.data()
       (observ, action, old_mean, old_logstd, reward), length = data
@@ -238,21 +234,17 @@ class PPOAlgorithm(object):
         length = tf.identity(length)
       observ = self._observ_filter.transform(observ)
       reward = self._reward_filter.transform(reward)
-      policy_summary = self._update_policy(
-          observ, action, old_mean, old_logstd, reward, length)
+      policy_summary = self._update_policy(observ, action, old_mean, old_logstd, reward, length)
       with tf.control_dependencies([policy_summary]):
         value_summary = self._update_value(observ, reward, length)
       with tf.control_dependencies([value_summary]):
-        penalty_summary = self._adjust_penalty(
-            observ, old_mean, old_logstd, length)
+        penalty_summary = self._adjust_penalty(observ, old_mean, old_logstd, length)
       with tf.control_dependencies([penalty_summary]):
-        clear_memory = tf.group(
-            self._memory.clear(), self._memory_index.assign(0))
+        clear_memory = tf.group(self._memory.clear(), self._memory_index.assign(0))
       with tf.control_dependencies([clear_memory]):
-        weight_summary = utility.variable_summaries(
-            tf.trainable_variables(), self._config.weight_summaries)
-        return tf.summary.merge([
-            policy_summary, value_summary, penalty_summary, weight_summary])
+        weight_summary = utility.variable_summaries(tf.trainable_variables(),
+                                                    self._config.weight_summaries)
+        return tf.summary.merge([policy_summary, value_summary, penalty_summary, weight_summary])
 
   def _update_value(self, observ, reward, length):
     """Perform multiple update steps of the value baseline.
@@ -269,10 +261,9 @@ class PPOAlgorithm(object):
       Summary tensor.
     """
     with tf.name_scope('update_value'):
-      loss, summary = tf.scan(
-          lambda _1, _2: self._update_value_step(observ, reward, length),
-          tf.range(self._config.update_epochs_value),
-          [0., ''], parallel_iterations=1)
+      loss, summary = tf.scan(lambda _1, _2: self._update_value_step(observ, reward, length),
+                              tf.range(self._config.update_epochs_value), [0., ''],
+                              parallel_iterations=1)
       print_loss = tf.Print(0, [tf.reduce_mean(loss)], 'value loss: ')
       with tf.control_dependencies([loss, print_loss]):
         return summary[self._config.update_epochs_value // 2]
@@ -289,15 +280,13 @@ class PPOAlgorithm(object):
       Tuple of loss tensor and summary tensor.
     """
     loss, summary = self._value_loss(observ, reward, length)
-    gradients, variables = (
-        zip(*self._value_optimizer.compute_gradients(loss)))
-    optimize = self._value_optimizer.apply_gradients(
-        zip(gradients, variables))
+    gradients, variables = (zip(*self._value_optimizer.compute_gradients(loss)))
+    optimize = self._value_optimizer.apply_gradients(zip(gradients, variables))
     summary = tf.summary.merge([
         summary,
         tf.summary.scalar('gradient_norm', tf.global_norm(gradients)),
-        utility.gradient_summaries(
-            zip(gradients, variables), dict(value=r'.*'))])
+        utility.gradient_summaries(zip(gradients, variables), dict(value=r'.*'))
+    ])
     with tf.control_dependencies([optimize]):
       return [tf.identity(loss), tf.identity(summary)]
 
@@ -317,18 +306,17 @@ class PPOAlgorithm(object):
     """
     with tf.name_scope('value_loss'):
       value = self._network(observ, length).value
-      return_ = utility.discounted_return(
-          reward, length, self._config.discount)
+      return_ = utility.discounted_return(reward, length, self._config.discount)
       advantage = return_ - value
-      value_loss = 0.5 * self._mask(advantage ** 2, length)
+      value_loss = 0.5 * self._mask(advantage**2, length)
       summary = tf.summary.merge([
           tf.summary.histogram('value_loss', value_loss),
-          tf.summary.scalar('avg_value_loss', tf.reduce_mean(value_loss))])
+          tf.summary.scalar('avg_value_loss', tf.reduce_mean(value_loss))
+      ])
       value_loss = tf.reduce_mean(value_loss)
       return tf.check_numerics(value_loss, 'value_loss'), summary
 
-  def _update_policy(
-      self, observ, action, old_mean, old_logstd, reward, length):
+  def _update_policy(self, observ, action, old_mean, old_logstd, reward, length):
     """Perform multiple update steps of the policy.
 
     The advantage is computed once at the beginning and shared across
@@ -347,35 +335,28 @@ class PPOAlgorithm(object):
       Summary tensor.
     """
     with tf.name_scope('update_policy'):
-      return_ = utility.discounted_return(
-          reward, length, self._config.discount)
+      return_ = utility.discounted_return(reward, length, self._config.discount)
       value = self._network(observ, length).value
       if self._config.gae_lambda:
-        advantage = utility.lambda_return(
-            reward, value, length, self._config.discount,
-            self._config.gae_lambda)
+        advantage = utility.lambda_return(reward, value, length, self._config.discount,
+                                          self._config.gae_lambda)
       else:
         advantage = return_ - value
       mean, variance = tf.nn.moments(advantage, axes=[0, 1], keep_dims=True)
       advantage = (advantage - mean) / (tf.sqrt(variance) + 1e-8)
       advantage = tf.Print(
-          advantage, [tf.reduce_mean(return_), tf.reduce_mean(value)],
-          'return and value: ')
-      advantage = tf.Print(
-          advantage, [tf.reduce_mean(advantage)],
-          'normalized advantage: ')
+          advantage, [tf.reduce_mean(return_), tf.reduce_mean(value)], 'return and value: ')
+      advantage = tf.Print(advantage, [tf.reduce_mean(advantage)], 'normalized advantage: ')
       # pylint: disable=g-long-lambda
-      loss, summary = tf.scan(
-          lambda _1, _2: self._update_policy_step(
-              observ, action, old_mean, old_logstd, advantage, length),
-          tf.range(self._config.update_epochs_policy),
-          [0., ''], parallel_iterations=1)
+      loss, summary = tf.scan(lambda _1, _2: self._update_policy_step(
+          observ, action, old_mean, old_logstd, advantage, length),
+                              tf.range(self._config.update_epochs_policy), [0., ''],
+                              parallel_iterations=1)
       print_loss = tf.Print(0, [tf.reduce_mean(loss)], 'policy loss: ')
       with tf.control_dependencies([loss, print_loss]):
         return summary[self._config.update_epochs_policy // 2]
 
-  def _update_policy_step(
-      self, observ, action, old_mean, old_logstd, advantage, length):
+  def _update_policy_step(self, observ, action, old_mean, old_logstd, advantage, length):
     """Compute the current policy loss and perform a gradient update step.
 
     Args:
@@ -390,23 +371,19 @@ class PPOAlgorithm(object):
       Tuple of loss tensor and summary tensor.
     """
     network = self._network(observ, length)
-    loss, summary = self._policy_loss(
-        network.mean, network.logstd, old_mean, old_logstd, action,
-        advantage, length)
-    gradients, variables = (
-        zip(*self._policy_optimizer.compute_gradients(loss)))
-    optimize = self._policy_optimizer.apply_gradients(
-        zip(gradients, variables))
+    loss, summary = self._policy_loss(network.mean, network.logstd, old_mean, old_logstd, action,
+                                      advantage, length)
+    gradients, variables = (zip(*self._policy_optimizer.compute_gradients(loss)))
+    optimize = self._policy_optimizer.apply_gradients(zip(gradients, variables))
     summary = tf.summary.merge([
         summary,
         tf.summary.scalar('gradient_norm', tf.global_norm(gradients)),
-        utility.gradient_summaries(
-            zip(gradients, variables), dict(policy=r'.*'))])
+        utility.gradient_summaries(zip(gradients, variables), dict(policy=r'.*'))
+    ])
     with tf.control_dependencies([optimize]):
       return [tf.identity(loss), tf.identity(summary)]
 
-  def _policy_loss(
-      self, mean, logstd, old_mean, old_logstd, action, advantage, length):
+  def _policy_loss(self, mean, logstd, old_mean, old_logstd, action, advantage, length):
     """Compute the policy loss composed of multiple components.
 
     1. The policy gradient loss is importance sampled from the data-collecting
@@ -430,24 +407,20 @@ class PPOAlgorithm(object):
     """
     with tf.name_scope('policy_loss'):
       entropy = utility.diag_normal_entropy(mean, logstd)
-      kl = tf.reduce_mean(self._mask(utility.diag_normal_kl(
-          old_mean, old_logstd, mean, logstd), length), 1)
+      kl = tf.reduce_mean(
+          self._mask(utility.diag_normal_kl(old_mean, old_logstd, mean, logstd), length), 1)
       policy_gradient = tf.exp(
           utility.diag_normal_logpdf(mean, logstd, action) -
           utility.diag_normal_logpdf(old_mean, old_logstd, action))
-      surrogate_loss = -tf.reduce_mean(self._mask(
-          policy_gradient * tf.stop_gradient(advantage), length), 1)
+      surrogate_loss = -tf.reduce_mean(
+          self._mask(policy_gradient * tf.stop_gradient(advantage), length), 1)
       kl_penalty = self._penalty * kl
       cutoff_threshold = self._config.kl_target * self._config.kl_cutoff_factor
-      cutoff_count = tf.reduce_sum(
-          tf.cast(kl > cutoff_threshold, tf.int32))
-      with tf.control_dependencies([tf.cond(
-          cutoff_count > 0,
-          lambda: tf.Print(0, [cutoff_count], 'kl cutoff! '), int)]):
-        kl_cutoff = (
-            self._config.kl_cutoff_coef *
-            tf.cast(kl > cutoff_threshold, tf.float32) *
-            (kl - cutoff_threshold) ** 2)
+      cutoff_count = tf.reduce_sum(tf.cast(kl > cutoff_threshold, tf.int32))
+      with tf.control_dependencies(
+          [tf.cond(cutoff_count > 0, lambda: tf.Print(0, [cutoff_count], 'kl cutoff! '), int)]):
+        kl_cutoff = (self._config.kl_cutoff_coef * tf.cast(kl > cutoff_threshold, tf.float32) *
+                     (kl - cutoff_threshold)**2)
       policy_loss = surrogate_loss + kl_penalty + kl_cutoff
       summary = tf.summary.merge([
           tf.summary.histogram('entropy', entropy),
@@ -459,7 +432,8 @@ class PPOAlgorithm(object):
           tf.summary.histogram('policy_loss', policy_loss),
           tf.summary.scalar('avg_surr_loss', tf.reduce_mean(surrogate_loss)),
           tf.summary.scalar('avg_kl_penalty', tf.reduce_mean(kl_penalty)),
-          tf.summary.scalar('avg_policy_loss', tf.reduce_mean(policy_loss))])
+          tf.summary.scalar('avg_policy_loss', tf.reduce_mean(policy_loss))
+      ])
       policy_loss = tf.reduce_mean(policy_loss, 0)
       return tf.check_numerics(policy_loss, 'policy_loss'), summary
 
@@ -481,30 +455,30 @@ class PPOAlgorithm(object):
     """
     with tf.name_scope('adjust_penalty'):
       network = self._network(observ, length)
-      assert_change = tf.assert_equal(
-          tf.reduce_all(tf.equal(network.mean, old_mean)), False,
-          message='policy should change')
+      assert_change = tf.assert_equal(tf.reduce_all(tf.equal(network.mean, old_mean)),
+                                      False,
+                                      message='policy should change')
       print_penalty = tf.Print(0, [self._penalty], 'current penalty: ')
       with tf.control_dependencies([assert_change, print_penalty]):
-        kl_change = tf.reduce_mean(self._mask(utility.diag_normal_kl(
-            old_mean, old_logstd, network.mean, network.logstd), length))
+        kl_change = tf.reduce_mean(
+            self._mask(utility.diag_normal_kl(old_mean, old_logstd, network.mean, network.logstd),
+                       length))
         kl_change = tf.Print(kl_change, [kl_change], 'kl change: ')
         maybe_increase = tf.cond(
             kl_change > 1.3 * self._config.kl_target,
             # pylint: disable=g-long-lambda
-            lambda: tf.Print(self._penalty.assign(
-                self._penalty * 1.5), [0], 'increase penalty '),
+            lambda: tf.Print(self._penalty.assign(self._penalty * 1.5), [0], 'increase penalty '),
             float)
         maybe_decrease = tf.cond(
             kl_change < 0.7 * self._config.kl_target,
             # pylint: disable=g-long-lambda
-            lambda: tf.Print(self._penalty.assign(
-                self._penalty / 1.5), [0], 'decrease penalty '),
+            lambda: tf.Print(self._penalty.assign(self._penalty / 1.5), [0], 'decrease penalty '),
             float)
       with tf.control_dependencies([maybe_increase, maybe_decrease]):
         return tf.summary.merge([
             tf.summary.scalar('kl_change', kl_change),
-            tf.summary.scalar('penalty', self._penalty)])
+            tf.summary.scalar('penalty', self._penalty)
+        ])
 
   def _mask(self, tensor, length):
     """Set padding elements of a batch of sequences to zero.
@@ -548,11 +522,14 @@ class PPOAlgorithm(object):
       with tf.device('/gpu:0' if use_gpu else '/cpu:0'):
         observ = tf.check_numerics(observ, 'observ')
         cell = self._config.network(self._batch_env.action.shape[1].value)
-        (mean, logstd, value), state = tf.nn.dynamic_rnn(
-            cell, observ, length, state, tf.float32, swap_memory=True)
+        (mean, logstd, value), state = tf.nn.dynamic_rnn(cell,
+                                                         observ,
+                                                         length,
+                                                         state,
+                                                         tf.float32,
+                                                         swap_memory=True)
       mean = tf.check_numerics(mean, 'mean')
       logstd = tf.check_numerics(logstd, 'logstd')
       value = tf.check_numerics(value, 'value')
-      policy = tf.contrib.distributions.MultivariateNormalDiag(
-          mean, tf.exp(logstd))
+      policy = tf.contrib.distributions.MultivariateNormalDiag(mean, tf.exp(logstd))
       return _NetworkOutput(policy, mean, logstd, value, state)
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/memory.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/memory.py
index 81e0f73d7..37a7c7f53 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/memory.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/memory.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Memory that stores episodes."""
 
 from __future__ import absolute_import
@@ -43,10 +42,9 @@ class EpisodeMemory(object):
       self._scope = scope
       self._length = tf.Variable(tf.zeros(capacity, tf.int32), False)
       self._buffers = [
-          tf.Variable(tf.zeros(
-              [capacity, max_length] + elem.shape.as_list(),
-              elem.dtype), False)
-          for elem in template]
+          tf.Variable(tf.zeros([capacity, max_length] + elem.shape.as_list(), elem.dtype), False)
+          for elem in template
+      ]
 
   def length(self, rows=None):
     """Tensor holding the current length of episodes.
@@ -72,13 +70,11 @@ class EpisodeMemory(object):
     """
     rows = tf.range(self._capacity) if rows is None else rows
     assert rows.shape.ndims == 1
-    assert_capacity = tf.assert_less(
-        rows, self._capacity,
-        message='capacity exceeded')
+    assert_capacity = tf.assert_less(rows, self._capacity, message='capacity exceeded')
     with tf.control_dependencies([assert_capacity]):
-      assert_max_length = tf.assert_less(
-          tf.gather(self._length, rows), self._max_length,
-          message='max length exceeded')
+      assert_max_length = tf.assert_less(tf.gather(self._length, rows),
+                                         self._max_length,
+                                         message='max length exceeded')
     append_ops = []
     with tf.control_dependencies([assert_max_length]):
       for buffer_, elements in zip(self._buffers, transitions):
@@ -86,8 +82,7 @@ class EpisodeMemory(object):
         indices = tf.stack([rows, timestep], 1)
         append_ops.append(tf.scatter_nd_update(buffer_, indices, elements))
     with tf.control_dependencies(append_ops):
-      episode_mask = tf.reduce_sum(tf.one_hot(
-          rows, self._capacity, dtype=tf.int32), 0)
+      episode_mask = tf.reduce_sum(tf.one_hot(rows, self._capacity, dtype=tf.int32), 0)
       return self._length.assign_add(episode_mask)
 
   def replace(self, episodes, length, rows=None):
@@ -103,11 +98,11 @@ class EpisodeMemory(object):
     """
     rows = tf.range(self._capacity) if rows is None else rows
     assert rows.shape.ndims == 1
-    assert_capacity = tf.assert_less(
-        rows, self._capacity, message='capacity exceeded')
+    assert_capacity = tf.assert_less(rows, self._capacity, message='capacity exceeded')
     with tf.control_dependencies([assert_capacity]):
-      assert_max_length = tf.assert_less_equal(
-          length, self._max_length, message='max length exceeded')
+      assert_max_length = tf.assert_less_equal(length,
+                                               self._max_length,
+                                               message='max length exceeded')
     replace_ops = []
     with tf.control_dependencies([assert_max_length]):
       for buffer_, elements in zip(self._buffers, episodes):
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/normalize.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/normalize.py
index 6c4170519..4de38fd25 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/normalize.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/normalize.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Normalize tensors based on streaming estimates of mean and variance."""
 
 from __future__ import absolute_import
@@ -24,8 +23,7 @@ import tensorflow as tf
 class StreamingNormalize(object):
   """Normalize tensors based on streaming estimates of mean and variance."""
 
-  def __init__(
-      self, template, center=True, scale=True, clip=10, name='normalize'):
+  def __init__(self, template, center=True, scale=True, clip=10, name='normalize'):
     """Normalize tensors based on streaming estimates of mean and variance.
 
     Centering the value, scaling it by the standard deviation, and clipping
@@ -69,8 +67,7 @@ class StreamingNormalize(object):
       if self._scale:
         # We cannot scale before seeing at least two samples.
         value /= tf.cond(
-            self._count > 1, lambda: self._std() + 1e-8,
-            lambda: tf.ones_like(self._var_sum))[None]
+            self._count > 1, lambda: self._std() + 1e-8, lambda: tf.ones_like(self._var_sum))[None]
       if self._clip:
         value = tf.clip_by_value(value, -self._clip, self._clip)
       # Remove batch dimension if necessary.
@@ -97,8 +94,7 @@ class StreamingNormalize(object):
         mean_delta = tf.reduce_sum(value - self._mean[None, ...], 0)
         new_mean = self._mean + mean_delta / step
         new_mean = tf.cond(self._count > 1, lambda: new_mean, lambda: value[0])
-        var_delta = (
-            value - self._mean[None, ...]) * (value - new_mean[None, ...])
+        var_delta = (value - self._mean[None, ...]) * (value - new_mean[None, ...])
         new_var_sum = self._var_sum + tf.reduce_sum(var_delta, 0)
       with tf.control_dependencies([new_mean, new_var_sum]):
         update = self._mean.assign(new_mean), self._var_sum.assign(new_var_sum)
@@ -116,10 +112,8 @@ class StreamingNormalize(object):
       Operation.
     """
     with tf.name_scope(self._name + '/reset'):
-      return tf.group(
-          self._count.assign(0),
-          self._mean.assign(tf.zeros_like(self._mean)),
-          self._var_sum.assign(tf.zeros_like(self._var_sum)))
+      return tf.group(self._count.assign(0), self._mean.assign(tf.zeros_like(self._mean)),
+                      self._var_sum.assign(tf.zeros_like(self._var_sum)))
 
   def summary(self):
     """Summary string of mean and standard deviation.
@@ -128,10 +122,8 @@ class StreamingNormalize(object):
       Summary tensor.
     """
     with tf.name_scope(self._name + '/summary'):
-      mean_summary = tf.cond(
-          self._count > 0, lambda: self._summary('mean', self._mean), str)
-      std_summary = tf.cond(
-          self._count > 1, lambda: self._summary('stddev', self._std()), str)
+      mean_summary = tf.cond(self._count > 0, lambda: self._summary('mean', self._mean), str)
+      std_summary = tf.cond(self._count > 1, lambda: self._summary('stddev', self._std()), str)
       return tf.summary.merge([mean_summary, std_summary])
 
   def _std(self):
@@ -143,10 +135,8 @@ class StreamingNormalize(object):
     Returns:
       Tensor of current variance.
     """
-    variance = tf.cond(
-        self._count > 1,
-        lambda: self._var_sum / tf.cast(self._count - 1, tf.float32),
-        lambda: tf.ones_like(self._var_sum) * float('nan'))
+    variance = tf.cond(self._count > 1, lambda: self._var_sum / tf.cast(
+        self._count - 1, tf.float32), lambda: tf.ones_like(self._var_sum) * float('nan'))
     # The epsilon corrects for small negative variance values caused by
     # the algorithm. It was empirically chosen to work with all environments
     # tested.
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/utility.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/utility.py
index 79645b16b..9f6360404 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/utility.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/ppo/utility.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Utilities for the PPO algorithm."""
 
 from __future__ import absolute_import
@@ -51,8 +50,7 @@ def reinit_nested_vars(variables, indices=None):
     Operation.
   """
   if isinstance(variables, (tuple, list)):
-    return tf.group(*[
-        reinit_nested_vars(variable, indices) for variable in variables])
+    return tf.group(*[reinit_nested_vars(variable, indices) for variable in variables])
   if indices is None:
     return variables.assign(tf.zeros_like(variables))
   else:
@@ -71,9 +69,8 @@ def assign_nested_vars(variables, tensors):
     Operation.
   """
   if isinstance(variables, (tuple, list)):
-    return tf.group(*[
-        assign_nested_vars(variable, tensor)
-        for variable, tensor in zip(variables, tensors)])
+    return tf.group(
+        *[assign_nested_vars(variable, tensor) for variable, tensor in zip(variables, tensors)])
   return variables.assign(tensors)
 
 
@@ -81,10 +78,11 @@ def discounted_return(reward, length, discount):
   """Discounted Monte-Carlo returns."""
   timestep = tf.range(reward.shape[1].value)
   mask = tf.cast(timestep[None, :] < length[:, None], tf.float32)
-  return_ = tf.reverse(tf.transpose(tf.scan(
-      lambda agg, cur: cur + discount * agg,
-      tf.transpose(tf.reverse(mask * reward, [1]), [1, 0]),
-      tf.zeros_like(reward[:, -1]), 1, False), [1, 0]), [1])
+  return_ = tf.reverse(
+      tf.transpose(
+          tf.scan(lambda agg, cur: cur + discount * agg,
+                  tf.transpose(tf.reverse(mask * reward, [1]), [1, 0]),
+                  tf.zeros_like(reward[:, -1]), 1, False), [1, 0]), [1])
   return tf.check_numerics(tf.stop_gradient(return_), 'return')
 
 
@@ -95,9 +93,8 @@ def fixed_step_return(reward, value, length, discount, window):
   return_ = tf.zeros_like(reward)
   for _ in range(window):
     return_ += reward
-    reward = discount * tf.concat(
-        [reward[:, 1:], tf.zeros_like(reward[:, -1:])], 1)
-  return_ += discount ** window * tf.concat(
+    reward = discount * tf.concat([reward[:, 1:], tf.zeros_like(reward[:, -1:])], 1)
+  return_ += discount**window * tf.concat(
       [value[:, window:], tf.zeros_like(value[:, -window:]), 1])
   return tf.check_numerics(tf.stop_gradient(mask * return_), 'return')
 
@@ -109,10 +106,11 @@ def lambda_return(reward, value, length, discount, lambda_):
   sequence = mask * reward + discount * value * (1 - lambda_)
   discount = mask * discount * lambda_
   sequence = tf.stack([sequence, discount], 2)
-  return_ = tf.reverse(tf.transpose(tf.scan(
-      lambda agg, cur: cur[0] + cur[1] * agg,
-      tf.transpose(tf.reverse(sequence, [1]), [1, 2, 0]),
-      tf.zeros_like(value[:, -1]), 1, False), [1, 0]), [1])
+  return_ = tf.reverse(
+      tf.transpose(
+          tf.scan(lambda agg, cur: cur[0] + cur[1] * agg,
+                  tf.transpose(tf.reverse(sequence, [1]), [1, 2, 0]), tf.zeros_like(value[:, -1]),
+                  1, False), [1, 0]), [1])
   return tf.check_numerics(tf.stop_gradient(return_), 'return')
 
 
@@ -122,27 +120,26 @@ def lambda_advantage(reward, value, length, discount):
   mask = tf.cast(timestep[None, :] < length[:, None], tf.float32)
   next_value = tf.concat([value[:, 1:], tf.zeros_like(value[:, -1:])], 1)
   delta = reward + discount * next_value - value
-  advantage = tf.reverse(tf.transpose(tf.scan(
-      lambda agg, cur: cur + discount * agg,
-      tf.transpose(tf.reverse(mask * delta, [1]), [1, 0]),
-      tf.zeros_like(delta[:, -1]), 1, False), [1, 0]), [1])
+  advantage = tf.reverse(
+      tf.transpose(
+          tf.scan(lambda agg, cur: cur + discount * agg,
+                  tf.transpose(tf.reverse(mask * delta, [1]), [1, 0]), tf.zeros_like(delta[:, -1]),
+                  1, False), [1, 0]), [1])
   return tf.check_numerics(tf.stop_gradient(advantage), 'advantage')
 
 
 def diag_normal_kl(mean0, logstd0, mean1, logstd1):
   """Epirical KL divergence of two normals with diagonal covariance."""
   logstd0_2, logstd1_2 = 2 * logstd0, 2 * logstd1
-  return 0.5 * (
-      tf.reduce_sum(tf.exp(logstd0_2 - logstd1_2), -1) +
-      tf.reduce_sum((mean1 - mean0) ** 2 / tf.exp(logstd1_2), -1) +
-      tf.reduce_sum(logstd1_2, -1) - tf.reduce_sum(logstd0_2, -1) -
-      mean0.shape[-1].value)
+  return 0.5 * (tf.reduce_sum(tf.exp(logstd0_2 - logstd1_2), -1) + tf.reduce_sum(
+      (mean1 - mean0)**2 / tf.exp(logstd1_2), -1) + tf.reduce_sum(logstd1_2, -1) -
+                tf.reduce_sum(logstd0_2, -1) - mean0.shape[-1].value)
 
 
 def diag_normal_logpdf(mean, logstd, loc):
   """Log density of a normal with diagonal covariance."""
   constant = -0.5 * (math.log(2 * math.pi) + logstd)
-  value = -0.5 * ((loc - mean) / tf.exp(logstd)) ** 2
+  value = -0.5 * ((loc - mean) / tf.exp(logstd))**2
   return tf.reduce_sum(constant + value, -1)
 
 
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/__init__.py
index 05ee82406..a3980d0f3 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/__init__.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/__init__.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Executable scripts for reinforcement learning."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/configs.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/configs.py
index 669f61827..1ea65948b 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/configs.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/configs.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Example configurations using the PPO algorithm."""
 
 from __future__ import absolute_import
@@ -33,10 +32,7 @@ def default():
   use_gpu = False
   # Network
   network = networks.ForwardGaussianPolicy
-  weight_summaries = dict(
-      all=r'.*',
-      policy=r'.*/policy/.*',
-      value=r'.*/value/.*')
+  weight_summaries = dict(all=r'.*', policy=r'.*/policy/.*', value=r'.*/value/.*')
   policy_layers = 200, 100
   value_layers = 200, 100
   init_mean_factor = 0.05
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/networks.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/networks.py
index fe46e6b8f..d2c22bd0f 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/networks.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/networks.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Networks for the PPO algorithm defined as recurrent cells."""
 
 from __future__ import absolute_import
@@ -20,11 +19,10 @@ from __future__ import print_function
 
 import tensorflow as tf
 
-
-_MEAN_WEIGHTS_INITIALIZER = tf.contrib.layers.variance_scaling_initializer(
-    factor=0.1)
+_MEAN_WEIGHTS_INITIALIZER = tf.contrib.layers.variance_scaling_initializer(factor=0.1)
 _LOGSTD_INITIALIZER = tf.random_normal_initializer(-1, 1e-10)
 
+
 class LinearGaussianPolicy(tf.contrib.rnn.RNNCell):
   """Indepent linear network with a tanh at the end for policy and feedforward network for the value.
 
@@ -56,15 +54,12 @@ class LinearGaussianPolicy(tf.contrib.rnn.RNNCell):
   def __call__(self, observation, state):
     with tf.variable_scope('policy'):
       x = tf.contrib.layers.flatten(observation)
-      mean = tf.contrib.layers.fully_connected(
-          x,
-          self._action_size,
-          tf.tanh,
-          weights_initializer=self._mean_weights_initializer)
-      logstd = tf.get_variable('logstd', mean.shape[1:], tf.float32,
-                               self._logstd_initializer)
-      logstd = tf.tile(logstd[None, ...],
-                       [tf.shape(mean)[0]] + [1] * logstd.shape.ndims)
+      mean = tf.contrib.layers.fully_connected(x,
+                                               self._action_size,
+                                               tf.tanh,
+                                               weights_initializer=self._mean_weights_initializer)
+      logstd = tf.get_variable('logstd', mean.shape[1:], tf.float32, self._logstd_initializer)
+      logstd = tf.tile(logstd[None, ...], [tf.shape(mean)[0]] + [1] * logstd.shape.ndims)
     with tf.variable_scope('value'):
       x = tf.contrib.layers.flatten(observation)
       for size in self._value_layers:
@@ -80,10 +75,12 @@ class ForwardGaussianPolicy(tf.contrib.rnn.RNNCell):
   is learned as independent parameter vector.
   """
 
-  def __init__(
-      self, policy_layers, value_layers, action_size,
-      mean_weights_initializer=_MEAN_WEIGHTS_INITIALIZER,
-      logstd_initializer=_LOGSTD_INITIALIZER):
+  def __init__(self,
+               policy_layers,
+               value_layers,
+               action_size,
+               mean_weights_initializer=_MEAN_WEIGHTS_INITIALIZER,
+               logstd_initializer=_LOGSTD_INITIALIZER):
     self._policy_layers = policy_layers
     self._value_layers = value_layers
     self._action_size = action_size
@@ -104,13 +101,12 @@ class ForwardGaussianPolicy(tf.contrib.rnn.RNNCell):
       x = tf.contrib.layers.flatten(observation)
       for size in self._policy_layers:
         x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
-      mean = tf.contrib.layers.fully_connected(
-          x, self._action_size, tf.tanh,
-          weights_initializer=self._mean_weights_initializer)
-      logstd = tf.get_variable(
-          'logstd', mean.shape[1:], tf.float32, self._logstd_initializer)
-      logstd = tf.tile(
-          logstd[None, ...], [tf.shape(mean)[0]] + [1] * logstd.shape.ndims)
+      mean = tf.contrib.layers.fully_connected(x,
+                                               self._action_size,
+                                               tf.tanh,
+                                               weights_initializer=self._mean_weights_initializer)
+      logstd = tf.get_variable('logstd', mean.shape[1:], tf.float32, self._logstd_initializer)
+      logstd = tf.tile(logstd[None, ...], [tf.shape(mean)[0]] + [1] * logstd.shape.ndims)
     with tf.variable_scope('value'):
       x = tf.contrib.layers.flatten(observation)
       for size in self._value_layers:
@@ -127,10 +123,12 @@ class RecurrentGaussianPolicy(tf.contrib.rnn.RNNCell):
   and uses a GRU cell.
   """
 
-  def __init__(
-      self, policy_layers, value_layers, action_size,
-      mean_weights_initializer=_MEAN_WEIGHTS_INITIALIZER,
-      logstd_initializer=_LOGSTD_INITIALIZER):
+  def __init__(self,
+               policy_layers,
+               value_layers,
+               action_size,
+               mean_weights_initializer=_MEAN_WEIGHTS_INITIALIZER,
+               logstd_initializer=_LOGSTD_INITIALIZER):
     self._policy_layers = policy_layers
     self._value_layers = value_layers
     self._action_size = action_size
@@ -152,13 +150,12 @@ class RecurrentGaussianPolicy(tf.contrib.rnn.RNNCell):
       for size in self._policy_layers[:-1]:
         x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
       x, state = self._cell(x, state)
-      mean = tf.contrib.layers.fully_connected(
-          x, self._action_size, tf.tanh,
-          weights_initializer=self._mean_weights_initializer)
-      logstd = tf.get_variable(
-          'logstd', mean.shape[1:], tf.float32, self._logstd_initializer)
-      logstd = tf.tile(
-          logstd[None, ...], [tf.shape(mean)[0]] + [1] * logstd.shape.ndims)
+      mean = tf.contrib.layers.fully_connected(x,
+                                               self._action_size,
+                                               tf.tanh,
+                                               weights_initializer=self._mean_weights_initializer)
+      logstd = tf.get_variable('logstd', mean.shape[1:], tf.float32, self._logstd_initializer)
+      logstd = tf.tile(logstd[None, ...], [tf.shape(mean)[0]] + [1] * logstd.shape.ndims)
     with tf.variable_scope('value'):
       x = tf.contrib.layers.flatten(observation)
       for size in self._value_layers:
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train.py
index e7048c2d9..99495b362 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 r"""Script to train a batch reinforcement learning algorithm.
 
 Command line:
@@ -68,21 +67,25 @@ def _define_loop(graph, logdir, train_steps, eval_steps):
   Returns:
     Loop object.
   """
-  loop = tools.Loop(
-      logdir, graph.step, graph.should_log, graph.do_report,
-      graph.force_reset)
-  loop.add_phase(
-      'train', graph.done, graph.score, graph.summary, train_steps,
-      report_every=None,
-      log_every=train_steps // 2,
-      checkpoint_every=None,
-      feed={graph.is_training: True})
-  loop.add_phase(
-      'eval', graph.done, graph.score, graph.summary, eval_steps,
-      report_every=eval_steps,
-      log_every=eval_steps // 2,
-      checkpoint_every=10 * eval_steps,
-      feed={graph.is_training: False})
+  loop = tools.Loop(logdir, graph.step, graph.should_log, graph.do_report, graph.force_reset)
+  loop.add_phase('train',
+                 graph.done,
+                 graph.score,
+                 graph.summary,
+                 train_steps,
+                 report_every=None,
+                 log_every=train_steps // 2,
+                 checkpoint_every=None,
+                 feed={graph.is_training: True})
+  loop.add_phase('eval',
+                 graph.done,
+                 graph.score,
+                 graph.summary,
+                 eval_steps,
+                 report_every=eval_steps,
+                 log_every=eval_steps // 2,
+                 checkpoint_every=10 * eval_steps,
+                 feed={graph.is_training: False})
   return loop
 
 
@@ -101,25 +104,19 @@ def train(config, env_processes):
   """
   tf.reset_default_graph()
   with config.unlocked:
-    config.network = functools.partial(
-        utility.define_network, config.network, config)
+    config.network = functools.partial(utility.define_network, config.network, config)
     config.policy_optimizer = getattr(tf.train, config.policy_optimizer)
     config.value_optimizer = getattr(tf.train, config.value_optimizer)
   if config.update_every % config.num_agents:
     tf.logging.warn('Number of agents should divide episodes per update.')
   with tf.device('/cpu:0'):
-    batch_env = utility.define_batch_env(
-        lambda: _create_environment(config),
-        config.num_agents, env_processes)
-    graph = utility.define_simulation_graph(
-        batch_env, config.algorithm, config)
-    loop = _define_loop(
-        graph, config.logdir,
-        config.update_every * config.max_length,
-        config.eval_episodes * config.max_length)
-    total_steps = int(
-        config.steps / config.update_every *
-        (config.update_every + config.eval_episodes))
+    batch_env = utility.define_batch_env(lambda: _create_environment(config), config.num_agents,
+                                         env_processes)
+    graph = utility.define_simulation_graph(batch_env, config.algorithm, config)
+    loop = _define_loop(graph, config.logdir, config.update_every * config.max_length,
+                        config.eval_episodes * config.max_length)
+    total_steps = int(config.steps / config.update_every *
+                      (config.update_every + config.eval_episodes))
   # Exclude episode related variables since the Python state of environments is
   # not checkpointed and thus new episodes start after resuming.
   saver = utility.define_saver(exclude=(r'.*_temporary/.*',))
@@ -137,8 +134,8 @@ def main(_):
   utility.set_up_logging()
   if not FLAGS.config:
     raise KeyError('You must specify a configuration.')
-  logdir = FLAGS.logdir and os.path.expanduser(os.path.join(
-      FLAGS.logdir, '{}-{}'.format(FLAGS.timestamp, FLAGS.config)))
+  logdir = FLAGS.logdir and os.path.expanduser(
+      os.path.join(FLAGS.logdir, '{}-{}'.format(FLAGS.timestamp, FLAGS.config)))
   try:
     config = utility.load_config(logdir)
   except IOError:
@@ -150,16 +147,11 @@ def main(_):
 
 if __name__ == '__main__':
   FLAGS = tf.app.flags.FLAGS
-  tf.app.flags.DEFINE_string(
-      'logdir', None,
-      'Base directory to store logs.')
-  tf.app.flags.DEFINE_string(
-      'timestamp', datetime.datetime.now().strftime('%Y%m%dT%H%M%S'),
-      'Sub directory to store logs.')
-  tf.app.flags.DEFINE_string(
-      'config', None,
-      'Configuration to execute.')
-  tf.app.flags.DEFINE_boolean(
-      'env_processes', True,
-      'Step environments in separate processes to circumvent the GIL.')
+  tf.app.flags.DEFINE_string('logdir', None, 'Base directory to store logs.')
+  tf.app.flags.DEFINE_string('timestamp',
+                             datetime.datetime.now().strftime('%Y%m%dT%H%M%S'),
+                             'Sub directory to store logs.')
+  tf.app.flags.DEFINE_string('config', None, 'Configuration to execute.')
+  tf.app.flags.DEFINE_boolean('env_processes', True,
+                              'Step environments in separate processes to circumvent the GIL.')
   tf.app.run()
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train_ppo_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train_ppo_test.py
index df5095825..930143c39 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train_ppo_test.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/train_ppo_test.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Tests for the PPO algorithm usage example."""
 
 from __future__ import absolute_import
@@ -29,7 +28,6 @@ from google3.robotics.reinforcement_learning.agents.scripts import configs
 from google3.robotics.reinforcement_learning.agents.scripts import networks
 from google3.robotics.reinforcement_learning.agents.scripts import train
 
-
 FLAGS = tf.app.flags.FLAGS
 
 
@@ -65,9 +63,11 @@ class PPOTest(tf.test.TestCase):
     for network, observ_shape in itertools.product(nets, observ_shapes):
       config = self._define_config()
       with config.unlocked:
-        config.env = functools.partial(
-            tools.MockEnvironment, observ_shape, action_shape=(3,),
-            min_duration=15, max_duration=15)
+        config.env = functools.partial(tools.MockEnvironment,
+                                       observ_shape,
+                                       action_shape=(3,),
+                                       min_duration=15,
+                                       max_duration=15)
         config.max_length = 20
         config.steps = 100
         config.network = network
@@ -77,9 +77,11 @@ class PPOTest(tf.test.TestCase):
   def test_no_crash_variable_duration(self):
     config = self._define_config()
     with config.unlocked:
-      config.env = functools.partial(
-          tools.MockEnvironment, observ_shape=(2, 3), action_shape=(3,),
-          min_duration=5, max_duration=25)
+      config.env = functools.partial(tools.MockEnvironment,
+                                     observ_shape=(2, 3),
+                                     action_shape=(3,),
+                                     min_duration=5,
+                                     max_duration=25)
       config.max_length = 25
       config.steps = 200
       config.network = networks.RecurrentGaussianPolicy
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/utility.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/utility.py
index 1342977c7..22bc50126 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/utility.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/utility.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Utilities for using reinforcement learning algorithms."""
 
 from __future__ import absolute_import
@@ -46,8 +45,7 @@ def define_simulation_graph(batch_env, algo_cls, config):
   do_report = tf.placeholder(tf.bool, name='do_report')
   force_reset = tf.placeholder(tf.bool, name='force_reset')
   algo = algo_cls(batch_env, step, is_training, should_log, config)
-  done, score, summary = tools.simulate(
-      batch_env, algo, should_log, force_reset)
+  done, score, summary = tools.simulate(batch_env, algo, should_log, force_reset)
   message = 'Graph contains {} trainable variables.'
   tf.logging.info(message.format(tools.count_weights()))
   # pylint: enable=unused-variable
@@ -67,9 +65,7 @@ def define_batch_env(constructor, num_agents, env_processes):
   """
   with tf.variable_scope('environments'):
     if env_processes:
-      envs = [
-          tools.wrappers.ExternalProcess(constructor)
-          for _ in range(num_agents)]
+      envs = [tools.wrappers.ExternalProcess(constructor) for _ in range(num_agents)]
     else:
       envs = [constructor() for _ in range(num_agents)]
     batch_env = tools.BatchEnv(envs, blocking=not env_processes)
@@ -108,15 +104,14 @@ def define_network(constructor, config, action_size):
   Returns:
     Created recurrent cell object.
   """
-  mean_weights_initializer = (
-      tf.contrib.layers.variance_scaling_initializer(
-          factor=config.init_mean_factor))
-  logstd_initializer = tf.random_normal_initializer(
-      config.init_logstd, 1e-10)
-  network = constructor(
-      config.policy_layers, config.value_layers, action_size,
-      mean_weights_initializer=mean_weights_initializer,
-      logstd_initializer=logstd_initializer)
+  mean_weights_initializer = (tf.contrib.layers.variance_scaling_initializer(
+      factor=config.init_mean_factor))
+  logstd_initializer = tf.random_normal_initializer(config.init_logstd, 1e-10)
+  network = constructor(config.policy_layers,
+                        config.value_layers,
+                        action_size,
+                        mean_weights_initializer=mean_weights_initializer,
+                        logstd_initializer=logstd_initializer)
   return network
 
 
@@ -134,9 +129,7 @@ def initialize_variables(sess, saver, logdir, checkpoint=None, resume=None):
     ValueError: If resume expected but no log directory specified.
     RuntimeError: If no resume expected but a checkpoint was found.
   """
-  sess.run(tf.group(
-      tf.local_variables_initializer(),
-      tf.global_variables_initializer()))
+  sess.run(tf.group(tf.local_variables_initializer(), tf.global_variables_initializer()))
   if resume and not (logdir or checkpoint):
     raise ValueError('Need to specify logdir to resume a checkpoint.')
   if logdir:
@@ -175,9 +168,8 @@ def save_config(config, logdir=None):
     with tf.gfile.GFile(config_path, 'w') as file_:
       yaml.dump(config, file_, default_flow_style=False)
   else:
-    message = (
-        'Start a new run without storing summaries and checkpoints since no '
-        'logging directory was specified.')
+    message = ('Start a new run without storing summaries and checkpoints since no '
+               'logging directory was specified.')
     tf.logging.info(message)
   return config
 
@@ -196,9 +188,8 @@ def load_config(logdir):
   """
   config_path = logdir and os.path.join(logdir, 'config.yaml')
   if not config_path or not tf.gfile.Exists(config_path):
-    message = (
-        'Cannot resume an existing run since the logging directory does not '
-        'contain a configuration file.')
+    message = ('Cannot resume an existing run since the logging directory does not '
+               'contain a configuration file.')
     raise IOError(message)
   with tf.gfile.FastGFile(config_path, 'r') as file_:
     config = yaml.load(file_)
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/visualize.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/visualize.py
index 3a5f62e4b..cc68c0d42 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/visualize.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/visualize.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 r"""Script to render videos of the Proximal Policy Gradient algorithm.
 
 Command line:
@@ -54,6 +53,8 @@ def _create_environment(config, outdir):
     setattr(env, 'spec', getattr(env, 'spec', None))
   if config.max_length:
     env = tools.wrappers.LimitDuration(env, config.max_length)
+
+
 #  env = gym.wrappers.Monitor(
 #      env, outdir, lambda unused_episode_number: True)
   env = tools.wrappers.RangeNormalize(env)
@@ -72,20 +73,20 @@ def _define_loop(graph, eval_steps):
   Returns:
     Loop object.
   """
-  loop = tools.Loop(
-      None, graph.step, graph.should_log, graph.do_report, graph.force_reset)
-  loop.add_phase(
-      'eval', graph.done, graph.score, graph.summary, eval_steps,
-      report_every=eval_steps,
-      log_every=None,
-      checkpoint_every=None,
-      feed={graph.is_training: False})
+  loop = tools.Loop(None, graph.step, graph.should_log, graph.do_report, graph.force_reset)
+  loop.add_phase('eval',
+                 graph.done,
+                 graph.score,
+                 graph.summary,
+                 eval_steps,
+                 report_every=eval_steps,
+                 log_every=None,
+                 checkpoint_every=None,
+                 feed={graph.is_training: False})
   return loop
 
 
-def visualize(
-    logdir, outdir, num_agents, num_episodes, checkpoint=None,
-    env_processes=True):
+def visualize(logdir, outdir, num_agents, num_episodes, checkpoint=None, env_processes=True):
   """Recover checkpoint and render videos from it.
 
   Args:
@@ -98,25 +99,20 @@ def visualize(
   """
   config = utility.load_config(logdir)
   with config.unlocked:
-    config.network = functools.partial(
-        utility.define_network, config.network, config)
+    config.network = functools.partial(utility.define_network, config.network, config)
     config.policy_optimizer = getattr(tf.train, config.policy_optimizer)
     config.value_optimizer = getattr(tf.train, config.value_optimizer)
   with tf.device('/cpu:0'):
-    batch_env = utility.define_batch_env(
-        lambda: _create_environment(config, outdir),
-        num_agents, env_processes)
-    graph = utility.define_simulation_graph(
-        batch_env, config.algorithm, config)
+    batch_env = utility.define_batch_env(lambda: _create_environment(config, outdir), num_agents,
+                                         env_processes)
+    graph = utility.define_simulation_graph(batch_env, config.algorithm, config)
     total_steps = num_episodes * config.max_length
     loop = _define_loop(graph, total_steps)
-  saver = utility.define_saver(
-      exclude=(r'.*_temporary/.*', r'global_step'))
+  saver = utility.define_saver(exclude=(r'.*_temporary/.*', r'global_step'))
   sess_config = tf.ConfigProto(allow_soft_placement=True)
   sess_config.gpu_options.allow_growth = True
   with tf.Session(config=sess_config) as sess:
-    utility.initialize_variables(
-        sess, saver, config.logdir, checkpoint, resume=True)
+    utility.initialize_variables(sess, saver, config.logdir, checkpoint, resume=True)
     for unused_score in loop.run(sess, saver, total_steps):
       pass
   batch_env.close()
@@ -129,29 +125,18 @@ def main(_):
     raise KeyError('You must specify logging and outdirs directories.')
   FLAGS.logdir = os.path.expanduser(FLAGS.logdir)
   FLAGS.outdir = os.path.expanduser(FLAGS.outdir)
-  visualize(
-      FLAGS.logdir, FLAGS.outdir, FLAGS.num_agents, FLAGS.num_episodes,
-      FLAGS.checkpoint, FLAGS.env_processes)
+  visualize(FLAGS.logdir, FLAGS.outdir, FLAGS.num_agents, FLAGS.num_episodes, FLAGS.checkpoint,
+            FLAGS.env_processes)
 
 
 if __name__ == '__main__':
   FLAGS = tf.app.flags.FLAGS
-  tf.app.flags.DEFINE_string(
-      'logdir', None,
-      'Directory to the checkpoint of a training run.')
-  tf.app.flags.DEFINE_string(
-      'outdir', None,
-      'Local directory for storing the monitoring outdir.')
-  tf.app.flags.DEFINE_string(
-      'checkpoint', None,
-      'Checkpoint name to load; defaults to most recent.')
-  tf.app.flags.DEFINE_integer(
-      'num_agents', 1,
-      'How many environments to step in parallel.')
-  tf.app.flags.DEFINE_integer(
-      'num_episodes', 5,
-      'Minimum number of episodes to render.')
-  tf.app.flags.DEFINE_boolean(
-      'env_processes', True,
-      'Step environments in separate processes to circumvent the GIL.')
+  tf.app.flags.DEFINE_string('logdir', None, 'Directory to the checkpoint of a training run.')
+  tf.app.flags.DEFINE_string('outdir', None, 'Local directory for storing the monitoring outdir.')
+  tf.app.flags.DEFINE_string('checkpoint', None,
+                             'Checkpoint name to load; defaults to most recent.')
+  tf.app.flags.DEFINE_integer('num_agents', 1, 'How many environments to step in parallel.')
+  tf.app.flags.DEFINE_integer('num_episodes', 5, 'Minimum number of episodes to render.')
+  tf.app.flags.DEFINE_boolean('env_processes', True,
+                              'Step environments in separate processes to circumvent the GIL.')
   tf.app.run()
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/__init__.py
index d624c8243..f2f435c3e 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/__init__.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/__init__.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Tools for reinforcement learning."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict.py
index 1707486d8..fbb08290f 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Wrap a dictionary to access keys as attributes."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict_test.py
index f5b2e3ce4..7ab1f415f 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict_test.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/attr_dict_test.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Tests for the attribute dictionary."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/batch_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/batch_env.py
index d103d0133..a2971bc4d 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/batch_env.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/batch_env.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Combine multiple environments to step them in batch."""
 
 from __future__ import absolute_import
@@ -84,13 +83,9 @@ class BatchEnv(object):
         message = 'Invalid action at index {}: {}'
         raise ValueError(message.format(index, action))
     if self._blocking:
-      transitions = [
-          env.step(action)
-          for env, action in zip(self._envs, actions)]
+      transitions = [env.step(action) for env, action in zip(self._envs, actions)]
     else:
-      transitions = [
-          env.step(action, blocking=False)
-          for env, action in zip(self._envs, actions)]
+      transitions = [env.step(action, blocking=False) for env, action in zip(self._envs, actions)]
       transitions = [transition() for transition in transitions]
     observs, rewards, dones, infos = zip(*transitions)
     observ = np.stack(observs)
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights.py
index dd0d870f6..279df82a0 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Count learnable parameters."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights_test.py
index 930aa8623..a42c60f57 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights_test.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/count_weights_test.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Tests for the weight counting utility."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_batch_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_batch_env.py
index 2765d4821..6cd0a2fdf 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_batch_env.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_batch_env.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Batch of environments inside the TensorFlow graph."""
 
 from __future__ import absolute_import
@@ -42,18 +41,18 @@ class InGraphBatchEnv(object):
     action_shape = self._parse_shape(self._batch_env.action_space)
     action_dtype = self._parse_dtype(self._batch_env.action_space)
     with tf.variable_scope('env_temporary'):
-      self._observ = tf.Variable(
-          tf.zeros((len(self._batch_env),) + observ_shape, observ_dtype),
-          name='observ', trainable=False)
-      self._action = tf.Variable(
-          tf.zeros((len(self._batch_env),) + action_shape, action_dtype),
-          name='action', trainable=False)
-      self._reward = tf.Variable(
-          tf.zeros((len(self._batch_env),), tf.float32),
-          name='reward', trainable=False)
-      self._done = tf.Variable(
-          tf.cast(tf.ones((len(self._batch_env),)), tf.bool),
-          name='done', trainable=False)
+      self._observ = tf.Variable(tf.zeros((len(self._batch_env),) + observ_shape, observ_dtype),
+                                 name='observ',
+                                 trainable=False)
+      self._action = tf.Variable(tf.zeros((len(self._batch_env),) + action_shape, action_dtype),
+                                 name='action',
+                                 trainable=False)
+      self._reward = tf.Variable(tf.zeros((len(self._batch_env),), tf.float32),
+                                 name='reward',
+                                 trainable=False)
+      self._done = tf.Variable(tf.cast(tf.ones((len(self._batch_env),)), tf.bool),
+                               name='done',
+                               trainable=False)
 
   def __getattr__(self, name):
     """Forward unimplemented attributes to one of the original environments.
@@ -89,16 +88,13 @@ class InGraphBatchEnv(object):
       if action.dtype in (tf.float16, tf.float32, tf.float64):
         action = tf.check_numerics(action, 'action')
       observ_dtype = self._parse_dtype(self._batch_env.observation_space)
-      observ, reward, done = tf.py_func(
-          lambda a: self._batch_env.step(a)[:3], [action],
-          [observ_dtype, tf.float32, tf.bool], name='step')
+      observ, reward, done = tf.py_func(lambda a: self._batch_env.step(a)[:3], [action],
+                                        [observ_dtype, tf.float32, tf.bool],
+                                        name='step')
       observ = tf.check_numerics(observ, 'observ')
       reward = tf.check_numerics(reward, 'reward')
-      return tf.group(
-          self._observ.assign(observ),
-          self._action.assign(action),
-          self._reward.assign(reward),
-          self._done.assign(done))
+      return tf.group(self._observ.assign(observ), self._action.assign(action),
+                      self._reward.assign(reward), self._done.assign(done))
 
   def reset(self, indices=None):
     """Reset the batch of environments.
@@ -112,15 +108,15 @@ class InGraphBatchEnv(object):
     if indices is None:
       indices = tf.range(len(self._batch_env))
     observ_dtype = self._parse_dtype(self._batch_env.observation_space)
-    observ = tf.py_func(
-        self._batch_env.reset, [indices], observ_dtype, name='reset')
+    observ = tf.py_func(self._batch_env.reset, [indices], observ_dtype, name='reset')
     observ = tf.check_numerics(observ, 'observ')
     reward = tf.zeros_like(indices, tf.float32)
     done = tf.zeros_like(indices, tf.bool)
     with tf.control_dependencies([
         tf.scatter_update(self._observ, indices, observ),
         tf.scatter_update(self._reward, indices, reward),
-        tf.scatter_update(self._done, indices, done)]):
+        tf.scatter_update(self._done, indices, done)
+    ]):
       return tf.identity(observ)
 
   @property
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_env.py
index 33ff31d07..6a71f4516 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_env.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/in_graph_env.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Put an OpenAI Gym environment into the TensorFlow graph."""
 
 from __future__ import absolute_import
@@ -42,16 +41,15 @@ class InGraphEnv(object):
     action_shape = self._parse_shape(self._env.action_space)
     action_dtype = self._parse_dtype(self._env.action_space)
     with tf.name_scope('environment'):
-      self._observ = tf.Variable(
-          tf.zeros(observ_shape, observ_dtype), name='observ', trainable=False)
-      self._action = tf.Variable(
-          tf.zeros(action_shape, action_dtype), name='action', trainable=False)
-      self._reward = tf.Variable(
-          0.0, dtype=tf.float32, name='reward', trainable=False)
-      self._done = tf.Variable(
-          True, dtype=tf.bool, name='done', trainable=False)
-      self._step = tf.Variable(
-          0, dtype=tf.int32, name='step', trainable=False)
+      self._observ = tf.Variable(tf.zeros(observ_shape, observ_dtype),
+                                 name='observ',
+                                 trainable=False)
+      self._action = tf.Variable(tf.zeros(action_shape, action_dtype),
+                                 name='action',
+                                 trainable=False)
+      self._reward = tf.Variable(0.0, dtype=tf.float32, name='reward', trainable=False)
+      self._done = tf.Variable(True, dtype=tf.bool, name='done', trainable=False)
+      self._step = tf.Variable(0, dtype=tf.int32, name='step', trainable=False)
 
   def __getattr__(self, name):
     """Forward unimplemented attributes to the original environment.
@@ -79,17 +77,14 @@ class InGraphEnv(object):
       if action.dtype in (tf.float16, tf.float32, tf.float64):
         action = tf.check_numerics(action, 'action')
       observ_dtype = self._parse_dtype(self._env.observation_space)
-      observ, reward, done = tf.py_func(
-          lambda a: self._env.step(a)[:3], [action],
-          [observ_dtype, tf.float32, tf.bool], name='step')
+      observ, reward, done = tf.py_func(lambda a: self._env.step(a)[:3], [action],
+                                        [observ_dtype, tf.float32, tf.bool],
+                                        name='step')
       observ = tf.check_numerics(observ, 'observ')
       reward = tf.check_numerics(reward, 'reward')
-      return tf.group(
-          self._observ.assign(observ),
-          self._action.assign(action),
-          self._reward.assign(reward),
-          self._done.assign(done),
-          self._step.assign_add(1))
+      return tf.group(self._observ.assign(observ), self._action.assign(action),
+                      self._reward.assign(reward), self._done.assign(done),
+                      self._step.assign_add(1))
 
   def reset(self):
     """Reset the environment.
@@ -100,10 +95,10 @@ class InGraphEnv(object):
     observ_dtype = self._parse_dtype(self._env.observation_space)
     observ = tf.py_func(self._env.reset, [], observ_dtype, name='reset')
     observ = tf.check_numerics(observ, 'observ')
-    with tf.control_dependencies([
-        self._observ.assign(observ),
-        self._reward.assign(0),
-        self._done.assign(False)]):
+    with tf.control_dependencies(
+        [self._observ.assign(observ),
+         self._reward.assign(0),
+         self._done.assign(False)]):
       return tf.identity(observ)
 
   @property
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop.py
index 36f1e7faa..78a3cfd29 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Execute operations in a loop and coordinate logging and checkpoints."""
 
 from __future__ import absolute_import
@@ -25,10 +24,8 @@ import tensorflow as tf
 
 from pybullet_envs.minitaur.agents.tools import streaming_mean
 
-
 _Phase = collections.namedtuple(
-    'Phase',
-    'name, writer, op, batch, steps, feed, report_every, log_every,'
+    'Phase', 'name, writer, op, batch, steps, feed, report_every, log_every,'
     'checkpoint_every')
 
 
@@ -56,16 +53,22 @@ class Loop(object):
       reset: Tensor indicating to the model to start a new computation.
     """
     self._logdir = logdir
-    self._step = (
-        tf.Variable(0, False, name='global_step') if step is None else step)
+    self._step = (tf.Variable(0, False, name='global_step') if step is None else step)
     self._log = tf.placeholder(tf.bool) if log is None else log
     self._report = tf.placeholder(tf.bool) if report is None else report
     self._reset = tf.placeholder(tf.bool) if reset is None else reset
     self._phases = []
 
-  def add_phase(
-      self, name, done, score, summary, steps,
-      report_every=None, log_every=None, checkpoint_every=None, feed=None):
+  def add_phase(self,
+                name,
+                done,
+                score,
+                summary,
+                steps,
+                report_every=None,
+                log_every=None,
+                checkpoint_every=None,
+                feed=None):
     """Add a phase to the loop protocol.
 
     If the model breaks long computation into multiple steps, the done tensor
@@ -97,13 +100,12 @@ class Loop(object):
     if done.shape.ndims is None or score.shape.ndims is None:
       raise ValueError("Rank of 'done' and 'score' tensors must be known.")
     writer = self._logdir and tf.summary.FileWriter(
-        os.path.join(self._logdir, name), tf.get_default_graph(),
-        flush_secs=60)
+        os.path.join(self._logdir, name), tf.get_default_graph(), flush_secs=60)
     op = self._define_step(done, score, summary)
     batch = 1 if score.shape.ndims == 0 else score.shape[0].value
-    self._phases.append(_Phase(
-        name, writer, op, batch, int(steps), feed, report_every,
-        log_every, checkpoint_every))
+    self._phases.append(
+        _Phase(name, writer, op, batch, int(steps), feed, report_every, log_every,
+               checkpoint_every))
 
   def run(self, sess, saver, max_step=None):
     """Run the loop schedule for a specified number of steps.
@@ -133,13 +135,11 @@ class Loop(object):
         tf.logging.info(message.format(phase.name, phase_step, global_step))
       # Populate book keeping tensors.
       phase.feed[self._reset] = (steps_in < steps_made)
-      phase.feed[self._log] = (
-          phase.writer and
-          self._is_every_steps(phase_step, phase.batch, phase.log_every))
-      phase.feed[self._report] = (
-          self._is_every_steps(phase_step, phase.batch, phase.report_every))
-      summary, mean_score, global_step, steps_made = sess.run(
-          phase.op, phase.feed)
+      phase.feed[self._log] = (phase.writer and
+                               self._is_every_steps(phase_step, phase.batch, phase.log_every))
+      phase.feed[self._report] = (self._is_every_steps(phase_step, phase.batch,
+                                                       phase.report_every))
+      summary, mean_score, global_step, steps_made = sess.run(phase.op, phase.feed)
       if self._is_every_steps(phase_step, phase.batch, phase.checkpoint_every):
         self._store_checkpoint(sess, saver, global_step)
       if self._is_every_steps(phase_step, phase.batch, phase.report_every):
@@ -207,8 +207,7 @@ class Loop(object):
     score_mean = streaming_mean.StreamingMean((), tf.float32)
     with tf.control_dependencies([done, score, summary]):
       done_score = tf.gather(score, tf.where(done)[:, 0])
-      submit_score = tf.cond(
-          tf.reduce_any(done), lambda: score_mean.submit(done_score), tf.no_op)
+      submit_score = tf.cond(tf.reduce_any(done), lambda: score_mean.submit(done_score), tf.no_op)
     with tf.control_dependencies([submit_score]):
       mean_score = tf.cond(self._report, score_mean.clear, float)
       steps_made = tf.shape(score)[0]
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop_test.py
index d4d03c513..3245d26b7 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop_test.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/loop_test.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Tests for the training loop."""
 
 from __future__ import absolute_import
@@ -28,8 +27,7 @@ class LoopTest(tf.test.TestCase):
   def test_report_every_step(self):
     step = tf.Variable(0, False, dtype=tf.int32, name='step')
     loop = tools.Loop(None, step)
-    loop.add_phase(
-        'phase_1', done=True, score=0, summary='', steps=1, report_every=3)
+    loop.add_phase('phase_1', done=True, score=0, summary='', steps=1, report_every=3)
     # Step:   0 1 2 3 4 5 6 7 8
     # Report:     x     x     x
     with self.test_session() as sess:
@@ -45,15 +43,33 @@ class LoopTest(tf.test.TestCase):
   def test_phases_feed(self):
     score = tf.placeholder(tf.float32, [])
     loop = tools.Loop(None)
-    loop.add_phase(
-        'phase_1', done=True, score=score, summary='', steps=1, report_every=1,
-        log_every=None, checkpoint_every=None, feed={score: 1})
-    loop.add_phase(
-        'phase_2', done=True, score=score, summary='', steps=3, report_every=1,
-        log_every=None, checkpoint_every=None, feed={score: 2})
-    loop.add_phase(
-        'phase_3', done=True, score=score, summary='', steps=2, report_every=1,
-        log_every=None, checkpoint_every=None, feed={score: 3})
+    loop.add_phase('phase_1',
+                   done=True,
+                   score=score,
+                   summary='',
+                   steps=1,
+                   report_every=1,
+                   log_every=None,
+                   checkpoint_every=None,
+                   feed={score: 1})
+    loop.add_phase('phase_2',
+                   done=True,
+                   score=score,
+                   summary='',
+                   steps=3,
+                   report_every=1,
+                   log_every=None,
+                   checkpoint_every=None,
+                   feed={score: 2})
+    loop.add_phase('phase_3',
+                   done=True,
+                   score=score,
+                   summary='',
+                   steps=2,
+                   report_every=1,
+                   log_every=None,
+                   checkpoint_every=None,
+                   feed={score: 3})
     with self.test_session() as sess:
       sess.run(tf.global_variables_initializer())
       scores = list(loop.run(sess, saver=None, max_step=15))
@@ -61,10 +77,8 @@ class LoopTest(tf.test.TestCase):
 
   def test_average_score_over_phases(self):
     loop = tools.Loop(None)
-    loop.add_phase(
-        'phase_1', done=True, score=1, summary='', steps=1, report_every=2)
-    loop.add_phase(
-        'phase_2', done=True, score=2, summary='', steps=2, report_every=5)
+    loop.add_phase('phase_1', done=True, score=1, summary='', steps=1, report_every=2)
+    loop.add_phase('phase_2', done=True, score=2, summary='', steps=2, report_every=5)
     # Score:    1 2 2 1 2 2 1 2 2 1 2 2 1 2 2 1 2
     # Report 1:       x           x           x
     # Report 2:               x             x
@@ -78,8 +92,7 @@ class LoopTest(tf.test.TestCase):
     done = tf.equal((step + 1) % 2, 0)
     score = tf.cast(step, tf.float32)
     loop = tools.Loop(None, step)
-    loop.add_phase(
-        'phase_1', done, score, summary='', steps=1, report_every=3)
+    loop.add_phase('phase_1', done, score, summary='', steps=1, report_every=3)
     # Score:  0 1 2 3 4 5 6 7 8
     # Done:     x   x   x   x
     # Report:     x     x     x
@@ -91,10 +104,9 @@ class LoopTest(tf.test.TestCase):
   def test_not_done_batch(self):
     step = tf.Variable(0, False, dtype=tf.int32, name='step')
     done = tf.equal([step % 3, step % 4], 0)
-    score = tf.cast([step, step ** 2], tf.float32)
+    score = tf.cast([step, step**2], tf.float32)
     loop = tools.Loop(None, step)
-    loop.add_phase(
-        'phase_1', done, score, summary='', steps=1, report_every=8)
+    loop.add_phase('phase_1', done, score, summary='', steps=1, report_every=8)
     # Step:    0  2  4  6
     # Score 1: 0  2  4  6
     # Done 1:  x        x
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_algorithm.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_algorithm.py
index 1bc58a906..e321afae3 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_algorithm.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_algorithm.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Mock algorithm for testing reinforcement learning code."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_environment.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_environment.py
index 248f515b1..9db14d56c 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_environment.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/mock_environment.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Mock environment for testing reinforcement learning code."""
 
 from __future__ import absolute_import
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate.py
index 45b36d6b0..e794f6a9d 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """In-graph simulation step of a vecrotized algorithm with environments."""
 
 from __future__ import absolute_import
@@ -55,7 +54,8 @@ def simulate(batch_env, algo, log=True, reset=False):
     reset_ops = [
         batch_env.reset(agent_indices),
         tf.scatter_update(score, agent_indices, zero_scores),
-        tf.scatter_update(length, agent_indices, zero_durations)]
+        tf.scatter_update(length, agent_indices, zero_durations)
+    ]
     with tf.control_dependencies(reset_ops):
       return algo.begin_episode(agent_indices)
 
@@ -76,9 +76,8 @@ def simulate(batch_env, algo, log=True, reset=False):
       add_score = score.assign_add(batch_env.reward)
       inc_length = length.assign_add(tf.ones(len(batch_env), tf.int32))
     with tf.control_dependencies([add_score, inc_length]):
-      experience_summary = algo.experience(
-          prevob, batch_env.action, batch_env.reward, batch_env.done,
-          batch_env.observ)
+      experience_summary = algo.experience(prevob, batch_env.action, batch_env.reward,
+                                           batch_env.done, batch_env.observ)
     return tf.summary.merge([step_summary, experience_summary])
 
   def _define_end_episode(agent_indices):
@@ -94,8 +93,7 @@ def simulate(batch_env, algo, log=True, reset=False):
     """
     assert agent_indices.shape.ndims == 1
     submit_score = mean_score.submit(tf.gather(score, agent_indices))
-    submit_length = mean_length.submit(
-        tf.cast(tf.gather(length, agent_indices), tf.float32))
+    submit_length = mean_length.submit(tf.cast(tf.gather(length, agent_indices), tf.float32))
     with tf.control_dependencies([submit_score, submit_length]):
       return algo.end_episode(agent_indices)
 
@@ -105,41 +103,34 @@ def simulate(batch_env, algo, log=True, reset=False):
     Returns:
       Summary string.
     """
-    score_summary = tf.cond(
-        tf.logical_and(log, tf.cast(mean_score.count, tf.bool)),
-        lambda: tf.summary.scalar('mean_score', mean_score.clear()), str)
-    length_summary = tf.cond(
-        tf.logical_and(log, tf.cast(mean_length.count, tf.bool)),
-        lambda: tf.summary.scalar('mean_length', mean_length.clear()), str)
+    score_summary = tf.cond(tf.logical_and(log, tf.cast(
+        mean_score.count, tf.bool)), lambda: tf.summary.scalar('mean_score', mean_score.clear()),
+                            str)
+    length_summary = tf.cond(tf.logical_and(
+        log, tf.cast(mean_length.count,
+                     tf.bool)), lambda: tf.summary.scalar('mean_length', mean_length.clear()), str)
     return tf.summary.merge([score_summary, length_summary])
 
   with tf.name_scope('simulate'):
     log = tf.convert_to_tensor(log)
     reset = tf.convert_to_tensor(reset)
     with tf.variable_scope('simulate_temporary'):
-      score = tf.Variable(
-          tf.zeros(len(batch_env), dtype=tf.float32), False, name='score')
-      length = tf.Variable(
-          tf.zeros(len(batch_env), dtype=tf.int32), False, name='length')
+      score = tf.Variable(tf.zeros(len(batch_env), dtype=tf.float32), False, name='score')
+      length = tf.Variable(tf.zeros(len(batch_env), dtype=tf.int32), False, name='length')
     mean_score = streaming_mean.StreamingMean((), tf.float32)
     mean_length = streaming_mean.StreamingMean((), tf.float32)
-    agent_indices = tf.cond(
-        reset,
-        lambda: tf.range(len(batch_env)),
-        lambda: tf.cast(tf.where(batch_env.done)[:, 0], tf.int32))
-    begin_episode = tf.cond(
-        tf.cast(tf.shape(agent_indices)[0], tf.bool),
-        lambda: _define_begin_episode(agent_indices), str)
+    agent_indices = tf.cond(reset, lambda: tf.range(len(batch_env)), lambda: tf.cast(
+        tf.where(batch_env.done)[:, 0], tf.int32))
+    begin_episode = tf.cond(tf.cast(tf.shape(agent_indices)[0],
+                                    tf.bool), lambda: _define_begin_episode(agent_indices), str)
     with tf.control_dependencies([begin_episode]):
       step = _define_step()
     with tf.control_dependencies([step]):
       agent_indices = tf.cast(tf.where(batch_env.done)[:, 0], tf.int32)
-      end_episode = tf.cond(
-          tf.cast(tf.shape(agent_indices)[0], tf.bool),
-          lambda: _define_end_episode(agent_indices), str)
+      end_episode = tf.cond(tf.cast(tf.shape(agent_indices)[0],
+                                    tf.bool), lambda: _define_end_episode(agent_indices), str)
     with tf.control_dependencies([end_episode]):
-      summary = tf.summary.merge([
-          _define_summaries(), begin_episode, step, end_episode])
+      summary = tf.summary.merge([_define_summaries(), begin_episode, step, end_episode])
     with tf.control_dependencies([summary]):
       done, score = tf.identity(batch_env.done), tf.identity(score)
     return done, score, summary
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate_test.py
index 590010fec..42b45bc7f 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate_test.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/simulate_test.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Tests for the simulation operation."""
 
 from __future__ import absolute_import
@@ -84,9 +83,10 @@ class SimulateTest(tf.test.TestCase):
   def _create_test_batch_env(self, durations):
     envs = []
     for duration in durations:
-      env = tools.MockEnvironment(
-          observ_shape=(2, 3), action_shape=(3,),
-          min_duration=duration, max_duration=duration)
+      env = tools.MockEnvironment(observ_shape=(2, 3),
+                                  action_shape=(3,),
+                                  min_duration=duration,
+                                  max_duration=duration)
       env = tools.wrappers.ConvertTo32Bit(env)
       envs.append(env)
     batch_env = tools.BatchEnv(envs, blocking=True)
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/streaming_mean.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/streaming_mean.py
index 3f620fe37..5992eaa6b 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/streaming_mean.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/streaming_mean.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Compute a streaming estimation of the mean of submitted tensors."""
 
 from __future__ import absolute_import
@@ -53,9 +52,8 @@ class StreamingMean(object):
     # Add a batch dimension if necessary.
     if value.shape.ndims == self._sum.shape.ndims:
       value = value[None, ...]
-    return tf.group(
-        self._sum.assign_add(tf.reduce_sum(value, 0)),
-        self._count.assign_add(tf.shape(value)[0]))
+    return tf.group(self._sum.assign_add(tf.reduce_sum(value, 0)),
+                    self._count.assign_add(tf.shape(value)[0]))
 
   def clear(self):
     """Return the mean estimate and reset the streaming statistics."""
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers.py
index 5cac88fdf..2e33f3942 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Wrappers for OpenAI Gym environments."""
 
 from __future__ import absolute_import
@@ -150,8 +149,7 @@ class FrameHistory(object):
     return self._select_frames()
 
   def _select_frames(self):
-    indices = [
-        (self._step - index) % self._capacity for index in self._past_indices]
+    indices = [(self._step - index) % self._capacity for index in self._past_indices]
     observ = self._buffer[indices]
     if self._flatten:
       observ = np.reshape(observ, (-1,) + observ.shape[2:])
@@ -192,14 +190,14 @@ class RangeNormalize(object):
 
   def __init__(self, env, observ=None, action=None):
     self._env = env
-    self._should_normalize_observ = (
-        observ is not False and self._is_finite(self._env.observation_space))
+    self._should_normalize_observ = (observ is not False and
+                                     self._is_finite(self._env.observation_space))
     if observ is True and not self._should_normalize_observ:
       raise ValueError('Cannot normalize infinite observation range.')
     if observ is None and not self._should_normalize_observ:
       tf.logging.info('Not normalizing infinite observation range.')
-    self._should_normalize_action = (
-        action is not False and self._is_finite(self._env.action_space))
+    self._should_normalize_action = (action is not False and
+                                     self._is_finite(self._env.action_space))
     if action is True and not self._should_normalize_action:
       raise ValueError('Cannot normalize infinite action range.')
     if action is None and not self._should_normalize_action:
@@ -327,8 +325,7 @@ class ExternalProcess(object):
       action_space: The cached action space of the environment.
     """
     self._conn, conn = multiprocessing.Pipe()
-    self._process = multiprocessing.Process(
-        target=self._worker, args=(constructor, conn))
+    self._process = multiprocessing.Process(target=self._worker, args=(constructor, conn))
     atexit.register(self.close)
     self._process.start()
     self._observ_space = None
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers_test.py b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers_test.py
index dfc6fb263..fac8dd0d4 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers_test.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/wrappers_test.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Tests for environment wrappers."""
 
 from __future__ import absolute_import
@@ -28,18 +27,20 @@ from agents import tools
 class ExternalProcessTest(tf.test.TestCase):
 
   def test_close_no_hang_after_init(self):
-    constructor = functools.partial(
-        tools.MockEnvironment,
-        observ_shape=(2, 3), action_shape=(2,),
-        min_duration=2, max_duration=2)
+    constructor = functools.partial(tools.MockEnvironment,
+                                    observ_shape=(2, 3),
+                                    action_shape=(2,),
+                                    min_duration=2,
+                                    max_duration=2)
     env = tools.wrappers.ExternalProcess(constructor)
     env.close()
 
   def test_close_no_hang_after_step(self):
-    constructor = functools.partial(
-        tools.MockEnvironment,
-        observ_shape=(2, 3), action_shape=(2,),
-        min_duration=5, max_duration=5)
+    constructor = functools.partial(tools.MockEnvironment,
+                                    observ_shape=(2, 3),
+                                    action_shape=(2,),
+                                    min_duration=5,
+                                    max_duration=5)
     env = tools.wrappers.ExternalProcess(constructor)
     env.reset()
     env.step(env.action_space.sample())
@@ -53,8 +54,7 @@ class ExternalProcessTest(tf.test.TestCase):
       env.step(env.action_space.sample())
 
   def test_reraise_exception_in_step(self):
-    constructor = functools.partial(
-        MockEnvironmentCrashInStep, crash_at_step=3)
+    constructor = functools.partial(MockEnvironmentCrashInStep, crash_at_step=3)
     env = tools.wrappers.ExternalProcess(constructor)
     env.reset()
     env.step(env.action_space.sample())
@@ -74,9 +74,10 @@ class MockEnvironmentCrashInStep(tools.MockEnvironment):
   """Raise an error after specified number of steps in an episode."""
 
   def __init__(self, crash_at_step):
-    super(MockEnvironmentCrashInStep, self).__init__(
-        observ_shape=(2, 3), action_shape=(2,),
-        min_duration=crash_at_step + 1, max_duration=crash_at_step + 1)
+    super(MockEnvironmentCrashInStep, self).__init__(observ_shape=(2, 3),
+                                                     action_shape=(2,),
+                                                     min_duration=crash_at_step + 1,
+                                                     max_duration=crash_at_step + 1)
     self._crash_at_step = crash_at_step
 
   def step(self, *args, **kwargs):
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/__init__.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/__init__.py
index 37def777d..72c619547 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/__init__.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/__init__.py
@@ -5,6 +5,3 @@ from pybullet_envs.minitaur.envs.minitaur_reactive_env import MinitaurReactiveEn
 from pybullet_envs.minitaur.envs.minitaur_stand_gym_env import MinitaurStandGymEnv
 from pybullet_envs.minitaur.envs.minitaur_trotting_env import MinitaurTrottingEnv
 from pybullet_envs.minitaur.envs.minitaur_four_leg_stand_env import MinitaurFourLegStandEnv
-
-
-
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/bullet_client.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/bullet_client.py
index 8fc1fcd5c..54e0bd09e 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/bullet_client.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/bullet_client.py
@@ -42,9 +42,13 @@ class BulletClient(object):
     attribute = getattr(pybullet, name)
     if inspect.isbuiltin(attribute):
       if name not in [
-          "invertTransform", "multiplyTransforms", "getMatrixFromQuaternion",
-          "getEulerFromQuaternion", "computeViewMatrixFromYawPitchRoll",
-          "computeProjectionMatrixFOV", "getQuaternionFromEuler",
+          "invertTransform",
+          "multiplyTransforms",
+          "getMatrixFromQuaternion",
+          "getEulerFromQuaternion",
+          "computeViewMatrixFromYawPitchRoll",
+          "computeProjectionMatrixFOV",
+          "getQuaternionFromEuler",
       ]:  # A temporary hack for now.
         attribute = functools.partial(attribute, physicsClientId=self._client)
     return attribute
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_alternating_legs_env_randomizer.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_alternating_legs_env_randomizer.py
index a2d1645f4..9f868ba60 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_alternating_legs_env_randomizer.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_alternating_legs_env_randomizer.py
@@ -4,11 +4,11 @@ The randomization include swing_offset, extension_offset of all legs that mimics
 bent legs, desired_pitch from user input, battery voltage and motor damping.
 """
 
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
 parentdir = os.path.dirname(os.path.dirname(parentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import numpy as np
 import tensorflow as tf
@@ -20,8 +20,7 @@ BATTERY_VOLTAGE_RANGE = (14.8, 16.8)
 MOTOR_VISCOUS_DAMPING_RANGE = (0, 0.01)
 
 
-class MinitaurAlternatingLegsEnvRandomizer(
-    env_randomizer_base.EnvRandomizerBase):
+class MinitaurAlternatingLegsEnvRandomizer(env_randomizer_base.EnvRandomizerBase):
   """A randomizer that changes the minitaur_gym_alternating_leg_env."""
 
   def __init__(self,
@@ -34,23 +33,20 @@ class MinitaurAlternatingLegsEnvRandomizer(
     self.perturb_desired_pitch_bound = perturb_desired_pitch_bound
 
   def randomize_env(self, env):
-    perturb_magnitude = np.random.uniform(
-        low=-self.perturb_swing_bound,
-        high=self.perturb_swing_bound,
-        size=NUM_LEGS)
+    perturb_magnitude = np.random.uniform(low=-self.perturb_swing_bound,
+                                          high=self.perturb_swing_bound,
+                                          size=NUM_LEGS)
     env.set_swing_offset(perturb_magnitude)
     tf.logging.info("swing_offset: {}".format(perturb_magnitude))
 
-    perturb_magnitude = np.random.uniform(
-        low=-self.perturb_extension_bound,
-        high=self.perturb_extension_bound,
-        size=NUM_LEGS)
+    perturb_magnitude = np.random.uniform(low=-self.perturb_extension_bound,
+                                          high=self.perturb_extension_bound,
+                                          size=NUM_LEGS)
     env.set_extension_offset(perturb_magnitude)
     tf.logging.info("extension_offset: {}".format(perturb_magnitude))
 
-    perturb_magnitude = np.random.uniform(
-        low=-self.perturb_desired_pitch_bound,
-        high=self.perturb_desired_pitch_bound)
+    perturb_magnitude = np.random.uniform(low=-self.perturb_desired_pitch_bound,
+                                          high=self.perturb_desired_pitch_bound)
     env.set_desired_pitch(perturb_magnitude)
     tf.logging.info("desired_pitch: {}".format(perturb_magnitude))
 
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer.py
index befdf7ea1..0fbfef36d 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer.py
@@ -1,11 +1,11 @@
 """Randomize the minitaur_gym_env when reset() is called."""
 import random
 
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
 parentdir = os.path.dirname(os.path.dirname(parentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import numpy as np
 from pybullet_envs.minitaur.envs import env_randomizer_base
@@ -52,24 +52,20 @@ class MinitaurEnvRandomizer(env_randomizer_base.EnvRandomizerBase):
     minitaur.SetBaseMasses(randomized_base_mass)
 
     leg_masses = minitaur.GetLegMassesFromURDF()
-    leg_masses_lower_bound = np.array(leg_masses) * (
-        1.0 + self._minitaur_leg_mass_err_range[0])
-    leg_masses_upper_bound = np.array(leg_masses) * (
-        1.0 + self._minitaur_leg_mass_err_range[1])
+    leg_masses_lower_bound = np.array(leg_masses) * (1.0 + self._minitaur_leg_mass_err_range[0])
+    leg_masses_upper_bound = np.array(leg_masses) * (1.0 + self._minitaur_leg_mass_err_range[1])
     randomized_leg_masses = [
         np.random.uniform(leg_masses_lower_bound[i], leg_masses_upper_bound[i])
         for i in xrange(len(leg_masses))
     ]
     minitaur.SetLegMasses(randomized_leg_masses)
 
-    randomized_battery_voltage = random.uniform(BATTERY_VOLTAGE_RANGE[0],
-                                                BATTERY_VOLTAGE_RANGE[1])
+    randomized_battery_voltage = random.uniform(BATTERY_VOLTAGE_RANGE[0], BATTERY_VOLTAGE_RANGE[1])
     minitaur.SetBatteryVoltage(randomized_battery_voltage)
 
     randomized_motor_damping = random.uniform(MOTOR_VISCOUS_DAMPING_RANGE[0],
                                               MOTOR_VISCOUS_DAMPING_RANGE[1])
     minitaur.SetMotorViscousDamping(randomized_motor_damping)
 
-    randomized_foot_friction = random.uniform(MINITAUR_LEG_FRICTION[0],
-                                              MINITAUR_LEG_FRICTION[1])
+    randomized_foot_friction = random.uniform(MINITAUR_LEG_FRICTION[0], MINITAUR_LEG_FRICTION[1])
     minitaur.SetFootFriction(randomized_foot_friction)
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_config.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_config.py
index 53d5ad122..85a97bab7 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_config.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_config.py
@@ -4,7 +4,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-
 PARAM_RANGE = {
     # The following ranges are in percentage. e.g. 0.8 means 80%.
     "mass": [0.8, 1.2],
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_from_config.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_from_config.py
index 97b91609b..a550f35a0 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_from_config.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_env_randomizer_from_config.py
@@ -7,11 +7,11 @@ from __future__ import print_function
 import functools
 import random
 
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
 parentdir = os.path.dirname(os.path.dirname(parentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import numpy as np
 import tensorflow as tf
@@ -32,8 +32,7 @@ class MinitaurEnvRandomizerFromConfig(env_randomizer_base.EnvRandomizerBase):
     except AttributeError:
       raise ValueError("Config {} is not found.".format(config))
     self._randomization_param_dict = config()
-    tf.logging.info("Randomization config is: {}".format(
-        self._randomization_param_dict))
+    tf.logging.info("Randomization config is: {}".format(self._randomization_param_dict))
 
   def randomize_env(self, env):
     """Randomize various physical properties of the environment.
@@ -43,35 +42,29 @@ class MinitaurEnvRandomizerFromConfig(env_randomizer_base.EnvRandomizerBase):
     Args:
       env: A minitaur gym environment.
     """
-    self._randomization_function_dict = self._build_randomization_function_dict(
-        env)
+    self._randomization_function_dict = self._build_randomization_function_dict(env)
     for param_name, random_range in self._randomization_param_dict.iteritems():
-      self._randomization_function_dict[param_name](
-          lower_bound=random_range[0], upper_bound=random_range[1])
+      self._randomization_function_dict[param_name](lower_bound=random_range[0],
+                                                    upper_bound=random_range[1])
 
   def _build_randomization_function_dict(self, env):
     func_dict = {}
-    func_dict["mass"] = functools.partial(
-        self._randomize_masses, minitaur=env.minitaur)
-    func_dict["inertia"] = functools.partial(
-        self._randomize_inertia, minitaur=env.minitaur)
-    func_dict["latency"] = functools.partial(
-        self._randomize_latency, minitaur=env.minitaur)
-    func_dict["joint friction"] = functools.partial(
-        self._randomize_joint_friction, minitaur=env.minitaur)
-    func_dict["motor friction"] = functools.partial(
-        self._randomize_motor_friction, minitaur=env.minitaur)
-    func_dict["restitution"] = functools.partial(
-        self._randomize_contact_restitution, minitaur=env.minitaur)
-    func_dict["lateral friction"] = functools.partial(
-        self._randomize_contact_friction, minitaur=env.minitaur)
-    func_dict["battery"] = functools.partial(
-        self._randomize_battery_level, minitaur=env.minitaur)
-    func_dict["motor strength"] = functools.partial(
-        self._randomize_motor_strength, minitaur=env.minitaur)
+    func_dict["mass"] = functools.partial(self._randomize_masses, minitaur=env.minitaur)
+    func_dict["inertia"] = functools.partial(self._randomize_inertia, minitaur=env.minitaur)
+    func_dict["latency"] = functools.partial(self._randomize_latency, minitaur=env.minitaur)
+    func_dict["joint friction"] = functools.partial(self._randomize_joint_friction,
+                                                    minitaur=env.minitaur)
+    func_dict["motor friction"] = functools.partial(self._randomize_motor_friction,
+                                                    minitaur=env.minitaur)
+    func_dict["restitution"] = functools.partial(self._randomize_contact_restitution,
+                                                 minitaur=env.minitaur)
+    func_dict["lateral friction"] = functools.partial(self._randomize_contact_friction,
+                                                      minitaur=env.minitaur)
+    func_dict["battery"] = functools.partial(self._randomize_battery_level, minitaur=env.minitaur)
+    func_dict["motor strength"] = functools.partial(self._randomize_motor_strength,
+                                                    minitaur=env.minitaur)
     # Settinmg control step needs access to the environment.
-    func_dict["control step"] = functools.partial(
-        self._randomize_control_step, env=env)
+    func_dict["control step"] = functools.partial(self._randomize_control_step, env=env)
     return func_dict
 
   def _randomize_control_step(self, env, lower_bound, upper_bound):
@@ -111,8 +104,8 @@ class MinitaurEnvRandomizerFromConfig(env_randomizer_base.EnvRandomizerBase):
 
   def _randomize_joint_friction(self, minitaur, lower_bound, upper_bound):
     num_knee_joints = minitaur.GetNumKneeJoints()
-    randomized_joint_frictions = np.random.uniform(
-        [lower_bound] * num_knee_joints, [upper_bound] * num_knee_joints)
+    randomized_joint_frictions = np.random.uniform([lower_bound] * num_knee_joints,
+                                                   [upper_bound] * num_knee_joints)
     minitaur.SetJointFriction(randomized_joint_frictions)
     tf.logging.info("joint friction is: {}".format(randomized_joint_frictions))
 
@@ -137,9 +130,7 @@ class MinitaurEnvRandomizerFromConfig(env_randomizer_base.EnvRandomizerBase):
     tf.logging.info("battery voltage is: {}".format(randomized_battery_voltage))
 
   def _randomize_motor_strength(self, minitaur, lower_bound, upper_bound):
-    randomized_motor_strength_ratios = np.random.uniform(
-        [lower_bound] * minitaur.num_motors,
-        [upper_bound] * minitaur.num_motors)
+    randomized_motor_strength_ratios = np.random.uniform([lower_bound] * minitaur.num_motors,
+                                                         [upper_bound] * minitaur.num_motors)
     minitaur.SetMotorStrengthRatios(randomized_motor_strength_ratios)
-    tf.logging.info(
-        "motor strength is: {}".format(randomized_motor_strength_ratios))
+    tf.logging.info("motor strength is: {}".format(randomized_motor_strength_ratios))
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_push_randomizer.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_push_randomizer.py
index e030c6ae4..8d9aa8e64 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_push_randomizer.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_push_randomizer.py
@@ -4,11 +4,11 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
 parentdir = os.path.dirname(os.path.dirname(parentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import math
 import numpy as np
@@ -50,12 +50,10 @@ class MinitaurPushRandomizer(env_randomizer_base.EnvRandomizerBase):
     self._perturbation_start_step = perturbation_start_step
     self._perturbation_interval_steps = perturbation_interval_steps
     self._perturbation_duration_steps = perturbation_duration_steps
-    self._horizontal_force_bound = (
-        horizontal_force_bound if horizontal_force_bound else
-        [_HORIZONTAL_FORCE_LOWER_BOUND, _HORIZONTAL_FORCE_UPPER_BOUND])
-    self._vertical_force_bound = (
-        vertical_force_bound if vertical_force_bound else
-        [_VERTICAL_FORCE_LOWER_BOUND, _VERTICAL_FORCE_UPPER_BOUND])
+    self._horizontal_force_bound = (horizontal_force_bound if horizontal_force_bound else
+                                    [_HORIZONTAL_FORCE_LOWER_BOUND, _HORIZONTAL_FORCE_UPPER_BOUND])
+    self._vertical_force_bound = (vertical_force_bound if vertical_force_bound else
+                                  [_VERTICAL_FORCE_LOWER_BOUND, _VERTICAL_FORCE_UPPER_BOUND])
 
   def randomize_env(self, env):
     """Randomizes the simulation environment.
@@ -75,23 +73,20 @@ class MinitaurPushRandomizer(env_randomizer_base.EnvRandomizerBase):
     """
     base_link_ids = env.minitaur.chassis_link_ids
     if env.env_step_counter % self._perturbation_interval_steps == 0:
-      self._applied_link_id = base_link_ids[np.random.randint(
-          0, len(base_link_ids))]
-      horizontal_force_magnitude = np.random.uniform(
-          self._horizontal_force_bound[0], self._horizontal_force_bound[1])
+      self._applied_link_id = base_link_ids[np.random.randint(0, len(base_link_ids))]
+      horizontal_force_magnitude = np.random.uniform(self._horizontal_force_bound[0],
+                                                     self._horizontal_force_bound[1])
       theta = np.random.uniform(0, 2 * math.pi)
-      vertical_force_magnitude = np.random.uniform(
-          self._vertical_force_bound[0], self._vertical_force_bound[1])
+      vertical_force_magnitude = np.random.uniform(self._vertical_force_bound[0],
+                                                   self._vertical_force_bound[1])
       self._applied_force = horizontal_force_magnitude * np.array(
-          [math.cos(theta), math.sin(theta), 0]) + np.array(
-              [0, 0, -vertical_force_magnitude])
+          [math.cos(theta), math.sin(theta), 0]) + np.array([0, 0, -vertical_force_magnitude])
 
     if (env.env_step_counter % self._perturbation_interval_steps <
         self._perturbation_duration_steps) and (env.env_step_counter >=
                                                 self._perturbation_start_step):
-      env.pybullet_client.applyExternalForce(
-          objectUniqueId=env.minitaur.quadruped,
-          linkIndex=self._applied_link_id,
-          forceObj=self._applied_force,
-          posObj=[0.0, 0.0, 0.0],
-          flags=env.pybullet_client.LINK_FRAME)
+      env.pybullet_client.applyExternalForce(objectUniqueId=env.minitaur.quadruped,
+                                             linkIndex=self._applied_link_id,
+                                             forceObj=self._applied_force,
+                                             posObj=[0.0, 0.0, 0.0],
+                                             flags=env.pybullet_client.LINK_FRAME)
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_terrain_randomizer.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_terrain_randomizer.py
index b7e5706b8..d5f33371e 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_terrain_randomizer.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_terrain_randomizer.py
@@ -4,11 +4,11 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
 parentdir = os.path.dirname(os.path.dirname(parentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import itertools
 import math
@@ -62,8 +62,7 @@ class PoissonDisc2D(object):
     self._grid = [None] * self._grid_size_x * self._grid_size_y
 
     # Generate the first sample point and set it as an active site.
-    first_sample = np.array(
-        np.random.random_sample(2)) * [grid_length, grid_width]
+    first_sample = np.array(np.random.random_sample(2)) * [grid_length, grid_width]
     self._active_list = [first_sample]
 
     # Also store the sample point in the grid.
@@ -114,8 +113,7 @@ class PoissonDisc2D(object):
     Returns:
       Whether the point is inside the grid.
     """
-    return (0 <= point[0] < self._grid_length) and (0 <= point[1] <
-                                                    self._grid_width)
+    return (0 <= point[0] < self._grid_length) and (0 <= point[1] < self._grid_width)
 
   def _is_in_range(self, index2d):
     """Checks if the cell ID is within the grid.
@@ -127,8 +125,7 @@ class PoissonDisc2D(object):
       Whether the cell (2D index) is inside the grid.
     """
 
-    return (0 <= index2d[0] < self._grid_size_x) and (0 <= index2d[1] <
-                                                      self._grid_size_y)
+    return (0 <= index2d[0] < self._grid_size_x) and (0 <= index2d[1] < self._grid_size_y)
 
   def _is_close_to_existing_points(self, point):
     """Checks if the point is close to any already sampled (and stored) points.
@@ -142,15 +139,13 @@ class PoissonDisc2D(object):
     """
     px, py = self._point_to_index_2d(point)
     # Now we can check nearby cells for existing points
-    for neighbor_cell in itertools.product(
-        xrange(px - 1, px + 2), xrange(py - 1, py + 2)):
+    for neighbor_cell in itertools.product(xrange(px - 1, px + 2), xrange(py - 1, py + 2)):
 
       if not self._is_in_range(neighbor_cell):
         continue
 
       maybe_a_point = self._grid[self._index_2d_to_1d(neighbor_cell)]
-      if maybe_a_point is not None and np.linalg.norm(
-          maybe_a_point - point) < self._min_radius:
+      if maybe_a_point is not None and np.linalg.norm(maybe_a_point - point) < self._min_radius:
         return True
 
     return False
@@ -168,8 +163,8 @@ class PoissonDisc2D(object):
       random_angle = np.random.uniform(0, 2 * math.pi)
 
       # The sampled 2D points near the active point
-      sample = random_radius * np.array(
-          [np.cos(random_angle), np.sin(random_angle)]) + active_point
+      sample = random_radius * np.array([np.cos(random_angle),
+                                         np.sin(random_angle)]) + active_point
 
       if not self._is_in_grid(sample):
         continue
@@ -214,12 +209,11 @@ class TerrainType(enum.Enum):
 class MinitaurTerrainRandomizer(env_randomizer_base.EnvRandomizerBase):
   """Generates an uneven terrain in the gym env."""
 
-  def __init__(
-      self,
-      terrain_type=TerrainType.TRIANGLE_MESH,
-      mesh_filename="robotics/reinforcement_learning/minitaur/envs/testdata/"
-      "triangle_mesh_terrain/terrain9735.obj",
-      mesh_scale=None):
+  def __init__(self,
+               terrain_type=TerrainType.TRIANGLE_MESH,
+               mesh_filename="robotics/reinforcement_learning/minitaur/envs/testdata/"
+               "triangle_mesh_terrain/terrain9735.obj",
+               mesh_scale=None):
     """Initializes the randomizer.
 
     Args:
@@ -261,9 +255,7 @@ class MinitaurTerrainRandomizer(env_randomizer_base.EnvRandomizerBase):
         flags=1,
         meshScale=self._mesh_scale)
     env.ground_id = env.pybullet_client.createMultiBody(
-        baseMass=0,
-        baseCollisionShapeIndex=terrain_collision_shape_id,
-        basePosition=[0, 0, 0])
+        baseMass=0, baseCollisionShapeIndex=terrain_collision_shape_id, basePosition=[0, 0, 0])
 
   def _generate_convex_blocks(self, env):
     """Adds random convex blocks to the flat ground.
@@ -277,8 +269,7 @@ class MinitaurTerrainRandomizer(env_randomizer_base.EnvRandomizerBase):
 
     """
 
-    poisson_disc = PoissonDisc2D(_GRID_LENGTH, _GRID_WIDTH, _MIN_BLOCK_DISTANCE,
-                                 _MAX_SAMPLE_SIZE)
+    poisson_disc = PoissonDisc2D(_GRID_LENGTH, _GRID_WIDTH, _MIN_BLOCK_DISTANCE, _MAX_SAMPLE_SIZE)
 
     block_centers = poisson_disc.generate()
 
@@ -289,12 +280,10 @@ class MinitaurTerrainRandomizer(env_randomizer_base.EnvRandomizerBase):
       # Do not place blocks near the point [0, 0], where the robot will start.
       if abs(shifted_center[0]) < 1.0 and abs(shifted_center[1]) < 1.0:
         continue
-      half_length = np.random.uniform(_MIN_BLOCK_LENGTH, _MAX_BLOCK_LENGTH) / (
-          2 * math.sqrt(2))
+      half_length = np.random.uniform(_MIN_BLOCK_LENGTH, _MAX_BLOCK_LENGTH) / (2 * math.sqrt(2))
       half_height = np.random.uniform(_MIN_BLOCK_HEIGHT, _MAX_BLOCK_HEIGHT) / 2
       box_id = env.pybullet_client.createCollisionShape(
-          env.pybullet_client.GEOM_BOX,
-          halfExtents=[half_length, half_length, half_height])
+          env.pybullet_client.GEOM_BOX, halfExtents=[half_length, half_length, half_height])
       env.pybullet_client.createMultiBody(
           baseMass=0,
           baseCollisionShapeIndex=box_id,
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur.py
index efe32837c..fc403c0d5 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur.py
@@ -2,10 +2,10 @@
 
 """
 
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import collections
 import copy
@@ -24,9 +24,8 @@ OVERHEAT_SHUTDOWN_TORQUE = 2.45
 OVERHEAT_SHUTDOWN_TIME = 1.0
 LEG_POSITION = ["front_left", "back_left", "front_right", "back_right"]
 MOTOR_NAMES = [
-    "motor_front_leftL_joint", "motor_front_leftR_joint",
-    "motor_back_leftL_joint", "motor_back_leftR_joint",
-    "motor_front_rightL_joint", "motor_front_rightR_joint",
+    "motor_front_leftL_joint", "motor_front_leftR_joint", "motor_back_leftL_joint",
+    "motor_back_leftR_joint", "motor_front_rightL_joint", "motor_front_rightR_joint",
     "motor_back_rightL_joint", "motor_back_rightR_joint"
 ]
 _CHASSIS_NAME_PATTERN = re.compile(r"chassis\D*center")
@@ -141,10 +140,9 @@ class Minitaur(object):
     if self._accurate_motor_model_enabled:
       self._kp = motor_kp
       self._kd = motor_kd
-      self._motor_model = motor.MotorModel(
-          torque_control_enabled=self._torque_control_enabled,
-          kp=self._kp,
-          kd=self._kd)
+      self._motor_model = motor.MotorModel(torque_control_enabled=self._torque_control_enabled,
+                                           kp=self._kp,
+                                           kd=self._kd)
     elif self._pd_control_enabled:
       self._kp = 8
       self._kd = 0.3
@@ -188,17 +186,14 @@ class Minitaur(object):
     self._link_urdf = []
     num_bodies = self._pybullet_client.getNumJoints(self.quadruped)
     for body_id in range(-1, num_bodies):  # -1 is for the base link.
-      inertia = self._pybullet_client.getDynamicsInfo(self.quadruped,
-                                                      body_id)[2]
+      inertia = self._pybullet_client.getDynamicsInfo(self.quadruped, body_id)[2]
       self._link_urdf.append(inertia)
     # We need to use id+1 to index self._link_urdf because it has the base
     # (index = -1) at the first element.
     self._base_inertia_urdf = [
         self._link_urdf[chassis_id + 1] for chassis_id in self._chassis_link_ids
     ]
-    self._leg_inertia_urdf = [
-        self._link_urdf[leg_id + 1] for leg_id in self._leg_link_ids
-    ]
+    self._leg_inertia_urdf = [self._link_urdf[leg_id + 1] for leg_id in self._leg_link_ids]
     self._leg_inertia_urdf.extend(
         [self._link_urdf[motor_id + 1] for motor_id in self._motor_link_ids])
 
@@ -239,13 +234,10 @@ class Minitaur(object):
     num_joints = self._pybullet_client.getNumJoints(self.quadruped)
     for i in range(num_joints):
       joint_info = self._pybullet_client.getJointInfo(self.quadruped, i)
-      self._pybullet_client.changeDynamics(
-          joint_info[0], -1, linearDamping=0, angularDamping=0)
+      self._pybullet_client.changeDynamics(joint_info[0], -1, linearDamping=0, angularDamping=0)
 
   def _BuildMotorIdList(self):
-    self._motor_id_list = [
-        self._joint_name_to_id[motor_name] for motor_name in MOTOR_NAMES
-    ]
+    self._motor_id_list = [self._joint_name_to_id[motor_name] for motor_name in MOTOR_NAMES]
 
   def IsObservationValid(self):
     """Whether the observation is valid for the current time step.
@@ -284,10 +276,10 @@ class Minitaur(object):
             useFixedBase=self._on_rack,
             flags=self._pybullet_client.URDF_USE_SELF_COLLISION)
       else:
-        self.quadruped = self._pybullet_client.loadURDF(
-            "%s/quadruped/minitaur.urdf" % self._urdf_root,
-            init_position,
-            useFixedBase=self._on_rack)
+        self.quadruped = self._pybullet_client.loadURDF("%s/quadruped/minitaur.urdf" %
+                                                        self._urdf_root,
+                                                        init_position,
+                                                        useFixedBase=self._on_rack)
       self._BuildJointNameToIdDict()
       self._BuildUrdfIds()
       if self._remove_default_joint_damping:
@@ -297,10 +289,9 @@ class Minitaur(object):
       self._RecordInertiaInfoFromURDF()
       self.ResetPose(add_constraint=True)
     else:
-      self._pybullet_client.resetBasePositionAndOrientation(
-          self.quadruped, init_position, INIT_ORIENTATION)
-      self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0],
-                                              [0, 0, 0])
+      self._pybullet_client.resetBasePositionAndOrientation(self.quadruped, init_position,
+                                                            INIT_ORIENTATION)
+      self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0], [0, 0, 0])
       self.ResetPose(add_constraint=False)
     self._overheat_counter = np.zeros(self.num_motors)
     self._motor_enabled_list = [True] * self.num_motors
@@ -325,25 +316,22 @@ class Minitaur(object):
     self.ReceiveObservation()
 
   def _SetMotorTorqueById(self, motor_id, torque):
-    self._pybullet_client.setJointMotorControl2(
-        bodyIndex=self.quadruped,
-        jointIndex=motor_id,
-        controlMode=self._pybullet_client.TORQUE_CONTROL,
-        force=torque)
+    self._pybullet_client.setJointMotorControl2(bodyIndex=self.quadruped,
+                                                jointIndex=motor_id,
+                                                controlMode=self._pybullet_client.TORQUE_CONTROL,
+                                                force=torque)
 
   def _SetDesiredMotorAngleById(self, motor_id, desired_angle):
-    self._pybullet_client.setJointMotorControl2(
-        bodyIndex=self.quadruped,
-        jointIndex=motor_id,
-        controlMode=self._pybullet_client.POSITION_CONTROL,
-        targetPosition=desired_angle,
-        positionGain=self._kp,
-        velocityGain=self._kd,
-        force=self._max_force)
+    self._pybullet_client.setJointMotorControl2(bodyIndex=self.quadruped,
+                                                jointIndex=motor_id,
+                                                controlMode=self._pybullet_client.POSITION_CONTROL,
+                                                targetPosition=desired_angle,
+                                                positionGain=self._kp,
+                                                velocityGain=self._kd,
+                                                force=self._max_force)
 
   def _SetDesiredMotorAngleByName(self, motor_name, desired_angle):
-    self._SetDesiredMotorAngleById(self._joint_name_to_id[motor_name],
-                                   desired_angle)
+    self._SetDesiredMotorAngleById(self._joint_name_to_id[motor_name], desired_angle)
 
   def ResetPose(self, add_constraint):
     """Reset the pose of the minitaur.
@@ -367,59 +355,53 @@ class Minitaur(object):
     knee_angle = -2.1834
 
     leg_position = LEG_POSITION[leg_id]
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["motor_" + leg_position + "L_joint"],
-        self._motor_direction[2 * leg_id] * half_pi,
-        targetVelocity=0)
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["knee_" + leg_position + "L_link"],
-        self._motor_direction[2 * leg_id] * knee_angle,
-        targetVelocity=0)
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["motor_" + leg_position + "R_joint"],
-        self._motor_direction[2 * leg_id + 1] * half_pi,
-        targetVelocity=0)
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["knee_" + leg_position + "R_link"],
-        self._motor_direction[2 * leg_id + 1] * knee_angle,
-        targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["motor_" + leg_position +
+                                                                 "L_joint"],
+                                          self._motor_direction[2 * leg_id] * half_pi,
+                                          targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["knee_" + leg_position +
+                                                                 "L_link"],
+                                          self._motor_direction[2 * leg_id] * knee_angle,
+                                          targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["motor_" + leg_position +
+                                                                 "R_joint"],
+                                          self._motor_direction[2 * leg_id + 1] * half_pi,
+                                          targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["knee_" + leg_position +
+                                                                 "R_link"],
+                                          self._motor_direction[2 * leg_id + 1] * knee_angle,
+                                          targetVelocity=0)
     if add_constraint:
       self._pybullet_client.createConstraint(
-          self.quadruped,
-          self._joint_name_to_id["knee_" + leg_position + "R_link"],
-          self.quadruped,
-          self._joint_name_to_id["knee_" + leg_position + "L_link"],
-          self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0],
-          KNEE_CONSTRAINT_POINT_RIGHT, KNEE_CONSTRAINT_POINT_LEFT)
+          self.quadruped, self._joint_name_to_id["knee_" + leg_position + "R_link"],
+          self.quadruped, self._joint_name_to_id["knee_" + leg_position + "L_link"],
+          self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], KNEE_CONSTRAINT_POINT_RIGHT,
+          KNEE_CONSTRAINT_POINT_LEFT)
 
     if self._accurate_motor_model_enabled or self._pd_control_enabled:
       # Disable the default motor in pybullet.
       self._pybullet_client.setJointMotorControl2(
           bodyIndex=self.quadruped,
-          jointIndex=(
-              self._joint_name_to_id["motor_" + leg_position + "L_joint"]),
+          jointIndex=(self._joint_name_to_id["motor_" + leg_position + "L_joint"]),
           controlMode=self._pybullet_client.VELOCITY_CONTROL,
           targetVelocity=0,
           force=knee_friction_force)
       self._pybullet_client.setJointMotorControl2(
           bodyIndex=self.quadruped,
-          jointIndex=(
-              self._joint_name_to_id["motor_" + leg_position + "R_joint"]),
+          jointIndex=(self._joint_name_to_id["motor_" + leg_position + "R_joint"]),
           controlMode=self._pybullet_client.VELOCITY_CONTROL,
           targetVelocity=0,
           force=knee_friction_force)
 
     else:
-      self._SetDesiredMotorAngleByName(
-          "motor_" + leg_position + "L_joint",
-          self._motor_direction[2 * leg_id] * half_pi)
-      self._SetDesiredMotorAngleByName(
-          "motor_" + leg_position + "R_joint",
-          self._motor_direction[2 * leg_id + 1] * half_pi)
+      self._SetDesiredMotorAngleByName("motor_" + leg_position + "L_joint",
+                                       self._motor_direction[2 * leg_id] * half_pi)
+      self._SetDesiredMotorAngleByName("motor_" + leg_position + "R_joint",
+                                       self._motor_direction[2 * leg_id + 1] * half_pi)
 
     self._pybullet_client.setJointMotorControl2(
         bodyIndex=self.quadruped,
@@ -440,8 +422,7 @@ class Minitaur(object):
     Returns:
       The position of minitaur's base.
     """
-    position, _ = (
-        self._pybullet_client.getBasePositionAndOrientation(self.quadruped))
+    position, _ = (self._pybullet_client.getBasePositionAndOrientation(self.quadruped))
     return position
 
   def GetTrueBaseRollPitchYaw(self):
@@ -464,10 +445,9 @@ class Minitaur(object):
     """
     delayed_orientation = np.array(
         self._control_observation[3 * self.num_motors:3 * self.num_motors + 4])
-    delayed_roll_pitch_yaw = self._pybullet_client.getEulerFromQuaternion(
-        delayed_orientation)
-    roll_pitch_yaw = self._AddSensorNoise(
-        np.array(delayed_roll_pitch_yaw), self._observation_noise_stdev[3])
+    delayed_roll_pitch_yaw = self._pybullet_client.getEulerFromQuaternion(delayed_orientation)
+    roll_pitch_yaw = self._AddSensorNoise(np.array(delayed_roll_pitch_yaw),
+                                          self._observation_noise_stdev[3])
     return roll_pitch_yaw
 
   def GetTrueMotorAngles(self):
@@ -492,9 +472,8 @@ class Minitaur(object):
     Returns:
       Motor angles polluted by noise and latency, mapped to [-pi, pi].
     """
-    motor_angles = self._AddSensorNoise(
-        np.array(self._control_observation[0:self.num_motors]),
-        self._observation_noise_stdev[0])
+    motor_angles = self._AddSensorNoise(np.array(self._control_observation[0:self.num_motors]),
+                                        self._observation_noise_stdev[0])
     return MapToMinusPiToPi(motor_angles)
 
   def GetTrueMotorVelocities(self):
@@ -518,8 +497,7 @@ class Minitaur(object):
       Velocities of all eight motors polluted by noise and latency.
     """
     return self._AddSensorNoise(
-        np.array(
-            self._control_observation[self.num_motors:2 * self.num_motors]),
+        np.array(self._control_observation[self.num_motors:2 * self.num_motors]),
         self._observation_noise_stdev[1])
 
   def GetTrueMotorTorques(self):
@@ -546,8 +524,7 @@ class Minitaur(object):
       Motor torques of all eight motors polluted by noise and latency.
     """
     return self._AddSensorNoise(
-        np.array(
-            self._control_observation[2 * self.num_motors:3 * self.num_motors]),
+        np.array(self._control_observation[2 * self.num_motors:3 * self.num_motors]),
         self._observation_noise_stdev[2])
 
   def GetTrueBaseOrientation(self):
@@ -556,8 +533,7 @@ class Minitaur(object):
     Returns:
       The orientation of minitaur's base.
     """
-    _, orientation = (
-        self._pybullet_client.getBasePositionAndOrientation(self.quadruped))
+    _, orientation = (self._pybullet_client.getBasePositionAndOrientation(self.quadruped))
     return orientation
 
   def GetBaseOrientation(self):
@@ -567,8 +543,7 @@ class Minitaur(object):
     Returns:
       The orientation of minitaur's base polluted by noise and latency.
     """
-    return self._pybullet_client.getQuaternionFromEuler(
-        self.GetBaseRollPitchYaw())
+    return self._pybullet_client.getQuaternionFromEuler(self.GetBaseRollPitchYaw())
 
   def GetTrueBaseRollPitchYawRate(self):
     """Get the rate of orientation change of the minitaur's base in euler angle.
@@ -588,8 +563,7 @@ class Minitaur(object):
       and latency.
     """
     return self._AddSensorNoise(
-        np.array(self._control_observation[3 * self.num_motors + 4:
-                                           3 * self.num_motors + 7]),
+        np.array(self._control_observation[3 * self.num_motors + 4:3 * self.num_motors + 7]),
         self._observation_noise_stdev[4])
 
   def GetActionDimension(self):
@@ -618,12 +592,9 @@ class Minitaur(object):
     """
     if self._motor_velocity_limit < np.inf:
       current_motor_angle = self.GetTrueMotorAngles()
-      motor_commands_max = (
-          current_motor_angle + self.time_step * self._motor_velocity_limit)
-      motor_commands_min = (
-          current_motor_angle - self.time_step * self._motor_velocity_limit)
-      motor_commands = np.clip(motor_commands, motor_commands_min,
-                               motor_commands_max)
+      motor_commands_max = (current_motor_angle + self.time_step * self._motor_velocity_limit)
+      motor_commands_min = (current_motor_angle - self.time_step * self._motor_velocity_limit)
+      motor_commands = np.clip(motor_commands, motor_commands_min, motor_commands_max)
     # Set the kp and kd for all the motors if not provided as an argument.
     if motor_kps is None:
       motor_kps = np.full(8, self._kp)
@@ -642,8 +613,7 @@ class Minitaur(object):
               self._overheat_counter[i] += 1
             else:
               self._overheat_counter[i] = 0
-            if (self._overheat_counter[i] >
-                OVERHEAT_SHUTDOWN_TIME / self.time_step):
+            if (self._overheat_counter[i] > OVERHEAT_SHUTDOWN_TIME / self.time_step):
               self._motor_enabled_list[i] = False
 
         # The torque is already in the observation space because we use
@@ -651,19 +621,17 @@ class Minitaur(object):
         self._observed_motor_torques = observed_torque
 
         # Transform into the motor space when applying the torque.
-        self._applied_motor_torque = np.multiply(actual_torque,
-                                                 self._motor_direction)
+        self._applied_motor_torque = np.multiply(actual_torque, self._motor_direction)
 
-        for motor_id, motor_torque, motor_enabled in zip(
-            self._motor_id_list, self._applied_motor_torque,
-            self._motor_enabled_list):
+        for motor_id, motor_torque, motor_enabled in zip(self._motor_id_list,
+                                                         self._applied_motor_torque,
+                                                         self._motor_enabled_list):
           if motor_enabled:
             self._SetMotorTorqueById(motor_id, motor_torque)
           else:
             self._SetMotorTorqueById(motor_id, 0)
       else:
-        torque_commands = -1 * motor_kps * (
-            q - motor_commands) - motor_kds * qdot
+        torque_commands = -1 * motor_kps * (q - motor_commands) - motor_kds * qdot
 
         # The torque is already in the observation space because we use
         # GetMotorAngles and GetMotorVelocities.
@@ -673,14 +641,12 @@ class Minitaur(object):
         self._applied_motor_torques = np.multiply(self._observed_motor_torques,
                                                   self._motor_direction)
 
-        for motor_id, motor_torque in zip(self._motor_id_list,
-                                          self._applied_motor_torques):
+        for motor_id, motor_torque in zip(self._motor_id_list, self._applied_motor_torques):
           self._SetMotorTorqueById(motor_id, motor_torque)
     else:
-      motor_commands_with_direction = np.multiply(motor_commands,
-                                                  self._motor_direction)
-      for motor_id, motor_command_with_direction in zip(
-          self._motor_id_list, motor_commands_with_direction):
+      motor_commands_with_direction = np.multiply(motor_commands, self._motor_direction)
+      for motor_id, motor_command_with_direction in zip(self._motor_id_list,
+                                                        motor_commands_with_direction):
         self._SetDesiredMotorAngleById(motor_id, motor_command_with_direction)
 
   def ConvertFromLegModel(self, actions):
@@ -698,13 +664,13 @@ class Minitaur(object):
     quater_pi = math.pi / 4
     for i in range(self.num_motors):
       action_idx = int(i // 2)
-      forward_backward_component = (-scale_for_singularity * quater_pi * (
-          actions[action_idx + half_num_motors] + offset_for_singularity))
+      forward_backward_component = (
+          -scale_for_singularity * quater_pi *
+          (actions[action_idx + half_num_motors] + offset_for_singularity))
       extension_component = (-1)**i * quater_pi * actions[action_idx]
       if i >= half_num_motors:
         extension_component = -extension_component
-      motor_angle[i] = (
-          math.pi + forward_backward_component + extension_component)
+      motor_angle[i] = (math.pi + forward_backward_component + extension_component)
     return motor_angle
 
   def GetBaseMassesFromURDF(self):
@@ -734,12 +700,10 @@ class Minitaur(object):
         the length of self._chassis_link_ids.
     """
     if len(base_mass) != len(self._chassis_link_ids):
-      raise ValueError(
-          "The length of base_mass {} and self._chassis_link_ids {} are not "
-          "the same.".format(len(base_mass), len(self._chassis_link_ids)))
+      raise ValueError("The length of base_mass {} and self._chassis_link_ids {} are not "
+                       "the same.".format(len(base_mass), len(self._chassis_link_ids)))
     for chassis_id, chassis_mass in zip(self._chassis_link_ids, base_mass):
-      self._pybullet_client.changeDynamics(
-          self.quadruped, chassis_id, mass=chassis_mass)
+      self._pybullet_client.changeDynamics(self.quadruped, chassis_id, mass=chassis_mass)
 
   def SetLegMasses(self, leg_masses):
     """Set the mass of the legs.
@@ -759,12 +723,10 @@ class Minitaur(object):
       raise ValueError("The number of values passed to SetLegMasses are "
                        "different than number of leg links and motors.")
     for leg_id, leg_mass in zip(self._leg_link_ids, leg_masses):
-      self._pybullet_client.changeDynamics(
-          self.quadruped, leg_id, mass=leg_mass)
+      self._pybullet_client.changeDynamics(self.quadruped, leg_id, mass=leg_mass)
     motor_masses = leg_masses[len(self._leg_link_ids):]
     for link_id, motor_mass in zip(self._motor_link_ids, motor_masses):
-      self._pybullet_client.changeDynamics(
-          self.quadruped, link_id, mass=motor_mass)
+      self._pybullet_client.changeDynamics(self.quadruped, link_id, mass=motor_mass)
 
   def SetBaseInertias(self, base_inertias):
     """Set the inertias of minitaur's base.
@@ -779,17 +741,15 @@ class Minitaur(object):
         negative values.
     """
     if len(base_inertias) != len(self._chassis_link_ids):
-      raise ValueError(
-          "The length of base_inertias {} and self._chassis_link_ids {} are "
-          "not the same.".format(
-              len(base_inertias), len(self._chassis_link_ids)))
-    for chassis_id, chassis_inertia in zip(self._chassis_link_ids,
-                                           base_inertias):
+      raise ValueError("The length of base_inertias {} and self._chassis_link_ids {} are "
+                       "not the same.".format(len(base_inertias), len(self._chassis_link_ids)))
+    for chassis_id, chassis_inertia in zip(self._chassis_link_ids, base_inertias):
       for inertia_value in chassis_inertia:
         if (np.asarray(inertia_value) < 0).any():
           raise ValueError("Values in inertia matrix should be non-negative.")
-      self._pybullet_client.changeDynamics(
-          self.quadruped, chassis_id, localInertiaDiagonal=chassis_inertia)
+      self._pybullet_client.changeDynamics(self.quadruped,
+                                           chassis_id,
+                                           localInertiaDiagonal=chassis_inertia)
 
   def SetLegInertias(self, leg_inertias):
     """Set the inertias of the legs.
@@ -813,16 +773,18 @@ class Minitaur(object):
       for inertia_value in leg_inertias:
         if (np.asarray(inertia_value) < 0).any():
           raise ValueError("Values in inertia matrix should be non-negative.")
-      self._pybullet_client.changeDynamics(
-          self.quadruped, leg_id, localInertiaDiagonal=leg_inertia)
+      self._pybullet_client.changeDynamics(self.quadruped,
+                                           leg_id,
+                                           localInertiaDiagonal=leg_inertia)
 
     motor_inertias = leg_inertias[len(self._leg_link_ids):]
     for link_id, motor_inertia in zip(self._motor_link_ids, motor_inertias):
       for inertia_value in motor_inertias:
         if (np.asarray(inertia_value) < 0).any():
           raise ValueError("Values in inertia matrix should be non-negative.")
-      self._pybullet_client.changeDynamics(
-          self.quadruped, link_id, localInertiaDiagonal=motor_inertia)
+      self._pybullet_client.changeDynamics(self.quadruped,
+                                           link_id,
+                                           localInertiaDiagonal=motor_inertia)
 
   def SetFootFriction(self, foot_friction):
     """Set the lateral friction of the feet.
@@ -832,8 +794,7 @@ class Minitaur(object):
         shared by all four feet.
     """
     for link_id in self._foot_link_ids:
-      self._pybullet_client.changeDynamics(
-          self.quadruped, link_id, lateralFriction=foot_friction)
+      self._pybullet_client.changeDynamics(self.quadruped, link_id, lateralFriction=foot_friction)
 
   # TODO(b/73748980): Add more API's to set other contact parameters.
   def SetFootRestitution(self, foot_restitution):
@@ -844,8 +805,7 @@ class Minitaur(object):
         This value is shared by all four feet.
     """
     for link_id in self._foot_link_ids:
-      self._pybullet_client.changeDynamics(
-          self.quadruped, link_id, restitution=foot_restitution)
+      self._pybullet_client.changeDynamics(self.quadruped, link_id, restitution=foot_restitution)
 
   def SetJointFriction(self, joint_frictions):
     for knee_joint_id, friction in zip(self._foot_link_ids, joint_frictions):
@@ -901,9 +861,8 @@ class Minitaur(object):
         return self._observation_history[-1]
       remaining_latency = latency - n_steps_ago * self.time_step
       blend_alpha = remaining_latency / self.time_step
-      observation = (
-          (1.0 - blend_alpha) * np.array(self._observation_history[n_steps_ago])
-          + blend_alpha * np.array(self._observation_history[n_steps_ago + 1]))
+      observation = ((1.0 - blend_alpha) * np.array(self._observation_history[n_steps_ago]) +
+                     blend_alpha * np.array(self._observation_history[n_steps_ago + 1]))
     return observation
 
   def _GetPDObservation(self):
@@ -913,15 +872,13 @@ class Minitaur(object):
     return (np.array(q), np.array(qdot))
 
   def _GetControlObservation(self):
-    control_delayed_observation = self._GetDelayedObservation(
-        self._control_latency)
+    control_delayed_observation = self._GetDelayedObservation(self._control_latency)
     return control_delayed_observation
 
   def _AddSensorNoise(self, sensor_values, noise_stdev):
     if noise_stdev <= 0:
       return sensor_values
-    observation = sensor_values + np.random.normal(
-        scale=noise_stdev, size=sensor_values.shape)
+    observation = sensor_values + np.random.normal(scale=noise_stdev, size=sensor_values.shape)
     return observation
 
   def SetControlLatency(self, latency):
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env.py
index 9529062fa..fa177b7b5 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env.py
@@ -3,10 +3,10 @@
 """
 import math
 
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 from gym import spaces
 import numpy as np
@@ -31,10 +31,7 @@ class MinitaurAlternatingLegsEnv(minitaur_gym_env.MinitaurGymEnv):
   expenditure.
 
   """
-  metadata = {
-      "render.modes": ["human", "rgb_array"],
-      "video.frames_per_second": 66
-  }
+  metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 66}
 
   def __init__(self,
                urdf_version=None,
@@ -81,23 +78,23 @@ class MinitaurAlternatingLegsEnv(minitaur_gym_env.MinitaurGymEnv):
     # _swing_offset and _extension_offset is to mimick the bent legs.
     self._swing_offset = np.zeros(NUM_LEGS)
     self._extension_offset = np.zeros(NUM_LEGS)
-    super(MinitaurAlternatingLegsEnv, self).__init__(
-        urdf_version=urdf_version,
-        accurate_motor_model_enabled=True,
-        motor_overheat_protection=True,
-        hard_reset=False,
-        motor_kp=motor_kp,
-        motor_kd=motor_kd,
-        remove_default_joint_damping=remove_default_joint_damping,
-        control_latency=control_latency,
-        pd_latency=pd_latency,
-        on_rack=on_rack,
-        render=render,
-        num_steps_to_log=num_steps_to_log,
-        env_randomizer=env_randomizer,
-        log_path=log_path,
-        control_time_step=control_time_step,
-        action_repeat=action_repeat)
+    super(MinitaurAlternatingLegsEnv,
+          self).__init__(urdf_version=urdf_version,
+                         accurate_motor_model_enabled=True,
+                         motor_overheat_protection=True,
+                         hard_reset=False,
+                         motor_kp=motor_kp,
+                         motor_kd=motor_kd,
+                         remove_default_joint_damping=remove_default_joint_damping,
+                         control_latency=control_latency,
+                         pd_latency=pd_latency,
+                         on_rack=on_rack,
+                         render=render,
+                         num_steps_to_log=num_steps_to_log,
+                         env_randomizer=env_randomizer,
+                         log_path=log_path,
+                         control_time_step=control_time_step,
+                         action_repeat=action_repeat)
 
     action_dim = 8
     action_high = np.array([0.1] * action_dim)
@@ -113,33 +110,29 @@ class MinitaurAlternatingLegsEnv(minitaur_gym_env.MinitaurGymEnv):
     # [swing leg 1, swing leg 2, swing leg 3, swing leg 4,
     #  extension leg 1, extension leg 2, extension leg 3, extension leg 4]
     init_pose = [
-        INIT_SWING_POS + self._swing_offset[0],
-        INIT_SWING_POS + self._swing_offset[1],
-        INIT_SWING_POS + self._swing_offset[2],
-        INIT_SWING_POS + self._swing_offset[3],
+        INIT_SWING_POS + self._swing_offset[0], INIT_SWING_POS + self._swing_offset[1],
+        INIT_SWING_POS + self._swing_offset[2], INIT_SWING_POS + self._swing_offset[3],
         INIT_EXTENSION_POS + self._extension_offset[0],
         INIT_EXTENSION_POS + self._extension_offset[1],
         INIT_EXTENSION_POS + self._extension_offset[2],
         INIT_EXTENSION_POS + self._extension_offset[3]
     ]
     initial_motor_angles = self._convert_from_leg_model(init_pose)
-    super(MinitaurAlternatingLegsEnv, self).reset(
-        initial_motor_angles=initial_motor_angles, reset_duration=0.5)
+    super(MinitaurAlternatingLegsEnv, self).reset(initial_motor_angles=initial_motor_angles,
+                                                  reset_duration=0.5)
     return self._get_observation()
 
   def _convert_from_leg_model(self, leg_pose):
     motor_pose = np.zeros(NUM_MOTORS)
     for i in range(NUM_LEGS):
       motor_pose[2 * i] = leg_pose[NUM_LEGS + i] - (-1)**(i / 2) * leg_pose[i]
-      motor_pose[2 * i
-                 + 1] = leg_pose[NUM_LEGS + i] + (-1)**(i / 2) * leg_pose[i]
+      motor_pose[2 * i + 1] = leg_pose[NUM_LEGS + i] + (-1)**(i / 2) * leg_pose[i]
     return motor_pose
 
   def _signal(self, t):
     initial_pose = np.array([
-        INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS,
-        INIT_EXTENSION_POS, INIT_EXTENSION_POS, INIT_EXTENSION_POS,
-        INIT_EXTENSION_POS
+        INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_EXTENSION_POS,
+        INIT_EXTENSION_POS, INIT_EXTENSION_POS, INIT_EXTENSION_POS
     ])
     amplitude = STEP_AMPLITUDE
     period = STEP_PERIOD
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env_example.py
index f1266d354..226edf3c5 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env_example.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_alternating_legs_env_example.py
@@ -3,10 +3,10 @@
 """
 import time
 
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import os
 import numpy as np
@@ -50,13 +50,11 @@ def hand_tuned_agent(observation, timestamp):
   pitch_compensation = pitch_gain * pitch + pitch_dot_gain * pitch_dot
 
   first_leg = [
-      0, -pitch_compensation, -pitch_compensation, 0, 0,
-      -pitch_compensation - roll_compensation,
+      0, -pitch_compensation, -pitch_compensation, 0, 0, -pitch_compensation - roll_compensation,
       pitch_compensation + roll_compensation, 0
   ]
   second_leg = [
-      -pitch_compensation, 0, 0, -pitch_compensation,
-      pitch_compensation - roll_compensation, 0, 0,
+      -pitch_compensation, 0, 0, -pitch_compensation, pitch_compensation - roll_compensation, 0, 0,
       -pitch_compensation + roll_compensation
   ]
   if (timestamp // minitaur_alternating_legs_env.STEP_PERIOD) % 2:
@@ -94,8 +92,7 @@ def hand_tuned_balance_example(log_path=None):
     for _ in range(steps):
       # Sleep to prevent serial buffer overflow on microcontroller.
       time.sleep(0.002)
-      action = hand_tuned_agent(observation,
-                                environment.minitaur.GetTimeSinceReset())
+      action = hand_tuned_agent(observation, environment.minitaur.GetTimeSinceReset())
       observation, reward, done, _ = environment.step(action)
       sum_reward += reward
       if done:
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env.py
index c2ec7ded4..b40d6f0d4 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env.py
@@ -4,10 +4,10 @@
 import math
 import random
 
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 from gym import spaces
 import numpy as np
@@ -52,61 +52,52 @@ class MinitaurBallGymEnv(minitaur_gym_env.MinitaurGymEnv):
         that its walking gait is clearer to visualize.
       render: Whether to render the simulation.
     """
-    super(MinitaurBallGymEnv, self).__init__(
-        urdf_root=urdf_root,
-        self_collision_enabled=self_collision_enabled,
-        pd_control_enabled=pd_control_enabled,
-        leg_model_enabled=leg_model_enabled,
-        on_rack=on_rack,
-        render=render)
+    super(MinitaurBallGymEnv, self).__init__(urdf_root=urdf_root,
+                                             self_collision_enabled=self_collision_enabled,
+                                             pd_control_enabled=pd_control_enabled,
+                                             leg_model_enabled=leg_model_enabled,
+                                             on_rack=on_rack,
+                                             render=render)
     self._cam_dist = 2.0
     self._cam_yaw = -70
     self._cam_pitch = -30
     self.action_space = spaces.Box(np.array([-1]), np.array([1]))
-    self.observation_space = spaces.Box(np.array([-math.pi, 0]),
-                                        np.array([math.pi, 100]))
+    self.observation_space = spaces.Box(np.array([-math.pi, 0]), np.array([math.pi, 100]))
 
   def reset(self):
     self._ball_id = 0
     super(MinitaurBallGymEnv, self).reset()
     self._init_ball_theta = random.uniform(-INIT_BALL_ANGLE, INIT_BALL_ANGLE)
     self._init_ball_distance = INIT_BALL_DISTANCE
-    self._ball_pos = [self._init_ball_distance *
-                      math.cos(self._init_ball_theta),
-                      self._init_ball_distance *
-                      math.sin(self._init_ball_theta), 1]
+    self._ball_pos = [
+        self._init_ball_distance * math.cos(self._init_ball_theta),
+        self._init_ball_distance * math.sin(self._init_ball_theta), 1
+    ]
     self._ball_id = self._pybullet_client.loadURDF(
         "%s/sphere_with_restitution.urdf" % self._urdf_root, self._ball_pos)
     return self._get_observation()
 
   def _get_observation(self):
     world_translation_minitaur, world_rotation_minitaur = (
-        self._pybullet_client.getBasePositionAndOrientation(
-            self.minitaur.quadruped))
+        self._pybullet_client.getBasePositionAndOrientation(self.minitaur.quadruped))
     world_translation_ball, world_rotation_ball = (
         self._pybullet_client.getBasePositionAndOrientation(self._ball_id))
-    minitaur_translation_world, minitaur_rotation_world = (
-        self._pybullet_client.invertTransform(world_translation_minitaur,
-                                              world_rotation_minitaur))
-    minitaur_translation_ball, _ = (
-        self._pybullet_client.multiplyTransforms(minitaur_translation_world,
-                                                 minitaur_rotation_world,
-                                                 world_translation_ball,
-                                                 world_rotation_ball))
-    distance = math.sqrt(minitaur_translation_ball[0]**2 +
-                         minitaur_translation_ball[1]**2)
-    angle = math.atan2(minitaur_translation_ball[0],
-                       minitaur_translation_ball[1])
+    minitaur_translation_world, minitaur_rotation_world = (self._pybullet_client.invertTransform(
+        world_translation_minitaur, world_rotation_minitaur))
+    minitaur_translation_ball, _ = (self._pybullet_client.multiplyTransforms(
+        minitaur_translation_world, minitaur_rotation_world, world_translation_ball,
+        world_rotation_ball))
+    distance = math.sqrt(minitaur_translation_ball[0]**2 + minitaur_translation_ball[1]**2)
+    angle = math.atan2(minitaur_translation_ball[0], minitaur_translation_ball[1])
     self._observation = [angle - math.pi / 2, distance]
     return self._observation
 
   def _transform_action_to_motor_command(self, action):
     if self._leg_model_enabled:
       for i, action_component in enumerate(action):
-        if not (-self._action_bound - ACTION_EPS <=
-                action_component <= self._action_bound + ACTION_EPS):
-          raise ValueError("{}th action {} out of bounds.".format
-                           (i, action_component))
+        if not (-self._action_bound - ACTION_EPS <= action_component <=
+                self._action_bound + ACTION_EPS):
+          raise ValueError("{}th action {} out of bounds.".format(i, action_component))
       action = self._apply_steering_to_locomotion(action)
       action = self.minitaur.ConvertFromLegModel(action)
     return action
@@ -126,15 +117,12 @@ class MinitaurBallGymEnv(minitaur_gym_env.MinitaurGymEnv):
     return action
 
   def _distance_to_ball(self):
-    world_translation_minitaur, _ = (
-        self._pybullet_client.getBasePositionAndOrientation(
-            self.minitaur.quadruped))
-    world_translation_ball, _ = (
-        self._pybullet_client.getBasePositionAndOrientation(
-            self._ball_id))
-    distance = math.sqrt(
-        (world_translation_ball[0] - world_translation_minitaur[0])**2 +
-        (world_translation_ball[1] - world_translation_minitaur[1])**2)
+    world_translation_minitaur, _ = (self._pybullet_client.getBasePositionAndOrientation(
+        self.minitaur.quadruped))
+    world_translation_ball, _ = (self._pybullet_client.getBasePositionAndOrientation(
+        self._ball_id))
+    distance = math.sqrt((world_translation_ball[0] - world_translation_minitaur[0])**2 +
+                         (world_translation_ball[1] - world_translation_minitaur[1])**2)
     return distance
 
   def _goal_state(self):
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env_example.py
index b52b945ad..404dc26ac 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env_example.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_ball_gym_env_example.py
@@ -31,4 +31,4 @@ def main():
 
 
 if __name__ == '__main__':
-    main()
\ No newline at end of file
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_derpy.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_derpy.py
index 2b5eb2698..7946955d4 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_derpy.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_derpy.py
@@ -6,13 +6,13 @@ It is the result of first pass system identification for the derpy robot. The
 """
 import math
 
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import numpy as np
-from pybullet_envs.minitaur.envs  import minitaur
+from pybullet_envs.minitaur.envs import minitaur
 
 KNEE_CONSTRAINT_POINT_LONG = [0, 0.0055, 0.088]
 KNEE_CONSTRAINT_POINT_SHORT = [0, 0.0055, 0.100]
@@ -46,13 +46,12 @@ class MinitaurDerpy(minitaur.Minitaur):
             "%s/quadruped/minitaur_derpy.urdf" % self._urdf_root,
             init_position,
             useFixedBase=self._on_rack,
-            flags=(
-                self._pybullet_client.URDF_USE_SELF_COLLISION_EXCLUDE_PARENT))
+            flags=(self._pybullet_client.URDF_USE_SELF_COLLISION_EXCLUDE_PARENT))
       else:
-        self.quadruped = self._pybullet_client.loadURDF(
-            "%s/quadruped/minitaur_derpy.urdf" % self._urdf_root,
-            init_position,
-            useFixedBase=self._on_rack)
+        self.quadruped = self._pybullet_client.loadURDF("%s/quadruped/minitaur_derpy.urdf" %
+                                                        self._urdf_root,
+                                                        init_position,
+                                                        useFixedBase=self._on_rack)
       self._BuildJointNameToIdDict()
       self._BuildUrdfIds()
       if self._remove_default_joint_damping:
@@ -62,10 +61,9 @@ class MinitaurDerpy(minitaur.Minitaur):
       self._RecordInertiaInfoFromURDF()
       self.ResetPose(add_constraint=True)
     else:
-      self._pybullet_client.resetBasePositionAndOrientation(
-          self.quadruped, init_position, minitaur.INIT_ORIENTATION)
-      self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0],
-                                              [0, 0, 0])
+      self._pybullet_client.resetBasePositionAndOrientation(self.quadruped, init_position,
+                                                            minitaur.INIT_ORIENTATION)
+      self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0], [0, 0, 0])
       self.ResetPose(add_constraint=False)
 
     self._overheat_counter = np.zeros(self.num_motors)
@@ -103,68 +101,60 @@ class MinitaurDerpy(minitaur.Minitaur):
     knee_angle = -2.1834
 
     leg_position = minitaur.LEG_POSITION[leg_id]
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["motor_" + leg_position + "L_joint"],
-        self._motor_direction[2 * leg_id] * half_pi,
-        targetVelocity=0)
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["knee_" + leg_position + "L_joint"],
-        self._motor_direction[2 * leg_id] * knee_angle,
-        targetVelocity=0)
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["motor_" + leg_position + "R_joint"],
-        self._motor_direction[2 * leg_id + 1] * half_pi,
-        targetVelocity=0)
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["knee_" + leg_position + "R_joint"],
-        self._motor_direction[2 * leg_id + 1] * knee_angle,
-        targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["motor_" + leg_position +
+                                                                 "L_joint"],
+                                          self._motor_direction[2 * leg_id] * half_pi,
+                                          targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["knee_" + leg_position +
+                                                                 "L_joint"],
+                                          self._motor_direction[2 * leg_id] * knee_angle,
+                                          targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["motor_" + leg_position +
+                                                                 "R_joint"],
+                                          self._motor_direction[2 * leg_id + 1] * half_pi,
+                                          targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["knee_" + leg_position +
+                                                                 "R_joint"],
+                                          self._motor_direction[2 * leg_id + 1] * knee_angle,
+                                          targetVelocity=0)
     if add_constraint:
       if leg_id < 2:
         self._pybullet_client.createConstraint(
-            self.quadruped,
-            self._joint_name_to_id["knee_" + leg_position + "R_joint"],
-            self.quadruped,
-            self._joint_name_to_id["knee_" + leg_position + "L_joint"],
-            self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0],
-            KNEE_CONSTRAINT_POINT_SHORT, KNEE_CONSTRAINT_POINT_LONG)
+            self.quadruped, self._joint_name_to_id["knee_" + leg_position + "R_joint"],
+            self.quadruped, self._joint_name_to_id["knee_" + leg_position + "L_joint"],
+            self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], KNEE_CONSTRAINT_POINT_SHORT,
+            KNEE_CONSTRAINT_POINT_LONG)
       else:
         self._pybullet_client.createConstraint(
-            self.quadruped,
-            self._joint_name_to_id["knee_" + leg_position + "R_joint"],
-            self.quadruped,
-            self._joint_name_to_id["knee_" + leg_position + "L_joint"],
-            self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0],
-            KNEE_CONSTRAINT_POINT_LONG, KNEE_CONSTRAINT_POINT_SHORT)
+            self.quadruped, self._joint_name_to_id["knee_" + leg_position + "R_joint"],
+            self.quadruped, self._joint_name_to_id["knee_" + leg_position + "L_joint"],
+            self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], KNEE_CONSTRAINT_POINT_LONG,
+            KNEE_CONSTRAINT_POINT_SHORT)
 
     if self._accurate_motor_model_enabled or self._pd_control_enabled:
       # Disable the default motor in pybullet.
       self._pybullet_client.setJointMotorControl2(
           bodyIndex=self.quadruped,
-          jointIndex=(
-              self._joint_name_to_id["motor_" + leg_position + "L_joint"]),
+          jointIndex=(self._joint_name_to_id["motor_" + leg_position + "L_joint"]),
           controlMode=self._pybullet_client.VELOCITY_CONTROL,
           targetVelocity=0,
           force=knee_friction_force)
       self._pybullet_client.setJointMotorControl2(
           bodyIndex=self.quadruped,
-          jointIndex=(
-              self._joint_name_to_id["motor_" + leg_position + "R_joint"]),
+          jointIndex=(self._joint_name_to_id["motor_" + leg_position + "R_joint"]),
           controlMode=self._pybullet_client.VELOCITY_CONTROL,
           targetVelocity=0,
           force=knee_friction_force)
 
     else:
-      self._SetDesiredMotorAngleByName(
-          "motor_" + leg_position + "L_joint",
-          self._motor_direction[2 * leg_id] * half_pi)
-      self._SetDesiredMotorAngleByName(
-          "motor_" + leg_position + "R_joint",
-          self._motor_direction[2 * leg_id + 1] * half_pi)
+      self._SetDesiredMotorAngleByName("motor_" + leg_position + "L_joint",
+                                       self._motor_direction[2 * leg_id] * half_pi)
+      self._SetDesiredMotorAngleByName("motor_" + leg_position + "R_joint",
+                                       self._motor_direction[2 * leg_id + 1] * half_pi)
 
     self._pybullet_client.setJointMotorControl2(
         bodyIndex=self.quadruped,
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_extended_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_extended_env.py
new file mode 100644
index 000000000..1449441dd
--- /dev/null
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_extended_env.py
@@ -0,0 +1,256 @@
+"""Extends the environment by adding observation and action history.
+
+The implementation is a bit dirty import of the implementation in
+the experimental branch.
+
+"""
+
+from gym import spaces
+import numpy as np
+
+from pybullet_envs.minitaur.envs.minitaur_reactive_env import MinitaurReactiveEnv
+
+
+class MinitaurExtendedEnv(MinitaurReactiveEnv):
+  """The 'extended' environment for Markovian property.
+
+  This class implements to include prior actions and observations to the
+  observation vector, thus making the environment "more" Markovian. This is
+  especially useful for systems with latencies.
+
+  Args:
+    history_length: the length of the historic data
+    history_include_actions: a flag for including actions as history
+    history_include_states: a flag for including states as history
+    include_state_difference: a flag for including the first-order differences
+      as history
+    include_second_state_difference: a flag for including the second-order state
+      differences as history.
+    include_base_position: a flag for including the base as observation,
+    never_terminate: if this is on, the environment unconditionally never
+      terminates.
+    action_scale: the scale of actions,
+  """
+  MAX_BUFFER_SIZE = 1001
+  ACTION_DIM = 8
+  PARENT_OBSERVATION_DIM = 12
+  INIT_EXTENSION_POS = 2.0
+  INIT_SWING_POS = 0.0
+
+  metadata = {
+      "render.modes": ["human", "rgb_array"],
+      "video.frames_per_second": 50,
+  }
+
+  def __init__(self,
+               history_length=1,
+               history_include_actions=True,
+               history_include_states=False,
+               include_state_difference=False,
+               include_second_state_difference=False,
+               include_base_position=False,
+               include_leg_model=False,
+               never_terminate=False,
+               action_scale=0.5,
+               **kwargs):
+    self._kwargs = kwargs
+
+    self._history_length = history_length
+    self._history_include_actions = history_include_actions
+    self._history_include_states = history_include_states
+    self._include_state_difference = include_state_difference
+    self._include_second_state_difference = include_second_state_difference
+    self._include_base_position = include_base_position
+    self._include_leg_model = include_leg_model
+
+    self._never_terminate = never_terminate
+    self._action_scale = action_scale
+
+    self._past_parent_observations = np.zeros((self.MAX_BUFFER_SIZE + 1,
+                                               self.PARENT_OBSERVATION_DIM))
+    self._past_motor_angles = np.zeros((self.MAX_BUFFER_SIZE + 1, 8))
+    self._past_actions = np.zeros((self.MAX_BUFFER_SIZE, self.ACTION_DIM))
+    self._counter = 0
+
+    super(MinitaurExtendedEnv, self).__init__(**kwargs)
+    self.action_space = spaces.Box(-1.0, 1.0, self.action_space.shape)
+    self.observation_space = spaces.Box(-np.inf, np.inf,
+                                        self._get_observation().shape)
+    # This is mainly for the TF-Agents compatibility
+    self.action_space.flat_dim = len(self.action_space.low)
+    self.observation_space.flat_dim = len(self.observation_space.low)
+
+  def _get_observation(self):
+    """Maybe concatenate motor velocity and torque into observations."""
+    parent_observation = super(MinitaurExtendedEnv, self)._get_observation()
+    parent_observation = np.array(parent_observation)
+    # Base class might require this.
+    self._observation = parent_observation
+    self._past_parent_observations[self._counter] = parent_observation
+    num_motors = self.minitaur.num_motors
+    self._past_motor_angles[self._counter] = parent_observation[-num_motors:]
+
+    history_states = []
+    history_actions = []
+    for i in range(self._history_length):
+      t = max(self._counter - i - 1, 0)
+
+      if self._history_include_states:
+        history_states.append(self._past_parent_observations[t])
+
+      if self._history_include_actions:
+        history_actions.append(self._past_actions[t])
+
+    t = self._counter
+    tm, tmm = max(0, self._counter - 1), max(0, self._counter - 2)
+
+    state_difference, second_state_difference = [], []
+    if self._include_state_difference:
+      state_difference = [
+          self._past_motor_angles[t] - self._past_motor_angles[tm]
+      ]
+    if self._include_second_state_difference:
+      second_state_difference = [
+          self._past_motor_angles[t] - 2 * self._past_motor_angles[tm] +
+          self._past_motor_angles[tmm]
+      ]
+
+    base_position = []
+    if self._include_base_position:
+      base_position = np.array((self.minitaur.GetBasePosition()))
+
+    leg_model = []
+    if self._include_leg_model:
+      raw_motor_angles = self.minitaur.GetMotorAngles()
+      leg_model = self._convert_to_leg_model(raw_motor_angles)
+
+    observation_list = (
+        [parent_observation] + history_states + history_actions +
+        state_difference + second_state_difference + [base_position] +
+        [leg_model])
+
+    full_observation = np.concatenate(observation_list)
+    return full_observation
+
+  def reset(self):
+    """Resets the time and history buffer."""
+    self._counter = 0
+    self._signal(self._counter)  # This sets the current phase
+    self._past_parent_observations = np.zeros((self.MAX_BUFFER_SIZE + 1,
+                                               self.PARENT_OBSERVATION_DIM))
+    self._past_motor_angles = np.zeros((self.MAX_BUFFER_SIZE + 1, 8))
+    self._past_actions = np.zeros((self.MAX_BUFFER_SIZE, self.ACTION_DIM))
+    self._counter = 0
+
+    return np.array(super(MinitaurExtendedEnv, self).reset())
+
+  def step(self, action):
+    """Step function wrapper can be used to add shaping terms to the reward.
+
+    Args:
+      action: an array of the given action
+
+    Returns:
+      next_obs: the next observation
+      reward: the reward for this experience tuple
+      done: the terminal flag
+      info: an additional information
+    """
+
+    action *= self._action_scale
+
+    self._past_actions[self._counter] = action
+    self._counter += 1
+
+    next_obs, _, done, info = super(MinitaurExtendedEnv, self).step(action)
+
+    reward = self.reward()
+    info.update(base_reward=reward)
+
+    return next_obs, reward, done, info
+
+  def terminate(self):
+    """The helper function to terminate the environment."""
+    super(MinitaurExtendedEnv, self)._close()
+
+  def _termination(self):
+    """Determines whether the env is terminated or not.
+
+    checks whether 1) the front leg is bent too much or 2) the time exceeds
+    the manually set weights.
+
+    Returns:
+      terminal: the terminal flag whether the env is terminated or not
+    """
+    if self._never_terminate:
+      return False
+
+    leg_model = self._convert_to_leg_model(self.minitaur.GetMotorAngles())
+    swing0 = leg_model[0]
+    swing1 = leg_model[2]
+    maximum_swing_angle = 0.8
+    if swing0 > maximum_swing_angle or swing1 > maximum_swing_angle:
+      return True
+
+    if self._counter >= 500:
+      return True
+
+    return False
+
+  def reward(self):
+    """Compute rewards for the given time step.
+
+    It considers two terms: 1) forward velocity reward and 2) action
+    acceleration penalty.
+
+    Returns:
+      reward: the computed reward.
+    """
+    current_base_position = self.minitaur.GetBasePosition()
+    dt = self.control_time_step
+    velocity = (current_base_position[0] - self._last_base_position[0]) / dt
+    velocity_reward = np.clip(velocity, -0.5, 0.5)
+
+    action = self._past_actions[self._counter - 1]
+    prev_action = self._past_actions[max(self._counter - 2, 0)]
+    prev_prev_action = self._past_actions[max(self._counter - 3, 0)]
+    acc = action - 2 * prev_action + prev_prev_action
+    action_acceleration_penalty = np.mean(np.abs(acc))
+
+    reward = 0.0
+    reward += 1.0 * velocity_reward
+    reward -= 0.1 * action_acceleration_penalty
+
+    return reward
+
+  @staticmethod
+  def convert_to_leg_model(motor_angles):
+    """A helper function to convert motor angles to leg model.
+
+    Args:
+      motor_angles: raw motor angles:
+
+    Returns:
+      leg_angles: the leg pose model represented in swing and extension.
+    """
+    # TODO(sehoonha): clean up model conversion codes
+    num_legs = 4
+    # motor_angles = motor_angles / (np.pi / 4.)
+    leg_angles = np.zeros(num_legs * 2)
+    for i in range(num_legs):
+      motor1, motor2 = motor_angles[2 * i:2 * i + 2]
+      swing = (-1)**(i // 2) * 0.5 * (motor2 - motor1)
+      extension = 0.5 * (motor1 + motor2)
+
+      leg_angles[i] = swing
+      leg_angles[i + num_legs] = extension
+
+    return leg_angles
+
+  def __getstate__(self):
+    """A helper get state function for pickling."""
+    return {"kwargs": self._kwargs}
+
+  def __setstate__(self, state):
+    """A helper set state function for pickling."""
+    self.__init__(**state["kwargs"])
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env.py
index 0774f1710..1008f8062 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env.py
@@ -32,10 +32,7 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv):
   action to zero and the height of the robot base. It prefers a similar pose to
   the signal while keeping balance.
   """
-  metadata = {
-      "render.modes": ["human", "rgb_array"],
-      "video.frames_per_second": 166
-  }
+  metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 166}
 
   def __init__(self,
                urdf_version=None,
@@ -101,23 +98,23 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv):
     self._extension_offset = np.zeros(NUM_LEGS)
     self._use_angular_velocity_in_observation = use_motor_angle_in_observation
     self._use_motor_angle_in_observation = use_motor_angle_in_observation
-    super(MinitaurFourLegStandEnv, self).__init__(
-        urdf_version=urdf_version,
-        control_time_step=control_time_step,
-        action_repeat=action_repeat,
-        remove_default_joint_damping=remove_default_joint_damping,
-        accurate_motor_model_enabled=True,
-        motor_overheat_protection=True,
-        hard_reset=hard_reset,
-        motor_kp=motor_kp,
-        motor_kd=motor_kd,
-        control_latency=control_latency,
-        pd_latency=pd_latency,
-        on_rack=on_rack,
-        render=render,
-        env_randomizer=env_randomizer,
-        reflection = False,
-        log_path=log_path)
+    super(MinitaurFourLegStandEnv,
+          self).__init__(urdf_version=urdf_version,
+                         control_time_step=control_time_step,
+                         action_repeat=action_repeat,
+                         remove_default_joint_damping=remove_default_joint_damping,
+                         accurate_motor_model_enabled=True,
+                         motor_overheat_protection=True,
+                         hard_reset=hard_reset,
+                         motor_kp=motor_kp,
+                         motor_kd=motor_kd,
+                         control_latency=control_latency,
+                         pd_latency=pd_latency,
+                         on_rack=on_rack,
+                         render=render,
+                         env_randomizer=env_randomizer,
+                         reflection=False,
+                         log_path=log_path)
 
     action_dim = 4
     action_low = np.array([-1.0] * action_dim)
@@ -138,20 +135,17 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv):
     # [swing leg 1, swing leg 2, swing leg 3, swing leg 4,
     #  extension leg 1, extension leg 2, extension leg 3, extension leg 4]
     init_pose = [
-        INIT_SWING_POS + self._swing_offset[0],
-        INIT_SWING_POS + self._swing_offset[1],
-        INIT_SWING_POS + self._swing_offset[2],
-        INIT_SWING_POS + self._swing_offset[3],
+        INIT_SWING_POS + self._swing_offset[0], INIT_SWING_POS + self._swing_offset[1],
+        INIT_SWING_POS + self._swing_offset[2], INIT_SWING_POS + self._swing_offset[3],
         INIT_EXTENSION_POS + self._extension_offset[0],
         INIT_EXTENSION_POS + self._extension_offset[1],
         INIT_EXTENSION_POS + self._extension_offset[2],
         INIT_EXTENSION_POS + self._extension_offset[3]
     ]
     initial_motor_angles = self._convert_from_leg_model(init_pose)
-    self._pybullet_client.resetBasePositionAndOrientation(
-        0, [0, 0, 0], [0, 0, 0, 1])
-    super(MinitaurFourLegStandEnv, self).reset(
-        initial_motor_angles=initial_motor_angles, reset_duration=0.5)
+    self._pybullet_client.resetBasePositionAndOrientation(0, [0, 0, 0], [0, 0, 0, 1])
+    super(MinitaurFourLegStandEnv, self).reset(initial_motor_angles=initial_motor_angles,
+                                               reset_duration=0.5)
     return self._get_observation()
 
   def step(self, action):
@@ -180,10 +174,8 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv):
         time.sleep(time_to_sleep)
       base_pos = self.minitaur.GetBasePosition()
       # Keep the previous orientation of the camera set by the user.
-      [yaw, pitch,
-       dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11]
-      self._pybullet_client.resetDebugVisualizerCamera(dist, yaw, pitch,
-                                                       base_pos)
+      [yaw, pitch, dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11]
+      self._pybullet_client.resetDebugVisualizerCamera(dist, yaw, pitch, base_pos)
     action = self._transform_action_to_motor_command(action)
     t = self._env_step_counter % MOVING_FLOOR_TOTAL_STEP
     if t == 0:
@@ -197,8 +189,8 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv):
     t = float(float(t) / float(MOVING_FLOOR_TOTAL_STEP))
     ori = map(operator.add, [x * (1.0 - t) for x in self._cur_ori],
               [x * t for x in self._goal_ori])
-    ori=list(ori)
-    print("ori=",ori)
+    ori = list(ori)
+    print("ori=", ori)
     self._pybullet_client.resetBasePositionAndOrientation(0, [0, 0, 0], ori)
     if self._env_step_counter % PERTURBATION_TOTAL_STEP == 0:
       self._perturbation_magnitude = random.uniform(0.0, 0.0)
@@ -218,8 +210,8 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv):
     obs = self._get_true_observation()
     reward = self._reward()
     if self._log_path is not None:
-      minitaur_logging.update_episode_proto(self._episode_proto, self.minitaur,
-                                            action, self._env_step_counter)
+      minitaur_logging.update_episode_proto(self._episode_proto, self.minitaur, action,
+                                            self._env_step_counter)
     if done:
       self.minitaur.Terminate()
     return np.array(self._get_observation()), reward, done, {}
@@ -228,15 +220,13 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv):
     motor_pose = np.zeros(NUM_MOTORS)
     for i in range(NUM_LEGS):
       motor_pose[2 * i] = leg_pose[NUM_LEGS + i] - (-1)**(i / 2) * leg_pose[i]
-      motor_pose[2 * i
-                 + 1] = leg_pose[NUM_LEGS + i] + (-1)**(i / 2) * leg_pose[i]
+      motor_pose[2 * i + 1] = leg_pose[NUM_LEGS + i] + (-1)**(i / 2) * leg_pose[i]
     return motor_pose
 
   def _signal(self, t):
     initial_pose = np.array([
-        INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS,
-        INIT_EXTENSION_POS, INIT_EXTENSION_POS, INIT_EXTENSION_POS,
-        INIT_EXTENSION_POS
+        INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_EXTENSION_POS,
+        INIT_EXTENSION_POS, INIT_EXTENSION_POS, INIT_EXTENSION_POS
     ])
     signal = initial_pose
     return signal
@@ -268,8 +258,7 @@ class MinitaurFourLegStandEnv(minitaur_gym_env.MinitaurGymEnv):
     rot_mat = self._pybullet_client.getMatrixFromQuaternion(orientation)
     local_up = rot_mat[6:]
     _, _, height = self.minitaur.GetBasePosition()
-    local_global_up_dot_product = np.dot(
-        np.asarray([0, 0, 1]), np.asarray(local_up))
+    local_global_up_dot_product = np.dot(np.asarray([0, 0, 1]), np.asarray(local_up))
     return local_global_up_dot_product < 0.85 or height < 0.15
 
   def _reward(self):
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env_example.py
index c154e879a..183213fce 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env_example.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env_example.py
@@ -5,7 +5,6 @@ import numpy as np
 import tensorflow as tf
 from pybullet_envs.minitaur.envs import minitaur_four_leg_stand_env
 
-
 FLAGS = tf.flags.FLAGS
 tf.flags.DEFINE_string("log_path", None, "The directory to write the log file.")
 NUM_LEGS = 4
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env.py
index 7553c33fa..44297d11b 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env.py
@@ -4,10 +4,10 @@
 import math
 import time
 
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import gym
 from gym import spaces
@@ -64,10 +64,7 @@ class MinitaurGymEnv(gym.Env):
   expenditure.
 
   """
-  metadata = {
-      "render.modes": ["human", "rgb_array"],
-      "video.frames_per_second": 100
-  }
+  metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 100}
 
   def __init__(self,
                urdf_root=pybullet_data.getDataPath(),
@@ -184,17 +181,14 @@ class MinitaurGymEnv(gym.Env):
         self._action_repeat = 1
       self.control_time_step = self._time_step * self._action_repeat
     # TODO(b/73829334): Fix the value of self._num_bullet_solver_iterations.
-    self._num_bullet_solver_iterations = int(
-        NUM_SIMULATION_ITERATION_STEPS / self._action_repeat)
+    self._num_bullet_solver_iterations = int(NUM_SIMULATION_ITERATION_STEPS / self._action_repeat)
     self._urdf_root = urdf_root
     self._self_collision_enabled = self_collision_enabled
     self._motor_velocity_limit = motor_velocity_limit
     self._observation = []
     self._true_observation = []
     self._objectives = []
-    self._objective_weights = [
-        distance_weight, energy_weight, drift_weight, shake_weight
-    ]
+    self._objective_weights = [distance_weight, energy_weight, drift_weight, shake_weight]
     self._env_step_counter = 0
     self._num_steps_to_log = num_steps_to_log
     self._is_render = render
@@ -226,12 +220,10 @@ class MinitaurGymEnv(gym.Env):
     self._urdf_version = urdf_version
     self._ground_id = None
     self._reflection = reflection
-    self._env_randomizers = convert_to_list(
-        env_randomizer) if env_randomizer else []
+    self._env_randomizers = convert_to_list(env_randomizer) if env_randomizer else []
     self._episode_proto = minitaur_logging_pb2.MinitaurEpisode()
     if self._is_render:
-      self._pybullet_client = bullet_client.BulletClient(
-          connection_mode=pybullet.GUI)
+      self._pybullet_client = bullet_client.BulletClient(connection_mode=pybullet.GUI)
     else:
       self._pybullet_client = bullet_client.BulletClient()
     if self._urdf_version is None:
@@ -249,7 +241,7 @@ class MinitaurGymEnv(gym.Env):
     self._hard_reset = hard_reset  # This assignment need to be after reset()
 
   def close(self):
-    if self._env_step_counter>0:
+    if self._env_step_counter > 0:
       self.logging.save_episode(self._episode_proto)
     self.minitaur.Terminate()
 
@@ -257,53 +249,48 @@ class MinitaurGymEnv(gym.Env):
     self._env_randomizers.append(env_randomizer)
 
   def reset(self, initial_motor_angles=None, reset_duration=1.0):
-    self._pybullet_client.configureDebugVisualizer(
-        self._pybullet_client.COV_ENABLE_RENDERING, 0)
-    if self._env_step_counter>0:
+    self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_RENDERING, 0)
+    if self._env_step_counter > 0:
       self.logging.save_episode(self._episode_proto)
     self._episode_proto = minitaur_logging_pb2.MinitaurEpisode()
-    minitaur_logging.preallocate_episode_proto(self._episode_proto,
-                                               self._num_steps_to_log)
+    minitaur_logging.preallocate_episode_proto(self._episode_proto, self._num_steps_to_log)
     if self._hard_reset:
       self._pybullet_client.resetSimulation()
       self._pybullet_client.setPhysicsEngineParameter(
           numSolverIterations=int(self._num_bullet_solver_iterations))
       self._pybullet_client.setTimeStep(self._time_step)
-      self._ground_id = self._pybullet_client.loadURDF(
-          "%s/plane.urdf" % self._urdf_root)
+      self._ground_id = self._pybullet_client.loadURDF("%s/plane.urdf" % self._urdf_root)
       if (self._reflection):
-        self._pybullet_client.changeVisualShape(self._ground_id,-1,rgbaColor=[1,1,1,0.8])
-        self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_PLANAR_REFLECTION,self._ground_id)
+        self._pybullet_client.changeVisualShape(self._ground_id, -1, rgbaColor=[1, 1, 1, 0.8])
+        self._pybullet_client.configureDebugVisualizer(
+            self._pybullet_client.COV_ENABLE_PLANAR_REFLECTION, self._ground_id)
       self._pybullet_client.setGravity(0, 0, -10)
       acc_motor = self._accurate_motor_model_enabled
       motor_protect = self._motor_overheat_protection
       if self._urdf_version not in MINIATUR_URDF_VERSION_MAP:
-        raise ValueError(
-            "%s is not a supported urdf_version." % self._urdf_version)
+        raise ValueError("%s is not a supported urdf_version." % self._urdf_version)
       else:
-        self.minitaur = (
-            MINIATUR_URDF_VERSION_MAP[self._urdf_version](
-                pybullet_client=self._pybullet_client,
-                action_repeat=self._action_repeat,
-                urdf_root=self._urdf_root,
-                time_step=self._time_step,
-                self_collision_enabled=self._self_collision_enabled,
-                motor_velocity_limit=self._motor_velocity_limit,
-                pd_control_enabled=self._pd_control_enabled,
-                accurate_motor_model_enabled=acc_motor,
-                remove_default_joint_damping=self._remove_default_joint_damping,
-                motor_kp=self._motor_kp,
-                motor_kd=self._motor_kd,
-                control_latency=self._control_latency,
-                pd_latency=self._pd_latency,
-                observation_noise_stdev=self._observation_noise_stdev,
-                torque_control_enabled=self._torque_control_enabled,
-                motor_overheat_protection=motor_protect,
-                on_rack=self._on_rack))
-    self.minitaur.Reset(
-        reload_urdf=False,
-        default_motor_angles=initial_motor_angles,
-        reset_time=reset_duration)
+        self.minitaur = (MINIATUR_URDF_VERSION_MAP[self._urdf_version](
+            pybullet_client=self._pybullet_client,
+            action_repeat=self._action_repeat,
+            urdf_root=self._urdf_root,
+            time_step=self._time_step,
+            self_collision_enabled=self._self_collision_enabled,
+            motor_velocity_limit=self._motor_velocity_limit,
+            pd_control_enabled=self._pd_control_enabled,
+            accurate_motor_model_enabled=acc_motor,
+            remove_default_joint_damping=self._remove_default_joint_damping,
+            motor_kp=self._motor_kp,
+            motor_kd=self._motor_kd,
+            control_latency=self._control_latency,
+            pd_latency=self._pd_latency,
+            observation_noise_stdev=self._observation_noise_stdev,
+            torque_control_enabled=self._torque_control_enabled,
+            motor_overheat_protection=motor_protect,
+            on_rack=self._on_rack))
+    self.minitaur.Reset(reload_urdf=False,
+                        default_motor_angles=initial_motor_angles,
+                        reset_time=reset_duration)
 
     # Loop over all env randomizers.
     for env_randomizer in self._env_randomizers:
@@ -313,10 +300,9 @@ class MinitaurGymEnv(gym.Env):
     self._env_step_counter = 0
     self._last_base_position = [0, 0, 0]
     self._objectives = []
-    self._pybullet_client.resetDebugVisualizerCamera(
-        self._cam_dist, self._cam_yaw, self._cam_pitch, [0, 0, 0])
-    self._pybullet_client.configureDebugVisualizer(
-        self._pybullet_client.COV_ENABLE_RENDERING, 1)
+    self._pybullet_client.resetDebugVisualizerCamera(self._cam_dist, self._cam_yaw,
+                                                     self._cam_pitch, [0, 0, 0])
+    self._pybullet_client.configureDebugVisualizer(self._pybullet_client.COV_ENABLE_RENDERING, 1)
     return self._get_observation()
 
   def seed(self, seed=None):
@@ -328,8 +314,7 @@ class MinitaurGymEnv(gym.Env):
       for i, action_component in enumerate(action):
         if not (-self._action_bound - ACTION_EPS <= action_component <=
                 self._action_bound + ACTION_EPS):
-          raise ValueError("{}th action {} out of bounds.".format(
-              i, action_component))
+          raise ValueError("{}th action {} out of bounds.".format(i, action_component))
       action = self.minitaur.ConvertFromLegModel(action)
     return action
 
@@ -361,10 +346,8 @@ class MinitaurGymEnv(gym.Env):
         time.sleep(time_to_sleep)
       base_pos = self.minitaur.GetBasePosition()
       # Keep the previous orientation of the camera set by the user.
-      [yaw, pitch,
-       dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11]
-      self._pybullet_client.resetDebugVisualizerCamera(dist, yaw, pitch,
-                                                       base_pos)
+      [yaw, pitch, dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11]
+      self._pybullet_client.resetDebugVisualizerCamera(dist, yaw, pitch, base_pos)
 
     for env_randomizer in self._env_randomizers:
       env_randomizer.randomize_step(self)
@@ -374,8 +357,8 @@ class MinitaurGymEnv(gym.Env):
     reward = self._reward()
     done = self._termination()
     if self._log_path is not None:
-      minitaur_logging.update_episode_proto(self._episode_proto, self.minitaur,
-                                            action, self._env_step_counter)
+      minitaur_logging.update_episode_proto(self._episode_proto, self.minitaur, action,
+                                            self._env_step_counter)
     self._env_step_counter += 1
     if done:
       self.minitaur.Terminate()
@@ -392,11 +375,11 @@ class MinitaurGymEnv(gym.Env):
         pitch=self._cam_pitch,
         roll=0,
         upAxisIndex=2)
-    proj_matrix = self._pybullet_client.computeProjectionMatrixFOV(
-        fov=60,
-        aspect=float(RENDER_WIDTH) / RENDER_HEIGHT,
-        nearVal=0.1,
-        farVal=100.0)
+    proj_matrix = self._pybullet_client.computeProjectionMatrixFOV(fov=60,
+                                                                   aspect=float(RENDER_WIDTH) /
+                                                                   RENDER_HEIGHT,
+                                                                   nearVal=0.1,
+                                                                   farVal=100.0)
     (_, _, px, _, _) = self._pybullet_client.getCameraImage(
         width=RENDER_WIDTH,
         height=RENDER_HEIGHT,
@@ -413,9 +396,8 @@ class MinitaurGymEnv(gym.Env):
     Returns:
       A numpy array of motor angles.
     """
-    return np.array(
-        self._observation[MOTOR_ANGLE_OBSERVATION_INDEX:
-                          MOTOR_ANGLE_OBSERVATION_INDEX + NUM_MOTORS])
+    return np.array(self._observation[MOTOR_ANGLE_OBSERVATION_INDEX:MOTOR_ANGLE_OBSERVATION_INDEX +
+                                      NUM_MOTORS])
 
   def get_minitaur_motor_velocities(self):
     """Get the minitaur's motor velocities.
@@ -424,8 +406,8 @@ class MinitaurGymEnv(gym.Env):
       A numpy array of motor velocities.
     """
     return np.array(
-        self._observation[MOTOR_VELOCITY_OBSERVATION_INDEX:
-                          MOTOR_VELOCITY_OBSERVATION_INDEX + NUM_MOTORS])
+        self._observation[MOTOR_VELOCITY_OBSERVATION_INDEX:MOTOR_VELOCITY_OBSERVATION_INDEX +
+                          NUM_MOTORS])
 
   def get_minitaur_motor_torques(self):
     """Get the minitaur's motor torques.
@@ -434,8 +416,8 @@ class MinitaurGymEnv(gym.Env):
       A numpy array of motor torques.
     """
     return np.array(
-        self._observation[MOTOR_TORQUE_OBSERVATION_INDEX:
-                          MOTOR_TORQUE_OBSERVATION_INDEX + NUM_MOTORS])
+        self._observation[MOTOR_TORQUE_OBSERVATION_INDEX:MOTOR_TORQUE_OBSERVATION_INDEX +
+                          NUM_MOTORS])
 
   def get_minitaur_base_orientation(self):
     """Get the minitaur's base orientation, represented by a quaternion.
@@ -459,8 +441,7 @@ class MinitaurGymEnv(gym.Env):
     rot_mat = self._pybullet_client.getMatrixFromQuaternion(orientation)
     local_up = rot_mat[6:]
     pos = self.minitaur.GetBasePosition()
-    return (np.dot(np.asarray([0, 0, 1]), np.asarray(local_up)) < 0.85 or
-            pos[2] < 0.13)
+    return (np.dot(np.asarray([0, 0, 1]), np.asarray(local_up)) < 0.85 or pos[2] < 0.13)
 
   def _termination(self):
     position = self.minitaur.GetBasePosition()
@@ -483,9 +464,7 @@ class MinitaurGymEnv(gym.Env):
         np.dot(self.minitaur.GetMotorTorques(),
                self.minitaur.GetMotorVelocities())) * self._time_step
     objectives = [forward_reward, energy_reward, drift_reward, shake_reward]
-    weighted_objectives = [
-        o * w for o, w in zip(objectives, self._objective_weights)
-    ]
+    weighted_objectives = [o * w for o, w in zip(objectives, self._objective_weights)]
     reward = sum(weighted_objectives)
     self._objectives.append(objectives)
     return reward
@@ -552,10 +531,8 @@ class MinitaurGymEnv(gym.Env):
     upper_bound = np.zeros(self._get_observation_dimension())
     num_motors = self.minitaur.num_motors
     upper_bound[0:num_motors] = math.pi  # Joint angle.
-    upper_bound[num_motors:2 * num_motors] = (
-        motor.MOTOR_SPEED_LIMIT)  # Joint velocity.
-    upper_bound[2 * num_motors:3 * num_motors] = (
-        motor.OBSERVED_TORQUE_LIMIT)  # Joint torque.
+    upper_bound[num_motors:2 * num_motors] = (motor.MOTOR_SPEED_LIMIT)  # Joint velocity.
+    upper_bound[2 * num_motors:3 * num_motors] = (motor.OBSERVED_TORQUE_LIMIT)  # Joint torque.
     upper_bound[3 * num_motors:] = 1.0  # Quaternion of base orientation.
     return upper_bound
 
@@ -577,7 +554,6 @@ class MinitaurGymEnv(gym.Env):
     _seed = seed
     _step = step
 
-
   def set_time_step(self, control_step, simulation_step=0.001):
     """Sets the time step of the environment.
 
@@ -591,18 +567,15 @@ class MinitaurGymEnv(gym.Env):
       ValueError: If the control step is smaller than the simulation step.
     """
     if control_step < simulation_step:
-      raise ValueError(
-          "Control step should be larger than or equal to simulation step.")
+      raise ValueError("Control step should be larger than or equal to simulation step.")
     self.control_time_step = control_step
     self._time_step = simulation_step
     self._action_repeat = int(round(control_step / simulation_step))
-    self._num_bullet_solver_iterations = (
-        NUM_SIMULATION_ITERATION_STEPS / self._action_repeat)
+    self._num_bullet_solver_iterations = (NUM_SIMULATION_ITERATION_STEPS / self._action_repeat)
     self._pybullet_client.setPhysicsEngineParameter(
         numSolverIterations=self._num_bullet_solver_iterations)
     self._pybullet_client.setTimeStep(self._time_step)
-    self.minitaur.SetTimeSteps(
-        action_repeat=self._action_repeat, simulation_step=self._time_step)
+    self.minitaur.SetTimeSteps(action_repeat=self._action_repeat, simulation_step=self._time_step)
 
   @property
   def pybullet_client(self):
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env_example.py
index c05022648..8441879ad 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env_example.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env_example.py
@@ -8,8 +8,8 @@ import os
 import inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(os.path.dirname(currentdir)))
-print("parentdir=",parentdir)
-os.sys.path.insert(0,parentdir)
+print("parentdir=", parentdir)
+os.sys.path.insert(0, parentdir)
 
 import argparse
 import numpy as np
@@ -17,7 +17,6 @@ import tensorflow as tf
 from pybullet_envs.minitaur.envs import minitaur_gym_env
 import time
 
-
 #FLAGS = flags.FLAGS
 #flags.DEFINE_enum(
 #    "example_name", "sine", ["sine", "reset", "stand", "overheat"],
@@ -60,7 +59,7 @@ def ResetPoseExample(log_path=None):
   action = [math.pi / 2] * 8
   for _ in range(steps):
     _, _, done, _ = environment.step(action)
-    time.sleep(1./100.)
+    time.sleep(1. / 100.)
     if done:
       break
 
@@ -104,7 +103,7 @@ def MotorOverheatExample(log_path=None):
     observation, _, _, _ = environment.step(action)
     current_row.extend(observation.tolist())
     actions_and_observations.append(current_row)
-    time.sleep(1./100.)
+    time.sleep(1. / 100.)
 
   if FLAGS.output_filename is not None:
     WriteToCSV(FLAGS.output_filename, actions_and_observations)
@@ -151,7 +150,7 @@ def SineStandExample(log_path=None):
     observation, _, _, _ = environment.step(action)
     current_row.extend(observation.tolist())
     actions_and_observations.append(current_row)
-    time.sleep(1./100.)
+    time.sleep(1. / 100.)
 
   if FLAGS.output_filename is not None:
     WriteToCSV(FLAGS.output_filename, actions_and_observations)
@@ -199,7 +198,7 @@ def SinePolicyExample(log_path=None):
     a4 = math.sin(t * speed + math.pi) * amplitude2
     action = [a1, a2, a2, a1, a3, a4, a4, a3]
     _, reward, done, _ = environment.step(action)
-    time.sleep(1./100.)
+    time.sleep(1. / 100.)
 
     sum_reward += reward
     if done:
@@ -207,14 +206,15 @@ def SinePolicyExample(log_path=None):
       environment.reset()
 
 
-
-
 def main():
   parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-  parser.add_argument('--env', help='environment ID (0==sine, 1==stand, 2=reset, 3=overheat)',type=int,  default=0)
+  parser.add_argument('--env',
+                      help='environment ID (0==sine, 1==stand, 2=reset, 3=overheat)',
+                      type=int,
+                      default=0)
   args = parser.parse_args()
   print("--env=" + str(args.env))
-    
+
   if (args.env == 0):
     SinePolicyExample()
   if (args.env == 1):
@@ -224,6 +224,6 @@ def main():
   if (args.env == 3):
     MotorOverheatExample()
 
-if __name__ == '__main__':
-    main()
 
+if __name__ == '__main__':
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging.py
index c40594042..e8467a5ae 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging.py
@@ -10,10 +10,10 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import datetime
 import os
@@ -74,9 +74,8 @@ def update_episode_proto(episode_proto, minitaur, action, step):
   """
   max_num_steps = len(episode_proto.state_action)
   if step >= max_num_steps:
-    tf.logging.warning(
-        "{}th step is not recorded in the logging since only {} steps were "
-        "pre-allocated.".format(step, max_num_steps))
+    tf.logging.warning("{}th step is not recorded in the logging since only {} steps were "
+                       "pre-allocated.".format(step, max_num_steps))
     return
   step_log = episode_proto.state_action[step]
   step_log.info_valid = minitaur.IsObservationValid()
@@ -95,8 +94,7 @@ def update_episode_proto(episode_proto, minitaur, action, step):
 
   _update_base_state(step_log.base_position, minitaur.GetBasePosition())
   _update_base_state(step_log.base_orientation, minitaur.GetBaseRollPitchYaw())
-  _update_base_state(step_log.base_angular_vel,
-                     minitaur.GetBaseRollPitchYawRate())
+  _update_base_state(step_log.base_angular_vel, minitaur.GetBaseRollPitchYawRate())
 
 
 class MinitaurLogging(object):
@@ -124,10 +122,8 @@ class MinitaurLogging(object):
     if not tf.gfile.Exists(self._log_path):
       tf.gfile.MakeDirs(self._log_path)
     ts = time.time()
-    time_stamp = datetime.datetime.fromtimestamp(ts).strftime(
-        "%Y-%m-%d-%H%M%S")
-    log_path = os.path.join(self._log_path,
-                            "minitaur_log_{}".format(time_stamp))
+    time_stamp = datetime.datetime.fromtimestamp(ts).strftime("%Y-%m-%d-%H%M%S")
+    log_path = os.path.join(self._log_path, "minitaur_log_{}".format(time_stamp))
     with tf.gfile.Open(log_path, "w") as f:
       f.write(episode_proto.SerializeToString())
     return log_path
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging_pb2.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging_pb2.py
index ff564386a..9650c4b24 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging_pb2.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_logging_pb2.py
@@ -3,12 +3,12 @@
 
 import sys
 
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
-_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode('latin1'))
 from google.protobuf import descriptor as _descriptor
 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
@@ -18,168 +18,271 @@ from google.protobuf import descriptor_pb2
 
 _sym_db = _symbol_database.Default()
 
-
 from pybullet_envs.minitaur.envs import timestamp_pb2 as timestamp__pb2
 from pybullet_envs.minitaur.envs import vector_pb2 as vector__pb2
 
-
 DESCRIPTOR = _descriptor.FileDescriptor(
-  name='minitaur_logging.proto',
-  package='robotics.reinforcement_learning.minitaur.envs',
-  syntax='proto3',
-  serialized_pb=_b('\n\x16minitaur_logging.proto\x12-robotics.reinforcement_learning.minitaur.envs\x1a\x0ftimestamp.proto\x1a\x0cvector.proto\"k\n\x0fMinitaurEpisode\x12X\n\x0cstate_action\x18\x01 \x03(\x0b\x32\x42.robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction\"U\n\x12MinitaurMotorState\x12\r\n\x05\x61ngle\x18\x01 \x01(\x01\x12\x10\n\x08velocity\x18\x02 \x01(\x01\x12\x0e\n\x06torque\x18\x03 \x01(\x01\x12\x0e\n\x06\x61\x63tion\x18\x04 \x01(\x01\"\xce\x02\n\x13MinitaurStateAction\x12\x12\n\ninfo_valid\x18\x06 \x01(\x08\x12(\n\x04time\x18\x01 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12\x32\n\rbase_position\x18\x02 \x01(\x0b\x32\x1b.robotics.messages.Vector3d\x12\x35\n\x10\x62\x61se_orientation\x18\x03 \x01(\x0b\x32\x1b.robotics.messages.Vector3d\x12\x35\n\x10\x62\x61se_angular_vel\x18\x04 \x01(\x0b\x32\x1b.robotics.messages.Vector3d\x12W\n\x0cmotor_states\x18\x05 \x03(\x0b\x32\x41.robotics.reinforcement_learning.minitaur.envs.MinitaurMotorStateb\x06proto3')
-  ,
-  dependencies=[timestamp__pb2.DESCRIPTOR,vector__pb2.DESCRIPTOR,])
-
-
-
+    name='minitaur_logging.proto',
+    package='robotics.reinforcement_learning.minitaur.envs',
+    syntax='proto3',
+    serialized_pb=_b(
+        '\n\x16minitaur_logging.proto\x12-robotics.reinforcement_learning.minitaur.envs\x1a\x0ftimestamp.proto\x1a\x0cvector.proto\"k\n\x0fMinitaurEpisode\x12X\n\x0cstate_action\x18\x01 \x03(\x0b\x32\x42.robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction\"U\n\x12MinitaurMotorState\x12\r\n\x05\x61ngle\x18\x01 \x01(\x01\x12\x10\n\x08velocity\x18\x02 \x01(\x01\x12\x0e\n\x06torque\x18\x03 \x01(\x01\x12\x0e\n\x06\x61\x63tion\x18\x04 \x01(\x01\"\xce\x02\n\x13MinitaurStateAction\x12\x12\n\ninfo_valid\x18\x06 \x01(\x08\x12(\n\x04time\x18\x01 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x12\x32\n\rbase_position\x18\x02 \x01(\x0b\x32\x1b.robotics.messages.Vector3d\x12\x35\n\x10\x62\x61se_orientation\x18\x03 \x01(\x0b\x32\x1b.robotics.messages.Vector3d\x12\x35\n\x10\x62\x61se_angular_vel\x18\x04 \x01(\x0b\x32\x1b.robotics.messages.Vector3d\x12W\n\x0cmotor_states\x18\x05 \x03(\x0b\x32\x41.robotics.reinforcement_learning.minitaur.envs.MinitaurMotorStateb\x06proto3'
+    ),
+    dependencies=[
+        timestamp__pb2.DESCRIPTOR,
+        vector__pb2.DESCRIPTOR,
+    ])
 
 _MINITAUREPISODE = _descriptor.Descriptor(
-  name='MinitaurEpisode',
-  full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurEpisode',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='state_action', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurEpisode.state_action', index=0,
-      number=1, type=11, cpp_type=10, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=104,
-  serialized_end=211,
+    name='MinitaurEpisode',
+    full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurEpisode',
+    filename=None,
+    file=DESCRIPTOR,
+    containing_type=None,
+    fields=[
+        _descriptor.FieldDescriptor(
+            name='state_action',
+            full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurEpisode.state_action',
+            index=0,
+            number=1,
+            type=11,
+            cpp_type=10,
+            label=3,
+            has_default_value=False,
+            default_value=[],
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            options=None,
+            file=DESCRIPTOR),
+    ],
+    extensions=[],
+    nested_types=[],
+    enum_types=[],
+    options=None,
+    is_extendable=False,
+    syntax='proto3',
+    extension_ranges=[],
+    oneofs=[],
+    serialized_start=104,
+    serialized_end=211,
 )
 
-
 _MINITAURMOTORSTATE = _descriptor.Descriptor(
-  name='MinitaurMotorState',
-  full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='angle', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.angle', index=0,
-      number=1, type=1, cpp_type=5, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='velocity', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.velocity', index=1,
-      number=2, type=1, cpp_type=5, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='torque', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.torque', index=2,
-      number=3, type=1, cpp_type=5, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='action', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.action', index=3,
-      number=4, type=1, cpp_type=5, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=213,
-  serialized_end=298,
+    name='MinitaurMotorState',
+    full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState',
+    filename=None,
+    file=DESCRIPTOR,
+    containing_type=None,
+    fields=[
+        _descriptor.FieldDescriptor(
+            name='angle',
+            full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.angle',
+            index=0,
+            number=1,
+            type=1,
+            cpp_type=5,
+            label=1,
+            has_default_value=False,
+            default_value=float(0),
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            options=None,
+            file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(
+            name='velocity',
+            full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.velocity',
+            index=1,
+            number=2,
+            type=1,
+            cpp_type=5,
+            label=1,
+            has_default_value=False,
+            default_value=float(0),
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            options=None,
+            file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(
+            name='torque',
+            full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.torque',
+            index=2,
+            number=3,
+            type=1,
+            cpp_type=5,
+            label=1,
+            has_default_value=False,
+            default_value=float(0),
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            options=None,
+            file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(
+            name='action',
+            full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState.action',
+            index=3,
+            number=4,
+            type=1,
+            cpp_type=5,
+            label=1,
+            has_default_value=False,
+            default_value=float(0),
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            options=None,
+            file=DESCRIPTOR),
+    ],
+    extensions=[],
+    nested_types=[],
+    enum_types=[],
+    options=None,
+    is_extendable=False,
+    syntax='proto3',
+    extension_ranges=[],
+    oneofs=[],
+    serialized_start=213,
+    serialized_end=298,
 )
 
-
 _MINITAURSTATEACTION = _descriptor.Descriptor(
-  name='MinitaurStateAction',
-  full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='info_valid', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.info_valid', index=0,
-      number=6, type=8, cpp_type=7, label=1,
-      has_default_value=False, default_value=False,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='time', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.time', index=1,
-      number=1, type=11, cpp_type=10, label=1,
-      has_default_value=False, default_value=None,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='base_position', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.base_position', index=2,
-      number=2, type=11, cpp_type=10, label=1,
-      has_default_value=False, default_value=None,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='base_orientation', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.base_orientation', index=3,
-      number=3, type=11, cpp_type=10, label=1,
-      has_default_value=False, default_value=None,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='base_angular_vel', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.base_angular_vel', index=4,
-      number=4, type=11, cpp_type=10, label=1,
-      has_default_value=False, default_value=None,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='motor_states', full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.motor_states', index=5,
-      number=5, type=11, cpp_type=10, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=301,
-  serialized_end=635,
+    name='MinitaurStateAction',
+    full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction',
+    filename=None,
+    file=DESCRIPTOR,
+    containing_type=None,
+    fields=[
+        _descriptor.FieldDescriptor(
+            name='info_valid',
+            full_name=
+            'robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.info_valid',
+            index=0,
+            number=6,
+            type=8,
+            cpp_type=7,
+            label=1,
+            has_default_value=False,
+            default_value=False,
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            options=None,
+            file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(
+            name='time',
+            full_name='robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.time',
+            index=1,
+            number=1,
+            type=11,
+            cpp_type=10,
+            label=1,
+            has_default_value=False,
+            default_value=None,
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            options=None,
+            file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(
+            name='base_position',
+            full_name=
+            'robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.base_position',
+            index=2,
+            number=2,
+            type=11,
+            cpp_type=10,
+            label=1,
+            has_default_value=False,
+            default_value=None,
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            options=None,
+            file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(
+            name='base_orientation',
+            full_name=
+            'robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.base_orientation',
+            index=3,
+            number=3,
+            type=11,
+            cpp_type=10,
+            label=1,
+            has_default_value=False,
+            default_value=None,
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            options=None,
+            file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(
+            name='base_angular_vel',
+            full_name=
+            'robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.base_angular_vel',
+            index=4,
+            number=4,
+            type=11,
+            cpp_type=10,
+            label=1,
+            has_default_value=False,
+            default_value=None,
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            options=None,
+            file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(
+            name='motor_states',
+            full_name=
+            'robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction.motor_states',
+            index=5,
+            number=5,
+            type=11,
+            cpp_type=10,
+            label=3,
+            has_default_value=False,
+            default_value=[],
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            options=None,
+            file=DESCRIPTOR),
+    ],
+    extensions=[],
+    nested_types=[],
+    enum_types=[],
+    options=None,
+    is_extendable=False,
+    syntax='proto3',
+    extension_ranges=[],
+    oneofs=[],
+    serialized_start=301,
+    serialized_end=635,
 )
 
 _MINITAUREPISODE.fields_by_name['state_action'].message_type = _MINITAURSTATEACTION
@@ -193,26 +296,34 @@ DESCRIPTOR.message_types_by_name['MinitaurMotorState'] = _MINITAURMOTORSTATE
 DESCRIPTOR.message_types_by_name['MinitaurStateAction'] = _MINITAURSTATEACTION
 _sym_db.RegisterFileDescriptor(DESCRIPTOR)
 
-MinitaurEpisode = _reflection.GeneratedProtocolMessageType('MinitaurEpisode', (_message.Message,), dict(
-  DESCRIPTOR = _MINITAUREPISODE,
-  __module__ = 'minitaur_logging_pb2'
-  # @@protoc_insertion_point(class_scope:robotics.reinforcement_learning.minitaur.envs.MinitaurEpisode)
-  ))
+MinitaurEpisode = _reflection.GeneratedProtocolMessageType(
+    'MinitaurEpisode',
+    (_message.Message,),
+    dict(
+        DESCRIPTOR=_MINITAUREPISODE,
+        __module__='minitaur_logging_pb2'
+        # @@protoc_insertion_point(class_scope:robotics.reinforcement_learning.minitaur.envs.MinitaurEpisode)
+    ))
 _sym_db.RegisterMessage(MinitaurEpisode)
 
-MinitaurMotorState = _reflection.GeneratedProtocolMessageType('MinitaurMotorState', (_message.Message,), dict(
-  DESCRIPTOR = _MINITAURMOTORSTATE,
-  __module__ = 'minitaur_logging_pb2'
-  # @@protoc_insertion_point(class_scope:robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState)
-  ))
+MinitaurMotorState = _reflection.GeneratedProtocolMessageType(
+    'MinitaurMotorState',
+    (_message.Message,),
+    dict(
+        DESCRIPTOR=_MINITAURMOTORSTATE,
+        __module__='minitaur_logging_pb2'
+        # @@protoc_insertion_point(class_scope:robotics.reinforcement_learning.minitaur.envs.MinitaurMotorState)
+    ))
 _sym_db.RegisterMessage(MinitaurMotorState)
 
-MinitaurStateAction = _reflection.GeneratedProtocolMessageType('MinitaurStateAction', (_message.Message,), dict(
-  DESCRIPTOR = _MINITAURSTATEACTION,
-  __module__ = 'minitaur_logging_pb2'
-  # @@protoc_insertion_point(class_scope:robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction)
-  ))
+MinitaurStateAction = _reflection.GeneratedProtocolMessageType(
+    'MinitaurStateAction',
+    (_message.Message,),
+    dict(
+        DESCRIPTOR=_MINITAURSTATEACTION,
+        __module__='minitaur_logging_pb2'
+        # @@protoc_insertion_point(class_scope:robotics.reinforcement_learning.minitaur.envs.MinitaurStateAction)
+    ))
 _sym_db.RegisterMessage(MinitaurStateAction)
 
-
 # @@protoc_insertion_point(module_scope)
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_proto_dump_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_proto_dump_example.py
index ca0534ad5..f194eb268 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_proto_dump_example.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_proto_dump_example.py
@@ -15,13 +15,13 @@ fields = episode.ListFields()
 recs = []
 
 for rec in fields[0][1]:
-	#print(rec.time)
-	for motorState in rec.motor_states:
-		#print("motorState.angle=",motorState.angle)
-		#print("motorState.velocity=",motorState.velocity)
-		#print("motorState.action=",motorState.action)
-		#print("motorState.torque=",motorState.torque)
-		recs.append([motorState.angle,motorState.velocity,motorState.action,motorState.torque])
-		
+  #print(rec.time)
+  for motorState in rec.motor_states:
+    #print("motorState.angle=",motorState.angle)
+    #print("motorState.velocity=",motorState.velocity)
+    #print("motorState.action=",motorState.action)
+    #print("motorState.torque=",motorState.torque)
+    recs.append([motorState.angle, motorState.velocity, motorState.action, motorState.torque])
+
 a = numpy.array(recs)
-numpy.savetxt("motorq_qdot_action_torque.csv", a, delimiter=",")
\ No newline at end of file
+numpy.savetxt("motorq_qdot_action_torque.csv", a, delimiter=",")
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller.py
index dcecf26a6..7fb43cbca 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller.py
@@ -17,8 +17,8 @@ DIAGONAL_LEG_PAIR_2 = (1, 2)
 
 class BehaviorParameters(
     collections.namedtuple("BehaviorParameters", [
-        "stance_duration", "desired_forward_speed", "turning_speed",
-        "standing_height", "desired_incline_angle"
+        "stance_duration", "desired_forward_speed", "turning_speed", "standing_height",
+        "desired_incline_angle"
     ])):
   __slots__ = ()
 
@@ -28,18 +28,16 @@ class BehaviorParameters(
               turning_speed=0,
               standing_height=0.21,
               desired_incline_angle=0):
-    return super(BehaviorParameters, cls).__new__(
-        cls, stance_duration, desired_forward_speed, turning_speed,
-        standing_height, desired_incline_angle)
+    return super(BehaviorParameters,
+                 cls).__new__(cls, stance_duration, desired_forward_speed, turning_speed,
+                              standing_height, desired_incline_angle)
 
 
 def motor_angles_to_leg_pose(motor_angles):
   leg_pose = np.zeros(_NUM_MOTORS)
   for i in range(_NUM_LEGS):
-    leg_pose[i] = 0.5 * (-1)**(i // 2) * (
-        motor_angles[2 * i + 1] - motor_angles[2 * i])
-    leg_pose[_NUM_LEGS + i] = 0.5 * (
-        motor_angles[2 * i] + motor_angles[2 * i + 1])
+    leg_pose[i] = 0.5 * (-1)**(i // 2) * (motor_angles[2 * i + 1] - motor_angles[2 * i])
+    leg_pose[_NUM_LEGS + i] = 0.5 * (motor_angles[2 * i] + motor_angles[2 * i + 1])
   return leg_pose
 
 
@@ -47,8 +45,7 @@ def leg_pose_to_motor_angles(leg_pose):
   motor_pose = np.zeros(_NUM_MOTORS)
   for i in range(_NUM_LEGS):
     motor_pose[2 * i] = leg_pose[_NUM_LEGS + i] - (-1)**(i // 2) * leg_pose[i]
-    motor_pose[2 * i + 1] = (
-        leg_pose[_NUM_LEGS + i] + (-1)**(i // 2) * leg_pose[i])
+    motor_pose[2 * i + 1] = (leg_pose[_NUM_LEGS + i] + (-1)**(i // 2) * leg_pose[i])
   return motor_pose
 
 
@@ -85,8 +82,7 @@ def foot_position_to_leg_pose(foot_position):
   ext = math.acos(cos_ext)
 
   hip_toe = math.sqrt(hip_toe_sqr)
-  cos_theta = (hip_toe_sqr + hip_ankle_sqr -
-               (l3 - l2)**2) / (2 * hip_ankle * hip_toe)
+  cos_theta = (hip_toe_sqr + hip_ankle_sqr - (l3 - l2)**2) / (2 * hip_ankle * hip_toe)
 
   assert cos_theta > 0
   theta = math.acos(cos_theta)
@@ -94,8 +90,7 @@ def foot_position_to_leg_pose(foot_position):
   return (-sw, ext)
 
 
-def foot_horizontal_position_to_leg_swing(foot_horizontal_position,
-                                          leg_extension):
+def foot_horizontal_position_to_leg_swing(foot_horizontal_position, leg_extension):
   """Computes the target leg swing.
 
   Sometimes it is more convenient to plan in the hybrid space.
@@ -119,8 +114,7 @@ def foot_horizontal_position_to_leg_swing(foot_horizontal_position,
 
   # Cap the foot horizontal (projected) position so the target leg pose is
   # always feasible.
-  foot_position = max(
-      min(toe_hip_len * 0.8, foot_horizontal_position), -toe_hip_len * 0.5)
+  foot_position = max(min(toe_hip_len * 0.8, foot_horizontal_position), -toe_hip_len * 0.5)
 
   sw_and_theta = math.asin(foot_position / toe_hip_len)
 
@@ -171,9 +165,7 @@ def generate_swing_trajectory(phase, init_pose, end_pose):
 
   b = (phi * phi * delta_2 - delta_1) / delta_p
 
-  delta = (
-      a * normalized_phase * normalized_phase + b * normalized_phase +
-      init_delta)
+  delta = (a * normalized_phase * normalized_phase + b * normalized_phase + init_delta)
 
   l1 = _UPPER_LEG_LEN
   l2 = _LOWER_SHORT_LEG_LEN
@@ -209,10 +201,9 @@ class RaibertSwingLegController(object):
     leg_pose_set = []
     for i in raibiert_controller.swing_set:
       target_foot_horizontal_position = (
-          raibiert_controller.behavior_parameters.stance_duration / 2 *
-          current_speed + self._speed_gain *
-          (current_speed -
-           raibiert_controller.behavior_parameters.desired_forward_speed))
+          raibiert_controller.behavior_parameters.stance_duration / 2 * current_speed +
+          self._speed_gain *
+          (current_speed - raibiert_controller.behavior_parameters.desired_forward_speed))
 
       # Use the swing phase [0, 1] to track the foot. The idea is
       # straightforward:
@@ -221,19 +212,19 @@ class RaibertSwingLegController(object):
       # 3) Find the next leg pose on the curve based on how much time left.
 
       # 1) Convert the target foot
-      target_leg_extension = (
-          raibiert_controller.nominal_leg_extension -
-          self._leg_extension_clearance)
-      target_leg_swing = foot_horizontal_position_to_leg_swing(
-          target_foot_horizontal_position, leg_extension=target_leg_extension)
+      target_leg_extension = (raibiert_controller.nominal_leg_extension -
+                              self._leg_extension_clearance)
+      target_leg_swing = foot_horizontal_position_to_leg_swing(target_foot_horizontal_position,
+                                                               leg_extension=target_leg_extension)
 
       target_leg_pose = (target_leg_swing, target_leg_extension)
 
       # 2) Generates the curve from the current leg pose to the target leg pose.
       # and Find the next leg pose on the curve based on current swing phase.
 
-      desired_leg_pose = self._leg_trajectory_generator(
-          phase, raibiert_controller.swing_start_leg_pose, target_leg_pose)
+      desired_leg_pose = self._leg_trajectory_generator(phase,
+                                                        raibiert_controller.swing_start_leg_pose,
+                                                        target_leg_pose)
 
       leg_pose_set.append(desired_leg_pose)
 
@@ -244,9 +235,7 @@ class RaibertSwingLegController(object):
 
 class RaibertStanceLegController(object):
 
-  def __init__(self,
-               speed_gain=0.1,
-               leg_trajectory_generator=generate_stance_trajectory):
+  def __init__(self, speed_gain=0.1, leg_trajectory_generator=generate_stance_trajectory):
     self._speed_gain = speed_gain
     self._leg_trajectory_generator = leg_trajectory_generator
 
@@ -255,20 +244,18 @@ class RaibertStanceLegController(object):
     current_speed = raibiert_controller.estimate_base_velocity()
     leg_pose_set = []
     for i in raibiert_controller.stance_set:
-      desired_forward_speed = (
-          raibiert_controller.behavior_parameters.desired_forward_speed)
-      target_foot_position = -(
-          raibiert_controller.behavior_parameters.stance_duration / 2 *
-          current_speed - self._speed_gain *
-          (current_speed - desired_forward_speed))
+      desired_forward_speed = (raibiert_controller.behavior_parameters.desired_forward_speed)
+      target_foot_position = -(raibiert_controller.behavior_parameters.stance_duration / 2 *
+                               current_speed - self._speed_gain *
+                               (current_speed - desired_forward_speed))
 
       target_leg_pose = (foot_horizontal_position_to_leg_swing(
-          target_foot_position,
-          leg_extension=raibiert_controller.nominal_leg_extension),
+          target_foot_position, leg_extension=raibiert_controller.nominal_leg_extension),
                          raibiert_controller.nominal_leg_extension)
 
-      desired_leg_pose = self._leg_trajectory_generator(
-          phase, raibiert_controller.stance_start_leg_pose, target_leg_pose)
+      desired_leg_pose = self._leg_trajectory_generator(phase,
+                                                        raibiert_controller.stance_start_leg_pose,
+                                                        target_leg_pose)
 
       leg_pose_set.append(desired_leg_pose)
 
@@ -288,8 +275,7 @@ class MinitaurRaibertTrottingController(object):
     self._robot = robot
     self._behavior_parameters = behavior_parameters
 
-    nominal_leg_pose = foot_position_to_leg_pose(
-        (0, -self._behavior_parameters.standing_height))
+    nominal_leg_pose = foot_position_to_leg_pose((0, -self._behavior_parameters.standing_height))
     self._nominal_leg_extension = nominal_leg_pose[1]
 
     self._swing_leg_controller = swing_leg_controller
@@ -337,8 +323,7 @@ class MinitaurRaibertTrottingController(object):
     # extract the swing leg pose from the current_leg_pose
     leg_pose = []
     for index in leg_indices:
-      leg_pose.append(
-          [current_leg_pose[index], current_leg_pose[index + _NUM_LEGS]])
+      leg_pose.append([current_leg_pose[index], current_leg_pose[index + _NUM_LEGS]])
 
     leg_pose = np.array(leg_pose)
     return np.mean(leg_pose, axis=0)
@@ -353,13 +338,13 @@ class MinitaurRaibertTrottingController(object):
 
   def get_phase(self):
     """Compute the current stance/swing phase."""
-    return math.fmod(self._time, self._behavior_parameters.stance_duration
-                    ) / self._behavior_parameters.stance_duration
+    return math.fmod(
+        self._time,
+        self._behavior_parameters.stance_duration) / self._behavior_parameters.stance_duration
 
   def update_swing_stance_set(self):
     """Switch the set of swing/stance legs based on timing."""
-    swing_stance_phase = math.fmod(
-        self._time, 2 * self._behavior_parameters.stance_duration)
+    swing_stance_phase = math.fmod(self._time, 2 * self._behavior_parameters.stance_duration)
     if swing_stance_phase < self._behavior_parameters.stance_duration:
       return (DIAGONAL_LEG_PAIR_1, DIAGONAL_LEG_PAIR_2)
     return (DIAGONAL_LEG_PAIR_2, DIAGONAL_LEG_PAIR_1)
@@ -387,8 +372,8 @@ class MinitaurRaibertTrottingController(object):
     toe_hip_len = math.sqrt(x**2 + y**2)
     horizontal_dist = toe_hip_len * delta_sw
     phase = self.get_phase()
-    speed = 0 if phase < 0.1 else horizontal_dist / (
-        self._behavior_parameters.stance_duration * phase)
+    speed = 0 if phase < 0.1 else horizontal_dist / (self._behavior_parameters.stance_duration *
+                                                     phase)
     return speed
 
   def get_swing_leg_action(self):
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller_example.py
index 87958dde5..8adeee376 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller_example.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_raibert_controller_example.py
@@ -1,6 +1,5 @@
 #The example to run the raibert controller in a Minitaur gym env.
 
-
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -9,8 +8,7 @@ import os
 import inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(os.path.dirname(currentdir)))
-os.sys.path.insert(0,parentdir)
-
+os.sys.path.insert(0, parentdir)
 
 import tensorflow as tf
 from pybullet_envs.minitaur.envs import minitaur_raibert_controller
@@ -21,9 +19,8 @@ FLAGS = tf.app.flags.FLAGS
 
 flags.DEFINE_float("motor_kp", 1.0, "The position gain of the motor.")
 flags.DEFINE_float("motor_kd", 0.015, "The speed gain of the motor.")
-flags.DEFINE_float(
-    "control_latency", 0.02, "The latency between sensor measurement and action"
-    " execution the robot.")
+flags.DEFINE_float("control_latency", 0.02, "The latency between sensor measurement and action"
+                   " execution the robot.")
 flags.DEFINE_string("log_path", None, "The directory to write the log file.")
 
 
@@ -55,20 +52,18 @@ def main(argv):
         log_path=FLAGS.log_path)
     env.reset()
 
-    controller = minitaur_raibert_controller.MinitaurRaibertTrottingController(
-        env.minitaur)
+    controller = minitaur_raibert_controller.MinitaurRaibertTrottingController(env.minitaur)
 
     tstart = env.minitaur.GetTimeSinceReset()
     for _ in range(1000):
       t = env.minitaur.GetTimeSinceReset() - tstart
-      controller.behavior_parameters = (
-          minitaur_raibert_controller.BehaviorParameters(
-              desired_forward_speed=speed(t)))
+      controller.behavior_parameters = (minitaur_raibert_controller.BehaviorParameters(
+          desired_forward_speed=speed(t)))
       controller.update(t)
       env.step(controller.get_action())
   finally:
     env.close()
 
+
 if __name__ == "__main__":
   tf.app.run(main)
-
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_rainbow_dash.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_rainbow_dash.py
index c33b4af77..d1787ea35 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_rainbow_dash.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_rainbow_dash.py
@@ -4,10 +4,10 @@ It is the result of first pass system identification for the rainbow dash robot.
 
 """
 
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import math
 
@@ -46,13 +46,12 @@ class MinitaurRainbowDash(minitaur.Minitaur):
             "%s/quadruped/minitaur_rainbow_dash.urdf" % self._urdf_root,
             init_position,
             useFixedBase=self._on_rack,
-            flags=(
-                self._pybullet_client.URDF_USE_SELF_COLLISION_EXCLUDE_PARENT))
+            flags=(self._pybullet_client.URDF_USE_SELF_COLLISION_EXCLUDE_PARENT))
       else:
-        self.quadruped = self._pybullet_client.loadURDF(
-            "%s/quadruped/minitaur_rainbow_dash.urdf" % self._urdf_root,
-            init_position,
-            useFixedBase=self._on_rack)
+        self.quadruped = self._pybullet_client.loadURDF("%s/quadruped/minitaur_rainbow_dash.urdf" %
+                                                        self._urdf_root,
+                                                        init_position,
+                                                        useFixedBase=self._on_rack)
       self._BuildJointNameToIdDict()
       self._BuildUrdfIds()
       if self._remove_default_joint_damping:
@@ -62,10 +61,9 @@ class MinitaurRainbowDash(minitaur.Minitaur):
       self._RecordInertiaInfoFromURDF()
       self.ResetPose(add_constraint=True)
     else:
-      self._pybullet_client.resetBasePositionAndOrientation(
-          self.quadruped, init_position, minitaur.INIT_ORIENTATION)
-      self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0],
-                                              [0, 0, 0])
+      self._pybullet_client.resetBasePositionAndOrientation(self.quadruped, init_position,
+                                                            minitaur.INIT_ORIENTATION)
+      self._pybullet_client.resetBaseVelocity(self.quadruped, [0, 0, 0], [0, 0, 0])
       self.ResetPose(add_constraint=False)
 
     self._overheat_counter = np.zeros(self.num_motors)
@@ -103,68 +101,60 @@ class MinitaurRainbowDash(minitaur.Minitaur):
     knee_angle = -2.1834
 
     leg_position = minitaur.LEG_POSITION[leg_id]
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["motor_" + leg_position + "L_joint"],
-        self._motor_direction[2 * leg_id] * half_pi,
-        targetVelocity=0)
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["knee_" + leg_position + "L_joint"],
-        self._motor_direction[2 * leg_id] * knee_angle,
-        targetVelocity=0)
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["motor_" + leg_position + "R_joint"],
-        self._motor_direction[2 * leg_id + 1] * half_pi,
-        targetVelocity=0)
-    self._pybullet_client.resetJointState(
-        self.quadruped,
-        self._joint_name_to_id["knee_" + leg_position + "R_joint"],
-        self._motor_direction[2 * leg_id + 1] * knee_angle,
-        targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["motor_" + leg_position +
+                                                                 "L_joint"],
+                                          self._motor_direction[2 * leg_id] * half_pi,
+                                          targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["knee_" + leg_position +
+                                                                 "L_joint"],
+                                          self._motor_direction[2 * leg_id] * knee_angle,
+                                          targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["motor_" + leg_position +
+                                                                 "R_joint"],
+                                          self._motor_direction[2 * leg_id + 1] * half_pi,
+                                          targetVelocity=0)
+    self._pybullet_client.resetJointState(self.quadruped,
+                                          self._joint_name_to_id["knee_" + leg_position +
+                                                                 "R_joint"],
+                                          self._motor_direction[2 * leg_id + 1] * knee_angle,
+                                          targetVelocity=0)
     if add_constraint:
       if leg_id < 2:
         self._pybullet_client.createConstraint(
-            self.quadruped,
-            self._joint_name_to_id["knee_" + leg_position + "R_joint"],
-            self.quadruped,
-            self._joint_name_to_id["knee_" + leg_position + "L_joint"],
-            self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0],
-            KNEE_CONSTRAINT_POINT_SHORT, KNEE_CONSTRAINT_POINT_LONG)
+            self.quadruped, self._joint_name_to_id["knee_" + leg_position + "R_joint"],
+            self.quadruped, self._joint_name_to_id["knee_" + leg_position + "L_joint"],
+            self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], KNEE_CONSTRAINT_POINT_SHORT,
+            KNEE_CONSTRAINT_POINT_LONG)
       else:
         self._pybullet_client.createConstraint(
-            self.quadruped,
-            self._joint_name_to_id["knee_" + leg_position + "R_joint"],
-            self.quadruped,
-            self._joint_name_to_id["knee_" + leg_position + "L_joint"],
-            self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0],
-            KNEE_CONSTRAINT_POINT_LONG, KNEE_CONSTRAINT_POINT_SHORT)
+            self.quadruped, self._joint_name_to_id["knee_" + leg_position + "R_joint"],
+            self.quadruped, self._joint_name_to_id["knee_" + leg_position + "L_joint"],
+            self._pybullet_client.JOINT_POINT2POINT, [0, 0, 0], KNEE_CONSTRAINT_POINT_LONG,
+            KNEE_CONSTRAINT_POINT_SHORT)
 
     if self._accurate_motor_model_enabled or self._pd_control_enabled:
       # Disable the default motor in pybullet.
       self._pybullet_client.setJointMotorControl2(
           bodyIndex=self.quadruped,
-          jointIndex=(
-              self._joint_name_to_id["motor_" + leg_position + "L_joint"]),
+          jointIndex=(self._joint_name_to_id["motor_" + leg_position + "L_joint"]),
           controlMode=self._pybullet_client.VELOCITY_CONTROL,
           targetVelocity=0,
           force=knee_friction_force)
       self._pybullet_client.setJointMotorControl2(
           bodyIndex=self.quadruped,
-          jointIndex=(
-              self._joint_name_to_id["motor_" + leg_position + "R_joint"]),
+          jointIndex=(self._joint_name_to_id["motor_" + leg_position + "R_joint"]),
           controlMode=self._pybullet_client.VELOCITY_CONTROL,
           targetVelocity=0,
           force=knee_friction_force)
 
     else:
-      self._SetDesiredMotorAngleByName(
-          "motor_" + leg_position + "L_joint",
-          self._motor_direction[2 * leg_id] * half_pi)
-      self._SetDesiredMotorAngleByName(
-          "motor_" + leg_position + "R_joint",
-          self._motor_direction[2 * leg_id + 1] * half_pi)
+      self._SetDesiredMotorAngleByName("motor_" + leg_position + "L_joint",
+                                       self._motor_direction[2 * leg_id] * half_pi)
+      self._SetDesiredMotorAngleByName("motor_" + leg_position + "R_joint",
+                                       self._motor_direction[2 * leg_id + 1] * half_pi)
 
     self._pybullet_client.setJointMotorControl2(
         bodyIndex=self.quadruped,
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env.py
index 540965a2f..a9492f309 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env.py
@@ -42,20 +42,17 @@ class MinitaurRandomizeTerrainGymEnv(minitaur_gym_env.MinitaurGymEnv):
         fileName=terrain_file_name,
         flags=1,
         meshScale=[0.5, 0.5, 0.5])
-    self._pybullet_client.createMultiBody(terrain_mass,
-                                          terrain_collision_shape_id,
-                                          terrain_visual_shape_id,
-                                          terrain_position,
+    self._pybullet_client.createMultiBody(terrain_mass, terrain_collision_shape_id,
+                                          terrain_visual_shape_id, terrain_position,
                                           terrain_orientation)
     self._pybullet_client.setGravity(0, 0, -10)
-    self.minitaur = (minitaur.Minitaur(
-        pybullet_client=self._pybullet_client,
-        urdf_root=self._urdf_root,
-        time_step=self._time_step,
-        self_collision_enabled=self._self_collision_enabled,
-        motor_velocity_limit=self._motor_velocity_limit,
-        pd_control_enabled=self._pd_control_enabled,
-        on_rack=self._on_rack))
+    self.minitaur = (minitaur.Minitaur(pybullet_client=self._pybullet_client,
+                                       urdf_root=self._urdf_root,
+                                       time_step=self._time_step,
+                                       self_collision_enabled=self._self_collision_enabled,
+                                       motor_velocity_limit=self._motor_velocity_limit,
+                                       pd_control_enabled=self._pd_control_enabled,
+                                       on_rack=self._on_rack))
     self._last_base_position = [0, 0, 0]
     for _ in xrange(100):
       if self._pd_control_enabled:
@@ -78,6 +75,5 @@ class MinitaurRandomizeTerrainGymEnv(minitaur_gym_env.MinitaurGymEnv):
     asset_source = os.path.join(terrain_dir, terrain_file_name)
     asset_destination = os.path.join(FLAGS.storage_dir, terrain_file_name)
     gfile.Copy(asset_source, asset_destination, overwrite=True)
-    terrain_file_name_complete = os.path.join(FLAGS.storage_dir,
-                                              terrain_file_name)
+    terrain_file_name_complete = os.path.join(FLAGS.storage_dir, terrain_file_name)
     return terrain_file_name_complete
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env_example.py
index 0e5638790..3ecf6dc9c 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env_example.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_randomize_terrain_gym_env_example.py
@@ -21,10 +21,7 @@ def ResetTerrainExample():
   num_reset = 10
   steps = 100
   env = minitaur_randomize_terrain_gym_env.MinitaurRandomizeTerrainGymEnv(
-      render=True,
-      leg_model_enabled=False,
-      motor_velocity_limit=np.inf,
-      pd_control_enabled=True)
+      render=True, leg_model_enabled=False, motor_velocity_limit=np.inf, pd_control_enabled=True)
   action = [math.pi / 2] * 8
   for _ in xrange(num_reset):
     env.reset()
@@ -37,10 +34,7 @@ def ResetTerrainExample():
 def SinePolicyExample():
   """An example of minitaur walking with a sine gait."""
   env = minitaur_randomize_terrain_gym_env.MinitaurRandomizeTerrainGymEnv(
-      render=True,
-      motor_velocity_limit=np.inf,
-      pd_control_enabled=True,
-      on_rack=False)
+      render=True, motor_velocity_limit=np.inf, pd_control_enabled=True, on_rack=False)
   sum_reward = 0
   steps = 200
   amplitude_1_bound = 0.5
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env.py
index 8ae8314f0..7f899cb4a 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env.py
@@ -2,10 +2,10 @@
 
 """
 
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import collections
 import math
@@ -19,8 +19,7 @@ NUM_LEGS = 4
 NUM_MOTORS = 2 * NUM_LEGS
 
 MinitaurPose = collections.namedtuple(
-    "MinitaurPose",
-    "swing_angle_1, swing_angle_2, swing_angle_3, swing_angle_4, "
+    "MinitaurPose", "swing_angle_1, swing_angle_2, swing_angle_3, swing_angle_4, "
     "extension_angle_1, extension_angle_2, extension_angle_3, "
     "extension_angle_4")
 
@@ -35,10 +34,7 @@ class MinitaurReactiveEnv(minitaur_gym_env.MinitaurGymEnv):
   expenditure.
 
   """
-  metadata = {
-      "render.modes": ["human", "rgb_array"],
-      "video.frames_per_second": 166
-  }
+  metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 166}
 
   def __init__(self,
                urdf_version=None,
@@ -96,24 +92,24 @@ class MinitaurReactiveEnv(minitaur_gym_env.MinitaurGymEnv):
     """
     self._use_angle_in_observation = use_angle_in_observation
 
-    super(MinitaurReactiveEnv, self).__init__(
-        urdf_version=urdf_version,
-        energy_weight=energy_weight,
-        accurate_motor_model_enabled=accurate_motor_model_enabled,
-        motor_overheat_protection=True,
-        motor_kp=motor_kp,
-        motor_kd=motor_kd,
-        remove_default_joint_damping=remove_default_joint_damping,
-        control_latency=control_latency,
-        pd_latency=pd_latency,
-        on_rack=on_rack,
-        render=render,
-        hard_reset=hard_reset,
-        num_steps_to_log=num_steps_to_log,
-        env_randomizer=env_randomizer,
-        log_path=log_path,
-        control_time_step=control_time_step,
-        action_repeat=action_repeat)
+    super(MinitaurReactiveEnv,
+          self).__init__(urdf_version=urdf_version,
+                         energy_weight=energy_weight,
+                         accurate_motor_model_enabled=accurate_motor_model_enabled,
+                         motor_overheat_protection=True,
+                         motor_kp=motor_kp,
+                         motor_kd=motor_kd,
+                         remove_default_joint_damping=remove_default_joint_damping,
+                         control_latency=control_latency,
+                         pd_latency=pd_latency,
+                         on_rack=on_rack,
+                         render=render,
+                         hard_reset=hard_reset,
+                         num_steps_to_log=num_steps_to_log,
+                         env_randomizer=env_randomizer,
+                         log_path=log_path,
+                         control_time_step=control_time_step,
+                         action_repeat=action_repeat)
 
     action_dim = 8
     action_low = np.array([-0.5] * action_dim)
@@ -126,34 +122,31 @@ class MinitaurReactiveEnv(minitaur_gym_env.MinitaurGymEnv):
   def reset(self):
     # TODO(b/73666007): Use composition instead of inheritance.
     # (http://go/design-for-testability-no-inheritance).
-    init_pose = MinitaurPose(
-        swing_angle_1=INIT_SWING_POS,
-        swing_angle_2=INIT_SWING_POS,
-        swing_angle_3=INIT_SWING_POS,
-        swing_angle_4=INIT_SWING_POS,
-        extension_angle_1=INIT_EXTENSION_POS,
-        extension_angle_2=INIT_EXTENSION_POS,
-        extension_angle_3=INIT_EXTENSION_POS,
-        extension_angle_4=INIT_EXTENSION_POS)
+    init_pose = MinitaurPose(swing_angle_1=INIT_SWING_POS,
+                             swing_angle_2=INIT_SWING_POS,
+                             swing_angle_3=INIT_SWING_POS,
+                             swing_angle_4=INIT_SWING_POS,
+                             extension_angle_1=INIT_EXTENSION_POS,
+                             extension_angle_2=INIT_EXTENSION_POS,
+                             extension_angle_3=INIT_EXTENSION_POS,
+                             extension_angle_4=INIT_EXTENSION_POS)
     # TODO(b/73734502): Refactor input of _convert_from_leg_model to namedtuple.
     initial_motor_angles = self._convert_from_leg_model(list(init_pose))
-    super(MinitaurReactiveEnv, self).reset(
-        initial_motor_angles=initial_motor_angles, reset_duration=0.5)
+    super(MinitaurReactiveEnv, self).reset(initial_motor_angles=initial_motor_angles,
+                                           reset_duration=0.5)
     return self._get_observation()
 
   def _convert_from_leg_model(self, leg_pose):
     motor_pose = np.zeros(NUM_MOTORS)
     for i in range(NUM_LEGS):
       motor_pose[int(2 * i)] = leg_pose[NUM_LEGS + i] - (-1)**int(i / 2) * leg_pose[i]
-      motor_pose[int(2 * i + 1)] = (
-          leg_pose[NUM_LEGS + i] + (-1)**int(i / 2) * leg_pose[i])
+      motor_pose[int(2 * i + 1)] = (leg_pose[NUM_LEGS + i] + (-1)**int(i / 2) * leg_pose[i])
     return motor_pose
 
   def _signal(self, t):
     initial_pose = np.array([
-        INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS,
-        INIT_EXTENSION_POS, INIT_EXTENSION_POS, INIT_EXTENSION_POS,
-        INIT_EXTENSION_POS
+        INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_SWING_POS, INIT_EXTENSION_POS,
+        INIT_EXTENSION_POS, INIT_EXTENSION_POS, INIT_EXTENSION_POS
     ])
     return initial_pose
 
@@ -214,8 +207,7 @@ class MinitaurReactiveEnv(minitaur_gym_env.MinitaurGymEnv):
     upper_bound_pitch_dot = 2 * math.pi / self._time_step
     upper_bound_motor_angle = 2 * math.pi
     upper_bound = [
-        upper_bound_roll, upper_bound_pitch, upper_bound_roll_dot,
-        upper_bound_pitch_dot
+        upper_bound_roll, upper_bound_pitch, upper_bound_roll_dot, upper_bound_pitch_dot
     ]
 
     if self._use_angle_in_observation:
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env_example.py
index 2c2be7106..e0d4000c1 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env_example.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_reactive_env_example.py
@@ -10,17 +10,14 @@ import time
 import inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(os.path.dirname(currentdir)))
-print("parentdir=",parentdir)
-os.sys.path.insert(0,parentdir)
-
-
+print("parentdir=", parentdir)
+os.sys.path.insert(0, parentdir)
 
 import tensorflow as tf
 from pybullet_envs.minitaur.agents.scripts import utility
 import pybullet_data
 from pybullet_envs.minitaur.envs import simple_ppo_agent
 
-
 flags = tf.app.flags
 FLAGS = tf.app.flags.FLAGS
 LOG_DIR = os.path.join(pybullet_data.getDataPath(), "policies/ppo/minitaur_reactive_env")
@@ -36,13 +33,12 @@ def main(argv):
   network = config.network
 
   with tf.Session() as sess:
-    agent = simple_ppo_agent.SimplePPOPolicy(
-        sess,
-        env,
-        network,
-        policy_layers=policy_layers,
-        value_layers=value_layers,
-        checkpoint=os.path.join(LOG_DIR, CHECKPOINT))
+    agent = simple_ppo_agent.SimplePPOPolicy(sess,
+                                             env,
+                                             network,
+                                             policy_layers=policy_layers,
+                                             value_layers=value_layers,
+                                             checkpoint=os.path.join(LOG_DIR, CHECKPOINT))
 
     sum_reward = 0
     observation = env.reset()
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env.py
index 3ea1b2e09..8135b7265 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env.py
@@ -29,10 +29,7 @@ class MinitaurStandGymEnv(minitaur_gym_env.MinitaurGymEnv):
   function is based on how long the minitaur stays standing up.
 
   """
-  metadata = {
-      "render.modes": ["human", "rgb_array"],
-      "video.frames_per_second": 50
-  }
+  metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 50}
 
   def __init__(self,
                urdf_root=pybullet_data.getDataPath(),
@@ -53,16 +50,15 @@ class MinitaurStandGymEnv(minitaur_gym_env.MinitaurGymEnv):
       pd_control_enabled: Whether to use PD controller for each motor.
       render: Whether to render the simulation.
     """
-    super(MinitaurStandGymEnv, self).__init__(
-        urdf_root=urdf_root,
-        action_repeat=action_repeat,
-        observation_noise_stdev=observation_noise_stdev,
-        self_collision_enabled=self_collision_enabled,
-        motor_velocity_limit=motor_velocity_limit,
-        pd_control_enabled=pd_control_enabled,
-        accurate_motor_model_enabled=True,
-        motor_overheat_protection=True,
-        render=render)
+    super(MinitaurStandGymEnv, self).__init__(urdf_root=urdf_root,
+                                              action_repeat=action_repeat,
+                                              observation_noise_stdev=observation_noise_stdev,
+                                              self_collision_enabled=self_collision_enabled,
+                                              motor_velocity_limit=motor_velocity_limit,
+                                              pd_control_enabled=pd_control_enabled,
+                                              accurate_motor_model_enabled=True,
+                                              motor_overheat_protection=True,
+                                              render=render)
     # Set the action dimension to 1, and reset the action space.
     action_dim = 1
     action_high = np.array([self._action_bound] * action_dim)
@@ -85,8 +81,8 @@ class MinitaurStandGymEnv(minitaur_gym_env.MinitaurGymEnv):
     for t in range(5000):
       if self._is_render:
         base_pos = self.minitaur.GetBasePosition()
-        self._pybullet_client.resetDebugVisualizerCamera(
-            self._cam_dist, self._cam_yaw, self._cam_pitch, base_pos)
+        self._pybullet_client.resetDebugVisualizerCamera(self._cam_dist, self._cam_yaw,
+                                                         self._cam_pitch, base_pos)
       state = self._get_true_observation()
       action = self._policy_flip(t, state[24:28])
       self.minitaur.ApplyAction(action)
@@ -226,8 +222,7 @@ class MinitaurStandGymEnv(minitaur_gym_env.MinitaurGymEnv):
     # Lower the signal a little, so that it becomes positive only for a short
     # amount time.
     lower_signal = -0.94
-    signal_unit = math.copysign(intensity,
-                                math.sin(time_step * speed) + lower_signal)
+    signal_unit = math.copysign(intensity, math.sin(time_step * speed) + lower_signal)
     # Only extend the leg, don't shorten.
     if signal_unit < 0:
       signal_unit = 0
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env_example.py
index 848c39d4a..aa07bd029 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env_example.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_stand_gym_env_example.py
@@ -13,9 +13,8 @@ from pybullet_envs.minitaur.envs import minitaur_stand_gym_env
 def StandUpExample():
   """An example that the minitaur stands up."""
   steps = 1000
-  environment = minitaur_stand_gym_env.MinitaurStandGymEnv(
-      render=True,
-      motor_velocity_limit=np.inf)
+  environment = minitaur_stand_gym_env.MinitaurStandGymEnv(render=True,
+                                                           motor_velocity_limit=np.inf)
   action = [0.5]
   _, _, done, _ = environment.step(action)
   for t in range(steps):
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env.py
index 97f820f54..e79e54926 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env.py
@@ -24,10 +24,7 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv):
   controller (e.g. a neural network).
 
   """
-  metadata = {
-      "render.modes": ["human", "rgb_array"],
-      "video.frames_per_second": 166
-  }
+  metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 166}
 
   def __init__(self,
                urdf_version=None,
@@ -103,8 +100,8 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv):
 
     # The reset position.
     self._init_pose = [
-        init_swing, init_swing, init_swing, init_swing, init_extension,
-        init_extension, init_extension, init_extension
+        init_swing, init_swing, init_swing, init_swing, init_extension, init_extension,
+        init_extension, init_extension
     ]
 
     self._step_frequency = step_frequency
@@ -112,23 +109,23 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv):
     self._swing_amplitude = swing_amplitude
     self._use_signal_in_observation = use_signal_in_observation
     self._use_angle_in_observation = use_angle_in_observation
-    super(MinitaurTrottingEnv, self).__init__(
-        urdf_version=urdf_version,
-        accurate_motor_model_enabled=accurate_motor_model_enabled,
-        motor_overheat_protection=True,
-        motor_kp=motor_kp,
-        motor_kd=motor_kd,
-        remove_default_joint_damping=remove_default_joint_damping,
-        control_latency=control_latency,
-        pd_latency=pd_latency,
-        on_rack=on_rack,
-        render=render,
-        hard_reset=hard_reset,
-        num_steps_to_log=num_steps_to_log,
-        env_randomizer=env_randomizer,
-        log_path=log_path,
-        control_time_step=control_time_step,
-        action_repeat=action_repeat)
+    super(MinitaurTrottingEnv,
+          self).__init__(urdf_version=urdf_version,
+                         accurate_motor_model_enabled=accurate_motor_model_enabled,
+                         motor_overheat_protection=True,
+                         motor_kp=motor_kp,
+                         motor_kd=motor_kd,
+                         remove_default_joint_damping=remove_default_joint_damping,
+                         control_latency=control_latency,
+                         pd_latency=pd_latency,
+                         on_rack=on_rack,
+                         render=render,
+                         hard_reset=hard_reset,
+                         num_steps_to_log=num_steps_to_log,
+                         env_randomizer=env_randomizer,
+                         log_path=log_path,
+                         control_time_step=control_time_step,
+                         action_repeat=action_repeat)
 
     action_dim = NUM_LEGS * 2
     action_high = np.array([0.25] * action_dim)
@@ -144,8 +141,8 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv):
     # [swing leg 1, swing leg 2, swing leg 3, swing leg 4,
     #  extension leg 1, extension leg 2, extension leg 3, extension leg 4]
     initial_motor_angles = self._convert_from_leg_model(self._init_pose)
-    super(MinitaurTrottingEnv, self).reset(
-        initial_motor_angles=initial_motor_angles, reset_duration=0.5)
+    super(MinitaurTrottingEnv, self).reset(initial_motor_angles=initial_motor_angles,
+                                           reset_duration=0.5)
     return self._get_observation()
 
   def _convert_from_leg_model(self, leg_pose):
@@ -161,8 +158,7 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv):
     motor_pose = np.zeros(NUM_MOTORS)
     for i in range(NUM_LEGS):
       motor_pose[int(2 * i)] = leg_pose[NUM_LEGS + i] - (-1)**int(i / 2) * leg_pose[i]
-      motor_pose[int(2 * i
-                 + 1)] = leg_pose[NUM_LEGS + i] + (-1)**int(i / 2) * leg_pose[i]
+      motor_pose[int(2 * i + 1)] = leg_pose[NUM_LEGS + i] + (-1)**int(i / 2) * leg_pose[i]
     return motor_pose
 
   def _gen_signal(self, t, phase):
@@ -179,8 +175,7 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv):
       The desired leg extension and swing angle at the current time.
     """
     period = 1 / self._step_frequency
-    extension = self._extension_amplitude * math.cos(
-        2 * math.pi / period * t + phase)
+    extension = self._extension_amplitude * math.cos(2 * math.pi / period * t + phase)
     swing = self._swing_amplitude * math.sin(2 * math.pi / period * t + phase)
     return extension, swing
 
@@ -198,8 +193,8 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv):
     ext_second_pair, sw_second_pair = self._gen_signal(t, math.pi)
 
     trotting_signal = np.array([
-        sw_first_pair, sw_second_pair, sw_second_pair, sw_first_pair,
-        ext_first_pair, ext_second_pair, ext_second_pair, ext_first_pair
+        sw_first_pair, sw_second_pair, sw_second_pair, sw_first_pair, ext_first_pair,
+        ext_second_pair, ext_second_pair, ext_first_pair
     ])
     signal = np.array(self._init_pose) + trotting_signal
     return signal
@@ -286,8 +281,7 @@ class MinitaurTrottingEnv(minitaur_gym_env.MinitaurGymEnv):
     """
     upper_bound = []
     upper_bound.extend([2 * math.pi] * 2)  # Roll, pitch, yaw of the base.
-    upper_bound.extend(
-        [2 * math.pi / self._time_step] * 2)  # Roll, pitch, yaw rate.
+    upper_bound.extend([2 * math.pi / self._time_step] * 2)  # Roll, pitch, yaw rate.
     if self._use_signal_in_observation:
       upper_bound.extend([2 * math.pi] * NUM_MOTORS)  # Signal
     if self._use_angle_in_observation:
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env_example.py
index e158e1160..e38f86428 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env_example.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_trotting_env_example.py
@@ -26,13 +26,12 @@ def main(argv):
   network = config.network
 
   with tf.Session() as sess:
-    agent = simple_ppo_agent.SimplePPOPolicy(
-        sess,
-        env,
-        network,
-        policy_layers=policy_layers,
-        value_layers=value_layers,
-        checkpoint=os.path.join(LOG_DIR, CHECKPOINT))
+    agent = simple_ppo_agent.SimplePPOPolicy(sess,
+                                             env,
+                                             network,
+                                             policy_layers=policy_layers,
+                                             value_layers=value_layers,
+                                             checkpoint=os.path.join(LOG_DIR, CHECKPOINT))
 
     sum_reward = 0
     observation = env.reset()
@@ -48,4 +47,3 @@ def main(argv):
 
 if __name__ == "__main__":
   tf.app.run(main)
-
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/motor.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/motor.py
index 20e271e1b..dc852b02b 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/motor.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/motor.py
@@ -8,8 +8,7 @@ MOTOR_VOLTAGE = 16.0
 MOTOR_RESISTANCE = 0.186
 MOTOR_TORQUE_CONSTANT = 0.0954
 MOTOR_VISCOUS_DAMPING = 0
-MOTOR_SPEED_LIMIT = MOTOR_VOLTAGE / (
-    MOTOR_VISCOUS_DAMPING + MOTOR_TORQUE_CONSTANT)
+MOTOR_SPEED_LIMIT = MOTOR_VOLTAGE / (MOTOR_VISCOUS_DAMPING + MOTOR_TORQUE_CONSTANT)
 NUM_MOTORS = 8
 
 
@@ -124,21 +123,19 @@ class MotorModel(object):
       observed_torque: The torque observed by the sensor.
     """
     observed_torque = np.clip(
-        self._torque_constant *
-        (np.asarray(pwm) * self._voltage / self._resistance),
+        self._torque_constant * (np.asarray(pwm) * self._voltage / self._resistance),
         -OBSERVED_TORQUE_LIMIT, OBSERVED_TORQUE_LIMIT)
 
     # Net voltage is clipped at 50V by diodes on the motor controller.
     voltage_net = np.clip(
         np.asarray(pwm) * self._voltage -
-        (self._torque_constant + self._viscous_damping) *
-        np.asarray(true_motor_velocity), -VOLTAGE_CLIPPING, VOLTAGE_CLIPPING)
+        (self._torque_constant + self._viscous_damping) * np.asarray(true_motor_velocity),
+        -VOLTAGE_CLIPPING, VOLTAGE_CLIPPING)
     current = voltage_net / self._resistance
     current_sign = np.sign(current)
     current_magnitude = np.absolute(current)
     # Saturate torque based on empirical current relation.
-    actual_torque = np.interp(current_magnitude, self._current_table,
-                              self._torque_table)
+    actual_torque = np.interp(current_magnitude, self._current_table, self._torque_table)
     actual_torque = np.multiply(current_sign, actual_torque)
     actual_torque = np.multiply(self._strength_ratios, actual_torque)
     return actual_torque, observed_torque
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent.py
index e3c4b4ed3..85e35ad52 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent.py
@@ -4,7 +4,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-
 import tensorflow as tf
 from pybullet_envs.agents.ppo import normalize
 from pybullet_envs.agents import utility
@@ -20,29 +19,25 @@ class SimplePPOPolicy(object):
   https://cs.corp.google.com/piper///depot/google3/robotics/reinforcement_learning/agents/scripts/visualize.py.
   """
 
-  def __init__(self, sess, env, network, policy_layers, value_layers,
-               checkpoint):
+  def __init__(self, sess, env, network, policy_layers, value_layers, checkpoint):
     self.env = env
     self.sess = sess
     observation_size = len(env.observation_space.low)
     action_size = len(env.action_space.low)
-    self.observation_placeholder = tf.placeholder(
-        tf.float32, [None, observation_size], name="Input")
-    self._observ_filter = normalize.StreamingNormalize(
-        self.observation_placeholder[0],
-        center=True,
-        scale=True,
-        clip=5,
-        name="normalize_observ")
-    self._restore_policy(
-        network,
-        policy_layers=policy_layers,
-        value_layers=value_layers,
-        action_size=action_size,
-        checkpoint=checkpoint)
+    self.observation_placeholder = tf.placeholder(tf.float32, [None, observation_size],
+                                                  name="Input")
+    self._observ_filter = normalize.StreamingNormalize(self.observation_placeholder[0],
+                                                       center=True,
+                                                       scale=True,
+                                                       clip=5,
+                                                       name="normalize_observ")
+    self._restore_policy(network,
+                         policy_layers=policy_layers,
+                         value_layers=value_layers,
+                         action_size=action_size,
+                         checkpoint=checkpoint)
 
-  def _restore_policy(self, network, policy_layers, value_layers, action_size,
-                      checkpoint):
+  def _restore_policy(self, network, policy_layers, value_layers, action_size, checkpoint):
     """Restore the PPO policy from a TensorFlow checkpoint.
 
     Args:
@@ -56,24 +51,21 @@ class SimplePPOPolicy(object):
     """
     observ = self._observ_filter.transform(self.observation_placeholder)
     with tf.variable_scope("network/rnn"):
-      self.network = network(
-          policy_layers=policy_layers,
-          value_layers=value_layers,
-          action_size=action_size)
+      self.network = network(policy_layers=policy_layers,
+                             value_layers=value_layers,
+                             action_size=action_size)
 
     with tf.variable_scope("temporary"):
-      self.last_state = tf.Variable(
-          self.network.zero_state(1, tf.float32), False)
+      self.last_state = tf.Variable(self.network.zero_state(1, tf.float32), False)
       self.sess.run(self.last_state.initializer)
 
     with tf.variable_scope("network"):
-      (mean_action, _, _), new_state = tf.nn.dynamic_rnn(
-          self.network,
-          observ[:, None],
-          tf.ones(1),
-          self.last_state,
-          tf.float32,
-          swap_memory=True)
+      (mean_action, _, _), new_state = tf.nn.dynamic_rnn(self.network,
+                                                         observ[:, None],
+                                                         tf.ones(1),
+                                                         self.last_state,
+                                                         tf.float32,
+                                                         swap_memory=True)
       self.mean_action = mean_action
       self.update_state = self.last_state.assign(new_state)
 
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent_example.py
index 0d6f6f807..08808f98d 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent_example.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/simple_ppo_agent_example.py
@@ -29,8 +29,7 @@ from pybullet_envs.minitaur.agents import simple_ppo_agent
 
 flags = tf.app.flags
 FLAGS = tf.app.flags.FLAGS
-flags.DEFINE_string("logdir", None,
-                    "The directory that contains checkpoint and config.")
+flags.DEFINE_string("logdir", None, "The directory that contains checkpoint and config.")
 flags.DEFINE_string("checkpoint", None, "The checkpoint file path.")
 flags.DEFINE_string("log_path", None, "The output path to write log.")
 
@@ -44,13 +43,13 @@ def main(argv):
   network = config.network
 
   with tf.Session() as sess:
-    agent = simple_ppo_agent.SimplePPOPolicy(
-        sess,
-        env,
-        network,
-        policy_layers=policy_layers,
-        value_layers=value_layers,
-        checkpoint=os.path.join(FLAGS.logdir, FLAGS.checkpoint))
+    agent = simple_ppo_agent.SimplePPOPolicy(sess,
+                                             env,
+                                             network,
+                                             policy_layers=policy_layers,
+                                             value_layers=value_layers,
+                                             checkpoint=os.path.join(FLAGS.logdir,
+                                                                     FLAGS.checkpoint))
 
     sum_reward = 0
     observation = env.reset()
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/timestamp_pb2.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/timestamp_pb2.py
index 25e3a230d..bd1bf7703 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/timestamp_pb2.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/timestamp_pb2.py
@@ -2,7 +2,7 @@
 # source: timestamp.proto
 
 import sys
-_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode('latin1'))
 from google.protobuf import descriptor as _descriptor
 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
@@ -12,65 +12,76 @@ from google.protobuf import descriptor_pb2
 
 _sym_db = _symbol_database.Default()
 
-
-
-
 DESCRIPTOR = _descriptor.FileDescriptor(
-  name='timestamp.proto',
-  package='google.protobuf',
-  syntax='proto3',
-  serialized_pb=_b('\n\x0ftimestamp.proto\x12\x0fgoogle.protobuf\"+\n\tTimestamp\x12\x0f\n\x07seconds\x18\x01 \x01(\x03\x12\r\n\x05nanos\x18\x02 \x01(\x05\x62\x06proto3')
-)
-
-
-
+    name='timestamp.proto',
+    package='google.protobuf',
+    syntax='proto3',
+    serialized_pb=_b(
+        '\n\x0ftimestamp.proto\x12\x0fgoogle.protobuf\"+\n\tTimestamp\x12\x0f\n\x07seconds\x18\x01 \x01(\x03\x12\r\n\x05nanos\x18\x02 \x01(\x05\x62\x06proto3'
+    ))
 
 _TIMESTAMP = _descriptor.Descriptor(
-  name='Timestamp',
-  full_name='google.protobuf.Timestamp',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='seconds', full_name='google.protobuf.Timestamp.seconds', index=0,
-      number=1, type=3, cpp_type=2, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='nanos', full_name='google.protobuf.Timestamp.nanos', index=1,
-      number=2, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=36,
-  serialized_end=79,
+    name='Timestamp',
+    full_name='google.protobuf.Timestamp',
+    filename=None,
+    file=DESCRIPTOR,
+    containing_type=None,
+    fields=[
+        _descriptor.FieldDescriptor(name='seconds',
+                                    full_name='google.protobuf.Timestamp.seconds',
+                                    index=0,
+                                    number=1,
+                                    type=3,
+                                    cpp_type=2,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=0,
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(name='nanos',
+                                    full_name='google.protobuf.Timestamp.nanos',
+                                    index=1,
+                                    number=2,
+                                    type=5,
+                                    cpp_type=1,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=0,
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+    ],
+    extensions=[],
+    nested_types=[],
+    enum_types=[],
+    options=None,
+    is_extendable=False,
+    syntax='proto3',
+    extension_ranges=[],
+    oneofs=[],
+    serialized_start=36,
+    serialized_end=79,
 )
 
 DESCRIPTOR.message_types_by_name['Timestamp'] = _TIMESTAMP
 _sym_db.RegisterFileDescriptor(DESCRIPTOR)
 
-Timestamp = _reflection.GeneratedProtocolMessageType('Timestamp', (_message.Message,), dict(
-  DESCRIPTOR = _TIMESTAMP,
-  __module__ = 'timestamp_pb2'
-  # @@protoc_insertion_point(class_scope:google.protobuf.Timestamp)
-  ))
+Timestamp = _reflection.GeneratedProtocolMessageType(
+    'Timestamp',
+    (_message.Message,),
+    dict(DESCRIPTOR=_TIMESTAMP,
+         __module__='timestamp_pb2'
+         # @@protoc_insertion_point(class_scope:google.protobuf.Timestamp)
+        ))
 _sym_db.RegisterMessage(Timestamp)
 
-
 # @@protoc_insertion_point(module_scope)
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/envs/vector_pb2.py b/examples/pybullet/gym/pybullet_envs/minitaur/envs/vector_pb2.py
index 67bacb9cd..400c6c805 100644
--- a/examples/pybullet/gym/pybullet_envs/minitaur/envs/vector_pb2.py
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/envs/vector_pb2.py
@@ -2,7 +2,7 @@
 # source: vector.proto
 
 import sys
-_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode('latin1'))
 from google.protobuf import descriptor as _descriptor
 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
@@ -12,348 +12,494 @@ from google.protobuf import descriptor_pb2
 
 _sym_db = _symbol_database.Default()
 
-
-
-
 DESCRIPTOR = _descriptor.FileDescriptor(
-  name='vector.proto',
-  package='robotics.messages',
-  syntax='proto3',
-  serialized_pb=_b('\n\x0cvector.proto\x12\x11robotics.messages\"6\n\x08Vector4d\x12\t\n\x01x\x18\x01 \x01(\x01\x12\t\n\x01y\x18\x02 \x01(\x01\x12\t\n\x01z\x18\x03 \x01(\x01\x12\t\n\x01w\x18\x04 \x01(\x01\"6\n\x08Vector4f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\x12\t\n\x01z\x18\x03 \x01(\x02\x12\t\n\x01w\x18\x04 \x01(\x02\"+\n\x08Vector3d\x12\t\n\x01x\x18\x01 \x01(\x01\x12\t\n\x01y\x18\x02 \x01(\x01\x12\t\n\x01z\x18\x03 \x01(\x01\"+\n\x08Vector3f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\x12\t\n\x01z\x18\x03 \x01(\x02\" \n\x08Vector2d\x12\t\n\x01x\x18\x01 \x01(\x01\x12\t\n\x01y\x18\x02 \x01(\x01\" \n\x08Vector2f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\"\x1b\n\x07Vectord\x12\x10\n\x04\x64\x61ta\x18\x01 \x03(\x01\x42\x02\x10\x01\"\x1b\n\x07Vectorf\x12\x10\n\x04\x64\x61ta\x18\x01 \x03(\x02\x42\x02\x10\x01\x62\x06proto3')
-)
-
-
-
+    name='vector.proto',
+    package='robotics.messages',
+    syntax='proto3',
+    serialized_pb=_b(
+        '\n\x0cvector.proto\x12\x11robotics.messages\"6\n\x08Vector4d\x12\t\n\x01x\x18\x01 \x01(\x01\x12\t\n\x01y\x18\x02 \x01(\x01\x12\t\n\x01z\x18\x03 \x01(\x01\x12\t\n\x01w\x18\x04 \x01(\x01\"6\n\x08Vector4f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\x12\t\n\x01z\x18\x03 \x01(\x02\x12\t\n\x01w\x18\x04 \x01(\x02\"+\n\x08Vector3d\x12\t\n\x01x\x18\x01 \x01(\x01\x12\t\n\x01y\x18\x02 \x01(\x01\x12\t\n\x01z\x18\x03 \x01(\x01\"+\n\x08Vector3f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\x12\t\n\x01z\x18\x03 \x01(\x02\" \n\x08Vector2d\x12\t\n\x01x\x18\x01 \x01(\x01\x12\t\n\x01y\x18\x02 \x01(\x01\" \n\x08Vector2f\x12\t\n\x01x\x18\x01 \x01(\x02\x12\t\n\x01y\x18\x02 \x01(\x02\"\x1b\n\x07Vectord\x12\x10\n\x04\x64\x61ta\x18\x01 \x03(\x01\x42\x02\x10\x01\"\x1b\n\x07Vectorf\x12\x10\n\x04\x64\x61ta\x18\x01 \x03(\x02\x42\x02\x10\x01\x62\x06proto3'
+    ))
 
 _VECTOR4D = _descriptor.Descriptor(
-  name='Vector4d',
-  full_name='robotics.messages.Vector4d',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='x', full_name='robotics.messages.Vector4d.x', index=0,
-      number=1, type=1, cpp_type=5, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='y', full_name='robotics.messages.Vector4d.y', index=1,
-      number=2, type=1, cpp_type=5, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='z', full_name='robotics.messages.Vector4d.z', index=2,
-      number=3, type=1, cpp_type=5, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='w', full_name='robotics.messages.Vector4d.w', index=3,
-      number=4, type=1, cpp_type=5, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=35,
-  serialized_end=89,
+    name='Vector4d',
+    full_name='robotics.messages.Vector4d',
+    filename=None,
+    file=DESCRIPTOR,
+    containing_type=None,
+    fields=[
+        _descriptor.FieldDescriptor(name='x',
+                                    full_name='robotics.messages.Vector4d.x',
+                                    index=0,
+                                    number=1,
+                                    type=1,
+                                    cpp_type=5,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(name='y',
+                                    full_name='robotics.messages.Vector4d.y',
+                                    index=1,
+                                    number=2,
+                                    type=1,
+                                    cpp_type=5,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(name='z',
+                                    full_name='robotics.messages.Vector4d.z',
+                                    index=2,
+                                    number=3,
+                                    type=1,
+                                    cpp_type=5,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(name='w',
+                                    full_name='robotics.messages.Vector4d.w',
+                                    index=3,
+                                    number=4,
+                                    type=1,
+                                    cpp_type=5,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+    ],
+    extensions=[],
+    nested_types=[],
+    enum_types=[],
+    options=None,
+    is_extendable=False,
+    syntax='proto3',
+    extension_ranges=[],
+    oneofs=[],
+    serialized_start=35,
+    serialized_end=89,
 )
 
-
 _VECTOR4F = _descriptor.Descriptor(
-  name='Vector4f',
-  full_name='robotics.messages.Vector4f',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='x', full_name='robotics.messages.Vector4f.x', index=0,
-      number=1, type=2, cpp_type=6, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='y', full_name='robotics.messages.Vector4f.y', index=1,
-      number=2, type=2, cpp_type=6, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='z', full_name='robotics.messages.Vector4f.z', index=2,
-      number=3, type=2, cpp_type=6, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='w', full_name='robotics.messages.Vector4f.w', index=3,
-      number=4, type=2, cpp_type=6, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=91,
-  serialized_end=145,
+    name='Vector4f',
+    full_name='robotics.messages.Vector4f',
+    filename=None,
+    file=DESCRIPTOR,
+    containing_type=None,
+    fields=[
+        _descriptor.FieldDescriptor(name='x',
+                                    full_name='robotics.messages.Vector4f.x',
+                                    index=0,
+                                    number=1,
+                                    type=2,
+                                    cpp_type=6,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(name='y',
+                                    full_name='robotics.messages.Vector4f.y',
+                                    index=1,
+                                    number=2,
+                                    type=2,
+                                    cpp_type=6,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(name='z',
+                                    full_name='robotics.messages.Vector4f.z',
+                                    index=2,
+                                    number=3,
+                                    type=2,
+                                    cpp_type=6,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(name='w',
+                                    full_name='robotics.messages.Vector4f.w',
+                                    index=3,
+                                    number=4,
+                                    type=2,
+                                    cpp_type=6,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+    ],
+    extensions=[],
+    nested_types=[],
+    enum_types=[],
+    options=None,
+    is_extendable=False,
+    syntax='proto3',
+    extension_ranges=[],
+    oneofs=[],
+    serialized_start=91,
+    serialized_end=145,
 )
 
-
 _VECTOR3D = _descriptor.Descriptor(
-  name='Vector3d',
-  full_name='robotics.messages.Vector3d',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='x', full_name='robotics.messages.Vector3d.x', index=0,
-      number=1, type=1, cpp_type=5, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='y', full_name='robotics.messages.Vector3d.y', index=1,
-      number=2, type=1, cpp_type=5, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='z', full_name='robotics.messages.Vector3d.z', index=2,
-      number=3, type=1, cpp_type=5, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=147,
-  serialized_end=190,
+    name='Vector3d',
+    full_name='robotics.messages.Vector3d',
+    filename=None,
+    file=DESCRIPTOR,
+    containing_type=None,
+    fields=[
+        _descriptor.FieldDescriptor(name='x',
+                                    full_name='robotics.messages.Vector3d.x',
+                                    index=0,
+                                    number=1,
+                                    type=1,
+                                    cpp_type=5,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(name='y',
+                                    full_name='robotics.messages.Vector3d.y',
+                                    index=1,
+                                    number=2,
+                                    type=1,
+                                    cpp_type=5,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(name='z',
+                                    full_name='robotics.messages.Vector3d.z',
+                                    index=2,
+                                    number=3,
+                                    type=1,
+                                    cpp_type=5,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+    ],
+    extensions=[],
+    nested_types=[],
+    enum_types=[],
+    options=None,
+    is_extendable=False,
+    syntax='proto3',
+    extension_ranges=[],
+    oneofs=[],
+    serialized_start=147,
+    serialized_end=190,
 )
 
-
 _VECTOR3F = _descriptor.Descriptor(
-  name='Vector3f',
-  full_name='robotics.messages.Vector3f',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='x', full_name='robotics.messages.Vector3f.x', index=0,
-      number=1, type=2, cpp_type=6, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='y', full_name='robotics.messages.Vector3f.y', index=1,
-      number=2, type=2, cpp_type=6, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='z', full_name='robotics.messages.Vector3f.z', index=2,
-      number=3, type=2, cpp_type=6, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=192,
-  serialized_end=235,
+    name='Vector3f',
+    full_name='robotics.messages.Vector3f',
+    filename=None,
+    file=DESCRIPTOR,
+    containing_type=None,
+    fields=[
+        _descriptor.FieldDescriptor(name='x',
+                                    full_name='robotics.messages.Vector3f.x',
+                                    index=0,
+                                    number=1,
+                                    type=2,
+                                    cpp_type=6,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(name='y',
+                                    full_name='robotics.messages.Vector3f.y',
+                                    index=1,
+                                    number=2,
+                                    type=2,
+                                    cpp_type=6,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(name='z',
+                                    full_name='robotics.messages.Vector3f.z',
+                                    index=2,
+                                    number=3,
+                                    type=2,
+                                    cpp_type=6,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+    ],
+    extensions=[],
+    nested_types=[],
+    enum_types=[],
+    options=None,
+    is_extendable=False,
+    syntax='proto3',
+    extension_ranges=[],
+    oneofs=[],
+    serialized_start=192,
+    serialized_end=235,
 )
 
-
 _VECTOR2D = _descriptor.Descriptor(
-  name='Vector2d',
-  full_name='robotics.messages.Vector2d',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='x', full_name='robotics.messages.Vector2d.x', index=0,
-      number=1, type=1, cpp_type=5, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='y', full_name='robotics.messages.Vector2d.y', index=1,
-      number=2, type=1, cpp_type=5, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=237,
-  serialized_end=269,
+    name='Vector2d',
+    full_name='robotics.messages.Vector2d',
+    filename=None,
+    file=DESCRIPTOR,
+    containing_type=None,
+    fields=[
+        _descriptor.FieldDescriptor(name='x',
+                                    full_name='robotics.messages.Vector2d.x',
+                                    index=0,
+                                    number=1,
+                                    type=1,
+                                    cpp_type=5,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(name='y',
+                                    full_name='robotics.messages.Vector2d.y',
+                                    index=1,
+                                    number=2,
+                                    type=1,
+                                    cpp_type=5,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+    ],
+    extensions=[],
+    nested_types=[],
+    enum_types=[],
+    options=None,
+    is_extendable=False,
+    syntax='proto3',
+    extension_ranges=[],
+    oneofs=[],
+    serialized_start=237,
+    serialized_end=269,
 )
 
-
 _VECTOR2F = _descriptor.Descriptor(
-  name='Vector2f',
-  full_name='robotics.messages.Vector2f',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='x', full_name='robotics.messages.Vector2f.x', index=0,
-      number=1, type=2, cpp_type=6, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='y', full_name='robotics.messages.Vector2f.y', index=1,
-      number=2, type=2, cpp_type=6, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=271,
-  serialized_end=303,
+    name='Vector2f',
+    full_name='robotics.messages.Vector2f',
+    filename=None,
+    file=DESCRIPTOR,
+    containing_type=None,
+    fields=[
+        _descriptor.FieldDescriptor(name='x',
+                                    full_name='robotics.messages.Vector2f.x',
+                                    index=0,
+                                    number=1,
+                                    type=2,
+                                    cpp_type=6,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+        _descriptor.FieldDescriptor(name='y',
+                                    full_name='robotics.messages.Vector2f.y',
+                                    index=1,
+                                    number=2,
+                                    type=2,
+                                    cpp_type=6,
+                                    label=1,
+                                    has_default_value=False,
+                                    default_value=float(0),
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=None,
+                                    file=DESCRIPTOR),
+    ],
+    extensions=[],
+    nested_types=[],
+    enum_types=[],
+    options=None,
+    is_extendable=False,
+    syntax='proto3',
+    extension_ranges=[],
+    oneofs=[],
+    serialized_start=271,
+    serialized_end=303,
 )
 
-
 _VECTORD = _descriptor.Descriptor(
-  name='Vectord',
-  full_name='robotics.messages.Vectord',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='data', full_name='robotics.messages.Vectord.data', index=0,
-      number=1, type=1, cpp_type=5, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')), file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=305,
-  serialized_end=332,
+    name='Vectord',
+    full_name='robotics.messages.Vectord',
+    filename=None,
+    file=DESCRIPTOR,
+    containing_type=None,
+    fields=[
+        _descriptor.FieldDescriptor(name='data',
+                                    full_name='robotics.messages.Vectord.data',
+                                    index=0,
+                                    number=1,
+                                    type=1,
+                                    cpp_type=5,
+                                    label=3,
+                                    has_default_value=False,
+                                    default_value=[],
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=_descriptor._ParseOptions(
+                                        descriptor_pb2.FieldOptions(), _b('\020\001')),
+                                    file=DESCRIPTOR),
+    ],
+    extensions=[],
+    nested_types=[],
+    enum_types=[],
+    options=None,
+    is_extendable=False,
+    syntax='proto3',
+    extension_ranges=[],
+    oneofs=[],
+    serialized_start=305,
+    serialized_end=332,
 )
 
-
 _VECTORF = _descriptor.Descriptor(
-  name='Vectorf',
-  full_name='robotics.messages.Vectorf',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='data', full_name='robotics.messages.Vectorf.data', index=0,
-      number=1, type=2, cpp_type=6, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')), file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=334,
-  serialized_end=361,
+    name='Vectorf',
+    full_name='robotics.messages.Vectorf',
+    filename=None,
+    file=DESCRIPTOR,
+    containing_type=None,
+    fields=[
+        _descriptor.FieldDescriptor(name='data',
+                                    full_name='robotics.messages.Vectorf.data',
+                                    index=0,
+                                    number=1,
+                                    type=2,
+                                    cpp_type=6,
+                                    label=3,
+                                    has_default_value=False,
+                                    default_value=[],
+                                    message_type=None,
+                                    enum_type=None,
+                                    containing_type=None,
+                                    is_extension=False,
+                                    extension_scope=None,
+                                    options=_descriptor._ParseOptions(
+                                        descriptor_pb2.FieldOptions(), _b('\020\001')),
+                                    file=DESCRIPTOR),
+    ],
+    extensions=[],
+    nested_types=[],
+    enum_types=[],
+    options=None,
+    is_extendable=False,
+    syntax='proto3',
+    extension_ranges=[],
+    oneofs=[],
+    serialized_start=334,
+    serialized_end=361,
 )
 
 DESCRIPTOR.message_types_by_name['Vector4d'] = _VECTOR4D
@@ -366,65 +512,82 @@ DESCRIPTOR.message_types_by_name['Vectord'] = _VECTORD
 DESCRIPTOR.message_types_by_name['Vectorf'] = _VECTORF
 _sym_db.RegisterFileDescriptor(DESCRIPTOR)
 
-Vector4d = _reflection.GeneratedProtocolMessageType('Vector4d', (_message.Message,), dict(
-  DESCRIPTOR = _VECTOR4D,
-  __module__ = 'vector_pb2'
-  # @@protoc_insertion_point(class_scope:robotics.messages.Vector4d)
-  ))
+Vector4d = _reflection.GeneratedProtocolMessageType(
+    'Vector4d',
+    (_message.Message,),
+    dict(DESCRIPTOR=_VECTOR4D,
+         __module__='vector_pb2'
+         # @@protoc_insertion_point(class_scope:robotics.messages.Vector4d)
+        ))
 _sym_db.RegisterMessage(Vector4d)
 
-Vector4f = _reflection.GeneratedProtocolMessageType('Vector4f', (_message.Message,), dict(
-  DESCRIPTOR = _VECTOR4F,
-  __module__ = 'vector_pb2'
-  # @@protoc_insertion_point(class_scope:robotics.messages.Vector4f)
-  ))
+Vector4f = _reflection.GeneratedProtocolMessageType(
+    'Vector4f',
+    (_message.Message,),
+    dict(DESCRIPTOR=_VECTOR4F,
+         __module__='vector_pb2'
+         # @@protoc_insertion_point(class_scope:robotics.messages.Vector4f)
+        ))
 _sym_db.RegisterMessage(Vector4f)
 
-Vector3d = _reflection.GeneratedProtocolMessageType('Vector3d', (_message.Message,), dict(
-  DESCRIPTOR = _VECTOR3D,
-  __module__ = 'vector_pb2'
-  # @@protoc_insertion_point(class_scope:robotics.messages.Vector3d)
-  ))
+Vector3d = _reflection.GeneratedProtocolMessageType(
+    'Vector3d',
+    (_message.Message,),
+    dict(DESCRIPTOR=_VECTOR3D,
+         __module__='vector_pb2'
+         # @@protoc_insertion_point(class_scope:robotics.messages.Vector3d)
+        ))
 _sym_db.RegisterMessage(Vector3d)
 
-Vector3f = _reflection.GeneratedProtocolMessageType('Vector3f', (_message.Message,), dict(
-  DESCRIPTOR = _VECTOR3F,
-  __module__ = 'vector_pb2'
-  # @@protoc_insertion_point(class_scope:robotics.messages.Vector3f)
-  ))
+Vector3f = _reflection.GeneratedProtocolMessageType(
+    'Vector3f',
+    (_message.Message,),
+    dict(DESCRIPTOR=_VECTOR3F,
+         __module__='vector_pb2'
+         # @@protoc_insertion_point(class_scope:robotics.messages.Vector3f)
+        ))
 _sym_db.RegisterMessage(Vector3f)
 
-Vector2d = _reflection.GeneratedProtocolMessageType('Vector2d', (_message.Message,), dict(
-  DESCRIPTOR = _VECTOR2D,
-  __module__ = 'vector_pb2'
-  # @@protoc_insertion_point(class_scope:robotics.messages.Vector2d)
-  ))
+Vector2d = _reflection.GeneratedProtocolMessageType(
+    'Vector2d',
+    (_message.Message,),
+    dict(DESCRIPTOR=_VECTOR2D,
+         __module__='vector_pb2'
+         # @@protoc_insertion_point(class_scope:robotics.messages.Vector2d)
+        ))
 _sym_db.RegisterMessage(Vector2d)
 
-Vector2f = _reflection.GeneratedProtocolMessageType('Vector2f', (_message.Message,), dict(
-  DESCRIPTOR = _VECTOR2F,
-  __module__ = 'vector_pb2'
-  # @@protoc_insertion_point(class_scope:robotics.messages.Vector2f)
-  ))
+Vector2f = _reflection.GeneratedProtocolMessageType(
+    'Vector2f',
+    (_message.Message,),
+    dict(DESCRIPTOR=_VECTOR2F,
+         __module__='vector_pb2'
+         # @@protoc_insertion_point(class_scope:robotics.messages.Vector2f)
+        ))
 _sym_db.RegisterMessage(Vector2f)
 
-Vectord = _reflection.GeneratedProtocolMessageType('Vectord', (_message.Message,), dict(
-  DESCRIPTOR = _VECTORD,
-  __module__ = 'vector_pb2'
-  # @@protoc_insertion_point(class_scope:robotics.messages.Vectord)
-  ))
+Vectord = _reflection.GeneratedProtocolMessageType(
+    'Vectord',
+    (_message.Message,),
+    dict(DESCRIPTOR=_VECTORD,
+         __module__='vector_pb2'
+         # @@protoc_insertion_point(class_scope:robotics.messages.Vectord)
+        ))
 _sym_db.RegisterMessage(Vectord)
 
-Vectorf = _reflection.GeneratedProtocolMessageType('Vectorf', (_message.Message,), dict(
-  DESCRIPTOR = _VECTORF,
-  __module__ = 'vector_pb2'
-  # @@protoc_insertion_point(class_scope:robotics.messages.Vectorf)
-  ))
+Vectorf = _reflection.GeneratedProtocolMessageType(
+    'Vectorf',
+    (_message.Message,),
+    dict(DESCRIPTOR=_VECTORF,
+         __module__='vector_pb2'
+         # @@protoc_insertion_point(class_scope:robotics.messages.Vectorf)
+        ))
 _sym_db.RegisterMessage(Vectorf)
 
-
 _VECTORD.fields_by_name['data'].has_options = True
-_VECTORD.fields_by_name['data']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001'))
+_VECTORD.fields_by_name['data']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(),
+                                                                     _b('\020\001'))
 _VECTORF.fields_by_name['data'].has_options = True
-_VECTORF.fields_by_name['data']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001'))
+_VECTORF.fields_by_name['data']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(),
+                                                                     _b('\020\001'))
 # @@protoc_insertion_point(module_scope)
diff --git a/examples/pybullet/gym/pybullet_envs/prediction/__init__.py b/examples/pybullet/gym/pybullet_envs/prediction/__init__.py
index 26f76e102..a575195ec 100644
--- a/examples/pybullet/gym/pybullet_envs/prediction/__init__.py
+++ b/examples/pybullet/gym/pybullet_envs/prediction/__init__.py
@@ -1,3 +1,2 @@
 from . import boxstack_pybullet_sim
 from . import pybullet_sim_gym_env
-
diff --git a/examples/pybullet/gym/pybullet_envs/prediction/boxstack_pybullet_sim.py b/examples/pybullet/gym/pybullet_envs/prediction/boxstack_pybullet_sim.py
index 42bc3cffa..85e29cc60 100644
--- a/examples/pybullet/gym/pybullet_envs/prediction/boxstack_pybullet_sim.py
+++ b/examples/pybullet/gym/pybullet_envs/prediction/boxstack_pybullet_sim.py
@@ -14,10 +14,7 @@ class BoxStackPyBulletSim(object):
 
   """
 
-  def __init__(self,
-               pybullet_client,
-               urdf_root= pybullet_data.getDataPath(),
-               time_step=0.01):
+  def __init__(self, pybullet_client, urdf_root=pybullet_data.getDataPath(), time_step=0.01):
     """Constructs an example simulation and reset it to the initial states.
 
     Args:
@@ -28,11 +25,10 @@ class BoxStackPyBulletSim(object):
     """
     self._pybullet_client = pybullet_client
     self._urdf_root = urdf_root
-    self.m_actions_taken_since_reset=0
+    self.m_actions_taken_since_reset = 0
     self.time_step = time_step
     self.stateId = -1
     self.Reset(reload_urdf=True)
-    
 
   def Reset(self, reload_urdf=False):
     """Reset the minitaur to its initial states.
@@ -41,58 +37,59 @@ class BoxStackPyBulletSim(object):
       reload_urdf: Whether to reload the urdf file. If not, Reset() just place
         the minitaur back to its starting position.
     """
-    self.m_actions_taken_since_reset=0
-    xPosRange=0.025
-    yPosRange=0.025
+    self.m_actions_taken_since_reset = 0
+    xPosRange = 0.025
+    yPosRange = 0.025
     boxHalfExtents = 0.025
-    
+
     if reload_urdf:
       camInfo = self._pybullet_client.getDebugVisualizerCamera()
-      cameraDistance=camInfo[10]
-      print("cameraYaw=",camInfo[8])
-      print("cameraPitch=",camInfo[9])
-      print("camtarget=",camInfo[11])
-      print("projectionMatrix=",camInfo[3])
-      self._pybullet_client.resetDebugVisualizerCamera(cameraDistance=0.3, cameraYaw=camInfo[8], cameraPitch=camInfo[9],cameraTargetPosition=camInfo[11])
+      cameraDistance = camInfo[10]
+      print("cameraYaw=", camInfo[8])
+      print("cameraPitch=", camInfo[9])
+      print("camtarget=", camInfo[11])
+      print("projectionMatrix=", camInfo[3])
+      self._pybullet_client.resetDebugVisualizerCamera(cameraDistance=0.3,
+                                                       cameraYaw=camInfo[8],
+                                                       cameraPitch=camInfo[9],
+                                                       cameraTargetPosition=camInfo[11])
 
       plane = self._pybullet_client.loadURDF("plane.urdf")
       texUid = self._pybullet_client.loadTexture("checker_blue.png")
-      self._pybullet_client.changeVisualShape(plane,-1, textureUniqueId = texUid)
-      
-      
-      self._numObjects=4 #random number?
-      
-      
-      self._cubes=[]
-      
-      red=[0.97,0.25,0.25,1]
-      green=[0.41,0.68,0.31,1]
-      yellow=[0.92,0.73,0,1]
-      blue=[0,0.55,0.81,1]
-      colors=[red,green,yellow,blue]
-      
-      for i in range (self._numObjects):
-          pos=[0,0,boxHalfExtents + i*2*boxHalfExtents]
-          orn = self._pybullet_client.getQuaternionFromEuler([0,0,0])
-          orn=[0,0,0,1]
-          cube = self._pybullet_client.loadURDF("cube_small.urdf",pos,orn)
-          self._pybullet_client.changeVisualShape(cube,-1,rgbaColor=colors[i])
-          self._cubes.append(cube)
-    
+      self._pybullet_client.changeVisualShape(plane, -1, textureUniqueId=texUid)
+
+      self._numObjects = 4  #random number?
+
+      self._cubes = []
+
+      red = [0.97, 0.25, 0.25, 1]
+      green = [0.41, 0.68, 0.31, 1]
+      yellow = [0.92, 0.73, 0, 1]
+      blue = [0, 0.55, 0.81, 1]
+      colors = [red, green, yellow, blue]
+
+      for i in range(self._numObjects):
+        pos = [0, 0, boxHalfExtents + i * 2 * boxHalfExtents]
+        orn = self._pybullet_client.getQuaternionFromEuler([0, 0, 0])
+        orn = [0, 0, 0, 1]
+        cube = self._pybullet_client.loadURDF("cube_small.urdf", pos, orn)
+        self._pybullet_client.changeVisualShape(cube, -1, rgbaColor=colors[i])
+        self._cubes.append(cube)
+
       self._pybullet_client.setGravity(0, 0, -10)
       self.stateId = self._pybullet_client.saveState()
     else:
-      if (self.stateId>=0):
+      if (self.stateId >= 0):
         self._pybullet_client.restoreState(self.stateId)
-    index=0
+    index = 0
     for i in self._cubes:
-      posX = random.uniform(-xPosRange,xPosRange)
-      posY = random.uniform(-yPosRange,yPosRange)
-      yaw = random.uniform(-math.pi,math.pi)
-      pos=[posX,posY,boxHalfExtents + index*2*boxHalfExtents]
-      index+=1
-      orn = self._pybullet_client.getQuaternionFromEuler([0,0,yaw])
-      self._pybullet_client.resetBasePositionAndOrientation(i,pos,orn)
+      posX = random.uniform(-xPosRange, xPosRange)
+      posY = random.uniform(-yPosRange, yPosRange)
+      yaw = random.uniform(-math.pi, math.pi)
+      pos = [posX, posY, boxHalfExtents + index * 2 * boxHalfExtents]
+      index += 1
+      orn = self._pybullet_client.getQuaternionFromEuler([0, 0, yaw])
+      self._pybullet_client.resetBasePositionAndOrientation(i, pos, orn)
 
   def GetActionDimension(self):
     """Get the length of the action list.
@@ -100,7 +97,7 @@ class BoxStackPyBulletSim(object):
     Returns:
       The length of the action list.
     """
-    return 4#self.num_motors
+    return 4  #self.num_motors
 
   def GetObservationUpperBound(self):
     """Get the upper bound of the observation.
@@ -124,7 +121,7 @@ class BoxStackPyBulletSim(object):
       The length of the observation list.
     """
     sz = len(self.GetObservation())
-    print("sz=",sz)
+    print("sz=", sz)
     return sz
 
   def GetObservation(self):
@@ -135,7 +132,7 @@ class BoxStackPyBulletSim(object):
     """
     observation = []
     for i in self._cubes:
-      pos,orn=self._pybullet_client.getBasePositionAndOrientation(i)
+      pos, orn = self._pybullet_client.getBasePositionAndOrientation(i)
       observation.extend(list(pos))
       observation.extend(list(orn))
     return observation
@@ -143,14 +140,14 @@ class BoxStackPyBulletSim(object):
   def ApplyAction(self, action):
     """Set the desired action.
     """
-    self.m_actions_taken_since_reset+=1
-    
+    self.m_actions_taken_since_reset += 1
 
   def Termination(self):
-  	return self.m_actions_taken_since_reset>=200
- 
-def CreateSim(pybullet_client,urdf_root,time_step):
+    return self.m_actions_taken_since_reset >= 200
+
+
+def CreateSim(pybullet_client, urdf_root, time_step):
   sim = BoxStackPyBulletSim(pybullet_client=pybullet_client,
-          urdf_root=urdf_root,
-          time_step=time_step)
+                            urdf_root=urdf_root,
+                            time_step=time_step)
   return sim
diff --git a/examples/pybullet/gym/pybullet_envs/prediction/pybullet_sim_gym_env.py b/examples/pybullet/gym/pybullet_envs/prediction/pybullet_sim_gym_env.py
index 2f451529a..1c308a7fb 100644
--- a/examples/pybullet/gym/pybullet_envs/prediction/pybullet_sim_gym_env.py
+++ b/examples/pybullet/gym/pybullet_envs/prediction/pybullet_sim_gym_env.py
@@ -5,8 +5,7 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
-
+os.sys.path.insert(0, parentdir)
 
 import math
 import time
@@ -30,20 +29,18 @@ class PyBulletSimGymEnv(gym.Env):
 
 
   """
-  metadata = {
-      "render.modes": ["human", "rgb_array"],
-      "video.frames_per_second": 50
-  }
+  metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 50}
 
-  def __init__(self,pybullet_sim_factory = boxstack_pybullet_sim,
+  def __init__(self,
+               pybullet_sim_factory=boxstack_pybullet_sim,
                render=True,
                render_sleep=False,
                debug_visualization=True,
-               hard_reset = False,
+               hard_reset=False,
                render_width=240,
                render_height=240,
                action_repeat=1,
-               time_step = 1./240.,
+               time_step=1. / 240.,
                num_bullet_solver_iterations=50,
                urdf_root=pybullet_data.getDataPath()):
     """Initialize the gym environment.
@@ -55,45 +52,46 @@ class PyBulletSimGymEnv(gym.Env):
     self._time_step = time_step
     self._urdf_root = urdf_root
     self._observation = []
-    self._action_repeat=action_repeat
+    self._action_repeat = action_repeat
     self._num_bullet_solver_iterations = num_bullet_solver_iterations
     self._env_step_counter = 0
     self._is_render = render
     self._debug_visualization = debug_visualization
     self._render_sleep = render_sleep
-    self._render_width=render_width
-    self._render_height=render_height
+    self._render_width = render_width
+    self._render_height = render_height
     self._cam_dist = .3
     self._cam_yaw = 50
     self._cam_pitch = -35
     self._hard_reset = True
     self._last_frame_time = 0.0
 
-    optionstring='--width={} --height={}'.format(render_width,render_height)
+    optionstring = '--width={} --height={}'.format(render_width, render_height)
 
     print("urdf_root=" + self._urdf_root)
 
     if self._is_render:
-      self._pybullet_client = bullet_client.BulletClient(
-          connection_mode=pybullet.GUI, options=optionstring)
+      self._pybullet_client = bullet_client.BulletClient(connection_mode=pybullet.GUI,
+                                                         options=optionstring)
     else:
       self._pybullet_client = bullet_client.BulletClient()
 
-    if (debug_visualization==False):
-      self._pybullet_client.configureDebugVisualizer(flag=self._pybullet_client.COV_ENABLE_GUI,enable=0)
-      self._pybullet_client.configureDebugVisualizer(flag=self._pybullet_client.COV_ENABLE_RGB_BUFFER_PREVIEW,enable=0)
-      self._pybullet_client.configureDebugVisualizer(flag=self._pybullet_client.COV_ENABLE_DEPTH_BUFFER_PREVIEW,enable=0)
-      self._pybullet_client.configureDebugVisualizer(flag=self._pybullet_client.COV_ENABLE_SEGMENTATION_MARK_PREVIEW,enable=0)
-
+    if (debug_visualization == False):
+      self._pybullet_client.configureDebugVisualizer(flag=self._pybullet_client.COV_ENABLE_GUI,
+                                                     enable=0)
+      self._pybullet_client.configureDebugVisualizer(
+          flag=self._pybullet_client.COV_ENABLE_RGB_BUFFER_PREVIEW, enable=0)
+      self._pybullet_client.configureDebugVisualizer(
+          flag=self._pybullet_client.COV_ENABLE_DEPTH_BUFFER_PREVIEW, enable=0)
+      self._pybullet_client.configureDebugVisualizer(
+          flag=self._pybullet_client.COV_ENABLE_SEGMENTATION_MARK_PREVIEW, enable=0)
 
     self._pybullet_client.setAdditionalSearchPath(urdf_root)
     self.seed()
     self.reset()
 
-    observation_high = (
-        self._example_sim.GetObservationUpperBound())
-    observation_low = (
-        self._example_sim.GetObservationLowerBound())
+    observation_high = (self._example_sim.GetObservationUpperBound())
+    observation_low = (self._example_sim.GetObservationLowerBound())
 
     action_dim = self._example_sim.GetActionDimension()
     self._action_bound = 1
@@ -104,7 +102,6 @@ class PyBulletSimGymEnv(gym.Env):
     self.viewer = None
     self._hard_reset = hard_reset  # This assignment need to be after reset()
 
-
   def configure(self, args):
     self._args = args
 
@@ -163,7 +160,6 @@ class PyBulletSimGymEnv(gym.Env):
       #self._pybullet_client.resetDebugVisualizerCamera(
       #    self._cam_dist, self._cam_yaw, self._cam_pitch, base_pos)
 
-
     for _ in range(self._action_repeat):
       self._example_sim.ApplyAction(action)
       self._pybullet_client.stepSimulation()
@@ -176,7 +172,7 @@ class PyBulletSimGymEnv(gym.Env):
   def render(self, mode="rgb_array", close=False):
     if mode != "rgb_array":
       return np.array([])
-    base_pos = [0,0,0]
+    base_pos = [0, 0, 0]
     view_matrix = self._pybullet_client.computeViewMatrixFromYawPitchRoll(
         cameraTargetPosition=base_pos,
         distance=self._cam_dist,
@@ -185,19 +181,24 @@ class PyBulletSimGymEnv(gym.Env):
         roll=0,
         upAxisIndex=2)
     proj_matrix = self._pybullet_client.computeProjectionMatrixFOV(
-        fov=60, aspect=float(self._render_width)/self._render_width,
-        nearVal=0.01, farVal=100.0)
-    proj_matrix=[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0, -0.02000020071864128, 0.0]
+        fov=60, aspect=float(self._render_width) / self._render_width, nearVal=0.01, farVal=100.0)
+    proj_matrix = [
+        1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, -1.0000200271606445, -1.0, 0.0, 0.0,
+        -0.02000020071864128, 0.0
+    ]
     (_, _, px, _, _) = self._pybullet_client.getCameraImage(
-        width=self._render_width, height=self._render_height, viewMatrix=view_matrix,
-        projectionMatrix=proj_matrix, renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)#ER_TINY_RENDERER)
+        width=self._render_width,
+        height=self._render_height,
+        viewMatrix=view_matrix,
+        projectionMatrix=proj_matrix,
+        renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)  #ER_TINY_RENDERER)
     rgb_array = np.array(px, dtype=np.uint8)
     rgb_array = np.reshape(rgb_array, (self._render_height, self._render_width, 4))
     rgb_array = rgb_array[:, :, :3]
     return rgb_array
 
   def _termination(self):
-    terminate=self._example_sim.Termination()
+    terminate = self._example_sim.Termination()
     return terminate
 
   def _reward(self):
diff --git a/examples/pybullet/gym/pybullet_envs/prediction/test_pybullet_sim_gym_env.py b/examples/pybullet/gym/pybullet_envs/prediction/test_pybullet_sim_gym_env.py
index 23f734e4e..ae24b3a9d 100644
--- a/examples/pybullet/gym/pybullet_envs/prediction/test_pybullet_sim_gym_env.py
+++ b/examples/pybullet/gym/pybullet_envs/prediction/test_pybullet_sim_gym_env.py
@@ -5,7 +5,7 @@ import os
 import inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import math
 import numpy as np
@@ -16,18 +16,18 @@ import time
 from pybullet_envs.prediction import boxstack_pybullet_sim
 from gym.wrappers.monitoring import video_recorder
 
+
 def ResetPoseExample(steps):
   """An example that the minitaur stands still using the reset pose."""
-  
-  
-  environment = pybullet_sim_gym_env.PyBulletSimGymEnv(
-      pybullet_sim_factory=boxstack_pybullet_sim,
-      debug_visualization=False,
-      render=True, action_repeat=30)
+
+  environment = pybullet_sim_gym_env.PyBulletSimGymEnv(pybullet_sim_factory=boxstack_pybullet_sim,
+                                                       debug_visualization=False,
+                                                       render=True,
+                                                       action_repeat=30)
   action = [math.pi / 2] * 8
-  
-  vid = video_recorder.VideoRecorder(env=environment,path="vid.mp4")
-  
+
+  vid = video_recorder.VideoRecorder(env=environment, path="vid.mp4")
+
   for _ in range(steps):
     print(_)
     startsim = time.time()
@@ -37,24 +37,21 @@ def ResetPoseExample(steps):
     #environment.render(mode='rgb_array')
     vid.capture_frame()
     stoprender = time.time()
-    print ("env.step " , (stopsim - startsim))
-    print ("env.render " , (stoprender - startrender))
+    print("env.step ", (stopsim - startsim))
+    print("env.render ", (stoprender - startrender))
     if done:
-    	environment.reset()
-  
-
+      environment.reset()
 
 
 def main():
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--env', help='environment ID (0==reset)',type=int,  default=0)
-    args = parser.parse_args()
-    print("--env=" + str(args.env))
-      
-    if (args.env == 0):
-      ResetPoseExample(steps = 1000)
+  parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument('--env', help='environment ID (0==reset)', type=int, default=0)
+  args = parser.parse_args()
+  print("--env=" + str(args.env))
 
-if __name__ == '__main__':
-    main()
+  if (args.env == 0):
+    ResetPoseExample(steps=1000)
 
 
+if __name__ == '__main__':
+  main()
diff --git a/examples/pybullet/gym/pybullet_envs/robot_bases.py b/examples/pybullet/gym/pybullet_envs/robot_bases.py
index 36e5b8c76..51c2791c1 100644
--- a/examples/pybullet/gym/pybullet_envs/robot_bases.py
+++ b/examples/pybullet/gym/pybullet_envs/robot_bases.py
@@ -4,339 +4,404 @@ import numpy as np
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(currentdir)
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 import pybullet_data
 
 
 class XmlBasedRobot:
-	"""
+  """
 	Base class for mujoco .xml based agents.
 	"""
 
-	self_collision = True
-	def __init__(self,  robot_name, action_dim, obs_dim, self_collision):
-		self.parts = None
-		self.objects = []
-		self.jdict = None
-		self.ordered_joints = None
-		self.robot_body = None
-
-		high = np.ones([action_dim])
-		self.action_space = gym.spaces.Box(-high, high)
-		high = np.inf * np.ones([obs_dim])
-		self.observation_space = gym.spaces.Box(-high, high)
-
-		#self.model_xml = model_xml
-		self.robot_name = robot_name
-		self.self_collision = self_collision
-
-	def addToScene(self, bullet_client, bodies):
-		self._p = bullet_client
-
-		if self.parts is not None:
-			parts = self.parts
-		else:
-			parts = {}
-
-		if self.jdict is not None:
-			joints = self.jdict
-		else:
-			joints = {}
-
-		if self.ordered_joints is not None:
-			ordered_joints = self.ordered_joints
-		else:
-			ordered_joints = []
-
-		if np.isscalar(bodies):	# streamline the case where bodies is actually just one body
-			bodies = [bodies]
-
-		dump = 0
-		for i in range(len(bodies)):
-			if self._p.getNumJoints(bodies[i]) == 0:
-				part_name, robot_name = self._p.getBodyInfo(bodies[i])
-				self.robot_name = robot_name.decode("utf8")
-				part_name = part_name.decode("utf8")
-				parts[part_name] = BodyPart(self._p, part_name, bodies, i, -1)
-			for j in range(self._p.getNumJoints(bodies[i])):
-				self._p.setJointMotorControl2(bodies[i],j,pybullet.POSITION_CONTROL,positionGain=0.1,velocityGain=0.1,force=0)
-				jointInfo = self._p.getJointInfo(bodies[i], j)
-				joint_name=jointInfo[1]
-				part_name=jointInfo[12]
-
-				joint_name = joint_name.decode("utf8")
-				part_name = part_name.decode("utf8")
-
-				if dump: print("ROBOT PART '%s'" % part_name)
-				if dump: print("ROBOT JOINT '%s'" % joint_name)  # limits = %+0.2f..%+0.2f effort=%0.3f speed=%0.3f" % ((joint_name,) + j.limits()) )
-
-				parts[part_name] = BodyPart(self._p, part_name, bodies, i, j)
-
-				if part_name == self.robot_name:
-					self.robot_body = parts[part_name]
+  self_collision = True
+
+  def __init__(self, robot_name, action_dim, obs_dim, self_collision):
+    self.parts = None
+    self.objects = []
+    self.jdict = None
+    self.ordered_joints = None
+    self.robot_body = None
+
+    high = np.ones([action_dim])
+    self.action_space = gym.spaces.Box(-high, high)
+    high = np.inf * np.ones([obs_dim])
+    self.observation_space = gym.spaces.Box(-high, high)
+
+    #self.model_xml = model_xml
+    self.robot_name = robot_name
+    self.self_collision = self_collision
+
+  def addToScene(self, bullet_client, bodies):
+    self._p = bullet_client
+
+    if self.parts is not None:
+      parts = self.parts
+    else:
+      parts = {}
+
+    if self.jdict is not None:
+      joints = self.jdict
+    else:
+      joints = {}
+
+    if self.ordered_joints is not None:
+      ordered_joints = self.ordered_joints
+    else:
+      ordered_joints = []
+
+    if np.isscalar(bodies):  # streamline the case where bodies is actually just one body
+      bodies = [bodies]
+
+    dump = 0
+    for i in range(len(bodies)):
+      if self._p.getNumJoints(bodies[i]) == 0:
+        part_name, robot_name = self._p.getBodyInfo(bodies[i])
+        self.robot_name = robot_name.decode("utf8")
+        part_name = part_name.decode("utf8")
+        parts[part_name] = BodyPart(self._p, part_name, bodies, i, -1)
+      for j in range(self._p.getNumJoints(bodies[i])):
+        self._p.setJointMotorControl2(bodies[i],
+                                      j,
+                                      pybullet.POSITION_CONTROL,
+                                      positionGain=0.1,
+                                      velocityGain=0.1,
+                                      force=0)
+        jointInfo = self._p.getJointInfo(bodies[i], j)
+        joint_name = jointInfo[1]
+        part_name = jointInfo[12]
+
+        joint_name = joint_name.decode("utf8")
+        part_name = part_name.decode("utf8")
+
+        if dump: print("ROBOT PART '%s'" % part_name)
+        if dump:
+          print(
+              "ROBOT JOINT '%s'" % joint_name
+          )  # limits = %+0.2f..%+0.2f effort=%0.3f speed=%0.3f" % ((joint_name,) + j.limits()) )
+
+        parts[part_name] = BodyPart(self._p, part_name, bodies, i, j)
+
+        if part_name == self.robot_name:
+          self.robot_body = parts[part_name]
+
+        if i == 0 and j == 0 and self.robot_body is None:  # if nothing else works, we take this as robot_body
+          parts[self.robot_name] = BodyPart(self._p, self.robot_name, bodies, 0, -1)
+          self.robot_body = parts[self.robot_name]
+
+        if joint_name[:6] == "ignore":
+          Joint(self._p, joint_name, bodies, i, j).disable_motor()
+          continue
+
+        if joint_name[:8] != "jointfix":
+          joints[joint_name] = Joint(self._p, joint_name, bodies, i, j)
+          ordered_joints.append(joints[joint_name])
+
+          joints[joint_name].power_coef = 100.0
+
+        # TODO: Maybe we need this
+        # joints[joint_name].power_coef, joints[joint_name].max_velocity = joints[joint_name].limits()[2:4]
+        # self.ordered_joints.append(joints[joint_name])
+        # self.jdict[joint_name] = joints[joint_name]
+
+    return parts, joints, ordered_joints, self.robot_body
+
+  def reset_pose(self, position, orientation):
+    self.parts[self.robot_name].reset_pose(position, orientation)
 
-				if i == 0 and j == 0 and self.robot_body is None:  # if nothing else works, we take this as robot_body
-					parts[self.robot_name] = BodyPart(self._p, self.robot_name, bodies, 0, -1)
-					self.robot_body = parts[self.robot_name]
-
-				if joint_name[:6] == "ignore":
-					Joint(self._p, joint_name, bodies, i, j).disable_motor()
-					continue
-
-				if joint_name[:8] != "jointfix":
-					joints[joint_name] = Joint(self._p, joint_name, bodies, i, j)
-					ordered_joints.append(joints[joint_name])
-
-					joints[joint_name].power_coef = 100.0
-
-				# TODO: Maybe we need this
-				# joints[joint_name].power_coef, joints[joint_name].max_velocity = joints[joint_name].limits()[2:4]
-				# self.ordered_joints.append(joints[joint_name])
-				# self.jdict[joint_name] = joints[joint_name]
-
-		return parts, joints, ordered_joints, self.robot_body
-
-	def reset_pose(self, position, orientation):
-		self.parts[self.robot_name].reset_pose(position, orientation)
 
 class MJCFBasedRobot(XmlBasedRobot):
-	"""
+  """
 	Base class for mujoco .xml based agents.
 	"""
 
-	def __init__(self,  model_xml, robot_name, action_dim, obs_dim, self_collision=True):
-		XmlBasedRobot.__init__(self, robot_name, action_dim, obs_dim, self_collision)
-		self.model_xml = model_xml
-		self.doneLoading=0
-	def reset(self, bullet_client):
-
-		self._p = bullet_client
-		#print("Created bullet_client with id=", self._p._client)
-		if (self.doneLoading==0):
-			self.ordered_joints = []
-			self.doneLoading=1
-			if self.self_collision:
-				self.objects = self._p.loadMJCF(os.path.join(pybullet_data.getDataPath(),"mjcf", self.model_xml), flags=pybullet.URDF_USE_SELF_COLLISION|pybullet.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS)
-				self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(self._p, self.objects	)
-			else:
-				self.objects = self._p.loadMJCF(os.path.join(pybullet_data.getDataPath(),"mjcf", self.model_xml))
-				self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(self._p, self.objects)
-		self.robot_specific_reset(self._p)
-
-		s = self.calc_state()  # optimization: calc_state() can calculate something in self.* for calc_potential() to use
-
-		return s
-
-	def calc_potential(self):
-		return 0
+  def __init__(self, model_xml, robot_name, action_dim, obs_dim, self_collision=True):
+    XmlBasedRobot.__init__(self, robot_name, action_dim, obs_dim, self_collision)
+    self.model_xml = model_xml
+    self.doneLoading = 0
+
+  def reset(self, bullet_client):
+
+    self._p = bullet_client
+    #print("Created bullet_client with id=", self._p._client)
+    if (self.doneLoading == 0):
+      self.ordered_joints = []
+      self.doneLoading = 1
+      if self.self_collision:
+        self.objects = self._p.loadMJCF(os.path.join(pybullet_data.getDataPath(), "mjcf",
+                                                     self.model_xml),
+                                        flags=pybullet.URDF_USE_SELF_COLLISION |
+                                        pybullet.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS)
+        self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(
+            self._p, self.objects)
+      else:
+        self.objects = self._p.loadMJCF(
+            os.path.join(pybullet_data.getDataPath(), "mjcf", self.model_xml))
+        self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(
+            self._p, self.objects)
+    self.robot_specific_reset(self._p)
+
+    s = self.calc_state(
+    )  # optimization: calc_state() can calculate something in self.* for calc_potential() to use
+
+    return s
+
+  def calc_potential(self):
+    return 0
 
 
 class URDFBasedRobot(XmlBasedRobot):
-	"""
+  """
 	Base class for URDF .xml based robots.
 	"""
 
-	def __init__(self, model_urdf, robot_name, action_dim, obs_dim, basePosition=[0, 0, 0], baseOrientation=[0, 0, 0, 1], fixed_base=False, self_collision=False):
-		XmlBasedRobot.__init__(self, robot_name, action_dim, obs_dim, self_collision)
-
-		self.model_urdf = model_urdf
-		self.basePosition = basePosition
-		self.baseOrientation = baseOrientation
-		self.fixed_base = fixed_base
-
-	def reset(self, bullet_client):
-		self._p = bullet_client
-		self.ordered_joints = []
-
-		print(os.path.join(os.path.dirname(__file__), "data", self.model_urdf))
-
-		if self.self_collision:
-			self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(self._p,
-				self._p.loadURDF(os.path.join(pybullet_data.getDataPath(), self.model_urdf),
-				basePosition=self.basePosition,
-				baseOrientation=self.baseOrientation,
-				useFixedBase=self.fixed_base,
-				flags=pybullet.URDF_USE_SELF_COLLISION))
-		else:
-			self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(self._p,
-				self._p.loadURDF(os.path.join(pybullet_data.getDataPath(), self.model_urdf),
-				basePosition=self.basePosition,
-				baseOrientation=self.baseOrientation,
-				useFixedBase=self.fixed_base))
-
-		self.robot_specific_reset(self._p)
-
-		s = self.calc_state()  # optimization: calc_state() can calculate something in self.* for calc_potential() to use
-		self.potential = self.calc_potential()
-
-		return s
-
-	def calc_potential(self):
-		return 0
+  def __init__(self,
+               model_urdf,
+               robot_name,
+               action_dim,
+               obs_dim,
+               basePosition=[0, 0, 0],
+               baseOrientation=[0, 0, 0, 1],
+               fixed_base=False,
+               self_collision=False):
+    XmlBasedRobot.__init__(self, robot_name, action_dim, obs_dim, self_collision)
+
+    self.model_urdf = model_urdf
+    self.basePosition = basePosition
+    self.baseOrientation = baseOrientation
+    self.fixed_base = fixed_base
+
+  def reset(self, bullet_client):
+    self._p = bullet_client
+    self.ordered_joints = []
+
+    print(os.path.join(os.path.dirname(__file__), "data", self.model_urdf))
+
+    if self.self_collision:
+      self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(
+          self._p,
+          self._p.loadURDF(os.path.join(pybullet_data.getDataPath(), self.model_urdf),
+                           basePosition=self.basePosition,
+                           baseOrientation=self.baseOrientation,
+                           useFixedBase=self.fixed_base,
+                           flags=pybullet.URDF_USE_SELF_COLLISION))
+    else:
+      self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(
+          self._p,
+          self._p.loadURDF(os.path.join(pybullet_data.getDataPath(), self.model_urdf),
+                           basePosition=self.basePosition,
+                           baseOrientation=self.baseOrientation,
+                           useFixedBase=self.fixed_base))
+
+    self.robot_specific_reset(self._p)
+
+    s = self.calc_state(
+    )  # optimization: calc_state() can calculate something in self.* for calc_potential() to use
+    self.potential = self.calc_potential()
+
+    return s
+
+  def calc_potential(self):
+    return 0
 
 
 class SDFBasedRobot(XmlBasedRobot):
-	"""
+  """
 	Base class for SDF robots in a Scene.
 	"""
 
-	def __init__(self,  model_sdf, robot_name, action_dim, obs_dim, basePosition=[0, 0, 0], baseOrientation=[0, 0, 0, 1], fixed_base=False, self_collision=False):
-		XmlBasedRobot.__init__(self, robot_name, action_dim, obs_dim, self_collision)
-
-		self.model_sdf = model_sdf
-		self.fixed_base = fixed_base
+  def __init__(self,
+               model_sdf,
+               robot_name,
+               action_dim,
+               obs_dim,
+               basePosition=[0, 0, 0],
+               baseOrientation=[0, 0, 0, 1],
+               fixed_base=False,
+               self_collision=False):
+    XmlBasedRobot.__init__(self, robot_name, action_dim, obs_dim, self_collision)
 
-	def reset(self, bullet_client):
-		self._p = bullet_client
+    self.model_sdf = model_sdf
+    self.fixed_base = fixed_base
 
-		self.ordered_joints = []
+  def reset(self, bullet_client):
+    self._p = bullet_client
 
-		self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(self._p,  # TODO: Not sure if this works, try it with kuka
-			self._p.loadSDF(os.path.join("models_robot", self.model_sdf)))
+    self.ordered_joints = []
 
-		self.robot_specific_reset(self._p)
+    self.parts, self.jdict, self.ordered_joints, self.robot_body = self.addToScene(
+        self._p,  # TODO: Not sure if this works, try it with kuka
+        self._p.loadSDF(os.path.join("models_robot", self.model_sdf)))
 
-		s = self.calc_state()  # optimization: calc_state() can calculate something in self.* for calc_potential() to use
-		self.potential = self.calc_potential()
+    self.robot_specific_reset(self._p)
 
-		return s
+    s = self.calc_state(
+    )  # optimization: calc_state() can calculate something in self.* for calc_potential() to use
+    self.potential = self.calc_potential()
 
-	def calc_potential(self):
-		return 0
+    return s
 
+  def calc_potential(self):
+    return 0
 
-class Pose_Helper: # dummy class to comply to original interface
-	def __init__(self, body_part):
-		self.body_part = body_part
 
-	def xyz(self):
-		return self.body_part.current_position()
+class Pose_Helper:  # dummy class to comply to original interface
 
-	def rpy(self):
-		return pybullet.getEulerFromQuaternion(self.body_part.current_orientation())
-
-	def orientation(self):
-		return self.body_part.current_orientation()
-
-class BodyPart:
-	def __init__(self, bullet_client, body_name, bodies, bodyIndex, bodyPartIndex):
-		self.bodies = bodies
-		self._p = bullet_client
-		self.bodyIndex = bodyIndex
-		self.bodyPartIndex = bodyPartIndex
-		self.initialPosition = self.current_position()
-		self.initialOrientation = self.current_orientation()
-		self.bp_pose = Pose_Helper(self)
+  def __init__(self, body_part):
+    self.body_part = body_part
 
-	def state_fields_of_pose_of(self, body_id, link_id=-1):  # a method you will most probably need a lot to get pose and orientation
-		if link_id == -1:
-			(x, y, z), (a, b, c, d) = self._p.getBasePositionAndOrientation(body_id)
-		else:
-			(x, y, z), (a, b, c, d), _, _, _, _ = self._p.getLinkState(body_id, link_id)
-		return np.array([x, y, z, a, b, c, d])
+  def xyz(self):
+    return self.body_part.current_position()
 
-	def get_position(self): return self.current_position()
+  def rpy(self):
+    return pybullet.getEulerFromQuaternion(self.body_part.current_orientation())
 
-	def get_pose(self):
-		return self.state_fields_of_pose_of(self.bodies[self.bodyIndex], self.bodyPartIndex)
+  def orientation(self):
+    return self.body_part.current_orientation()
 
-	def speed(self):
-		if self.bodyPartIndex == -1:
-			(vx, vy, vz), _ = self._p.getBaseVelocity(self.bodies[self.bodyIndex])
-		else:
-			(x,y,z), (a,b,c,d), _,_,_,_, (vx, vy, vz), (vr,vp,vy) = self._p.getLinkState(self.bodies[self.bodyIndex], self.bodyPartIndex, computeLinkVelocity=1)
-		return np.array([vx, vy, vz])
-
-	def current_position(self):
-		return self.get_pose()[:3]
-
-	def current_orientation(self):
-		return self.get_pose()[3:]
-
-	def get_orientation(self):
-		return self.current_orientation()
-
-	def reset_position(self, position):
-		self._p.resetBasePositionAndOrientation(self.bodies[self.bodyIndex], position, self.get_orientation())
-
-	def reset_orientation(self, orientation):
-		self._p.resetBasePositionAndOrientation(self.bodies[self.bodyIndex], self.get_position(), orientation)
-
-	def reset_velocity(self, linearVelocity=[0,0,0], angularVelocity =[0,0,0]):
-		self._p.resetBaseVelocity(self.bodies[self.bodyIndex], linearVelocity, angularVelocity)
-
-	def reset_pose(self, position, orientation):
-		self._p.resetBasePositionAndOrientation(self.bodies[self.bodyIndex], position, orientation)
-
-	def pose(self):
-		return self.bp_pose
-
-	def contact_list(self):
-		return self._p.getContactPoints(self.bodies[self.bodyIndex], -1, self.bodyPartIndex, -1)
 
+class BodyPart:
 
-class Joint:
-	def __init__(self, bullet_client, joint_name, bodies, bodyIndex, jointIndex):
-		self.bodies = bodies
-		self._p = bullet_client
-		self.bodyIndex = bodyIndex
-		self.jointIndex = jointIndex
-		self.joint_name = joint_name
+  def __init__(self, bullet_client, body_name, bodies, bodyIndex, bodyPartIndex):
+    self.bodies = bodies
+    self._p = bullet_client
+    self.bodyIndex = bodyIndex
+    self.bodyPartIndex = bodyPartIndex
+    self.initialPosition = self.current_position()
+    self.initialOrientation = self.current_orientation()
+    self.bp_pose = Pose_Helper(self)
 
-		jointInfo = self._p.getJointInfo(self.bodies[self.bodyIndex], self.jointIndex)
-		self.lowerLimit = jointInfo[8]
-		self.upperLimit = jointInfo[9]
+  def state_fields_of_pose_of(
+      self, body_id,
+      link_id=-1):  # a method you will most probably need a lot to get pose and orientation
+    if link_id == -1:
+      (x, y, z), (a, b, c, d) = self._p.getBasePositionAndOrientation(body_id)
+    else:
+      (x, y, z), (a, b, c, d), _, _, _, _ = self._p.getLinkState(body_id, link_id)
+    return np.array([x, y, z, a, b, c, d])
 
-		self.power_coeff = 0
+  def get_position(self):
+    return self.current_position()
 
-	def set_state(self, x, vx):
-		self._p.resetJointState(self.bodies[self.bodyIndex], self.jointIndex, x, vx)
+  def get_pose(self):
+    return self.state_fields_of_pose_of(self.bodies[self.bodyIndex], self.bodyPartIndex)
 
-	def current_position(self): # just some synonyme method
-		return self.get_state()
+  def speed(self):
+    if self.bodyPartIndex == -1:
+      (vx, vy, vz), _ = self._p.getBaseVelocity(self.bodies[self.bodyIndex])
+    else:
+      (x, y, z), (a, b, c, d), _, _, _, _, (vx, vy, vz), (vr, vp, vy) = self._p.getLinkState(
+          self.bodies[self.bodyIndex], self.bodyPartIndex, computeLinkVelocity=1)
+    return np.array([vx, vy, vz])
 
-	def current_relative_position(self):
-		pos, vel = self.get_state()
-		pos_mid = 0.5 * (self.lowerLimit + self.upperLimit);
-		return (
-			2 * (pos - pos_mid) / (self.upperLimit - self.lowerLimit),
-			0.1 * vel
-		)
+  def current_position(self):
+    return self.get_pose()[:3]
 
-	def get_state(self):
-		x, vx,_,_ = self._p.getJointState(self.bodies[self.bodyIndex],self.jointIndex)
-		return x, vx
+  def current_orientation(self):
+    return self.get_pose()[3:]
 
-	def get_position(self):
-		x, _ = self.get_state()
-		return x
+  def get_orientation(self):
+    return self.current_orientation()
 
-	def get_orientation(self):
-		_,r = self.get_state()
-		return r
+  def reset_position(self, position):
+    self._p.resetBasePositionAndOrientation(self.bodies[self.bodyIndex], position,
+                                            self.get_orientation())
 
-	def get_velocity(self):
-		_, vx = self.get_state()
-		return vx
+  def reset_orientation(self, orientation):
+    self._p.resetBasePositionAndOrientation(self.bodies[self.bodyIndex], self.get_position(),
+                                            orientation)
 
-	def set_position(self, position):
-		self._p.setJointMotorControl2(self.bodies[self.bodyIndex],self.jointIndex,pybullet.POSITION_CONTROL, targetPosition=position)
+  def reset_velocity(self, linearVelocity=[0, 0, 0], angularVelocity=[0, 0, 0]):
+    self._p.resetBaseVelocity(self.bodies[self.bodyIndex], linearVelocity, angularVelocity)
 
-	def set_velocity(self, velocity):
-		self._p.setJointMotorControl2(self.bodies[self.bodyIndex],self.jointIndex,pybullet.VELOCITY_CONTROL, targetVelocity=velocity)
+  def reset_pose(self, position, orientation):
+    self._p.resetBasePositionAndOrientation(self.bodies[self.bodyIndex], position, orientation)
 
-	def set_motor_torque(self, torque): # just some synonyme method
-		self.set_torque(torque)
+  def pose(self):
+    return self.bp_pose
 
-	def set_torque(self, torque):
-		self._p.setJointMotorControl2(bodyIndex=self.bodies[self.bodyIndex], jointIndex=self.jointIndex, controlMode=pybullet.TORQUE_CONTROL, force=torque) #, positionGain=0.1, velocityGain=0.1)
+  def contact_list(self):
+    return self._p.getContactPoints(self.bodies[self.bodyIndex], -1, self.bodyPartIndex, -1)
 
-	def reset_current_position(self, position, velocity): # just some synonyme method
-		self.reset_position(position, velocity)
 
-	def reset_position(self, position, velocity):
-		self._p.resetJointState(self.bodies[self.bodyIndex],self.jointIndex,targetValue=position, targetVelocity=velocity)
-		self.disable_motor()
+class Joint:
 
-	def disable_motor(self):
-		self._p.setJointMotorControl2(self.bodies[self.bodyIndex],self.jointIndex,controlMode=pybullet.POSITION_CONTROL, targetPosition=0, targetVelocity=0, positionGain=0.1, velocityGain=0.1, force=0)
+  def __init__(self, bullet_client, joint_name, bodies, bodyIndex, jointIndex):
+    self.bodies = bodies
+    self._p = bullet_client
+    self.bodyIndex = bodyIndex
+    self.jointIndex = jointIndex
+    self.joint_name = joint_name
+
+    jointInfo = self._p.getJointInfo(self.bodies[self.bodyIndex], self.jointIndex)
+    self.lowerLimit = jointInfo[8]
+    self.upperLimit = jointInfo[9]
+
+    self.power_coeff = 0
+
+  def set_state(self, x, vx):
+    self._p.resetJointState(self.bodies[self.bodyIndex], self.jointIndex, x, vx)
+
+  def current_position(self):  # just some synonyme method
+    return self.get_state()
+
+  def current_relative_position(self):
+    pos, vel = self.get_state()
+    pos_mid = 0.5 * (self.lowerLimit + self.upperLimit)
+    return (2 * (pos - pos_mid) / (self.upperLimit - self.lowerLimit), 0.1 * vel)
+
+  def get_state(self):
+    x, vx, _, _ = self._p.getJointState(self.bodies[self.bodyIndex], self.jointIndex)
+    return x, vx
+
+  def get_position(self):
+    x, _ = self.get_state()
+    return x
+
+  def get_orientation(self):
+    _, r = self.get_state()
+    return r
+
+  def get_velocity(self):
+    _, vx = self.get_state()
+    return vx
+
+  def set_position(self, position):
+    self._p.setJointMotorControl2(self.bodies[self.bodyIndex],
+                                  self.jointIndex,
+                                  pybullet.POSITION_CONTROL,
+                                  targetPosition=position)
+
+  def set_velocity(self, velocity):
+    self._p.setJointMotorControl2(self.bodies[self.bodyIndex],
+                                  self.jointIndex,
+                                  pybullet.VELOCITY_CONTROL,
+                                  targetVelocity=velocity)
+
+  def set_motor_torque(self, torque):  # just some synonyme method
+    self.set_torque(torque)
+
+  def set_torque(self, torque):
+    self._p.setJointMotorControl2(bodyIndex=self.bodies[self.bodyIndex],
+                                  jointIndex=self.jointIndex,
+                                  controlMode=pybullet.TORQUE_CONTROL,
+                                  force=torque)  #, positionGain=0.1, velocityGain=0.1)
+
+  def reset_current_position(self, position, velocity):  # just some synonyme method
+    self.reset_position(position, velocity)
+
+  def reset_position(self, position, velocity):
+    self._p.resetJointState(self.bodies[self.bodyIndex],
+                            self.jointIndex,
+                            targetValue=position,
+                            targetVelocity=velocity)
+    self.disable_motor()
+
+  def disable_motor(self):
+    self._p.setJointMotorControl2(self.bodies[self.bodyIndex],
+                                  self.jointIndex,
+                                  controlMode=pybullet.POSITION_CONTROL,
+                                  targetPosition=0,
+                                  targetVelocity=0,
+                                  positionGain=0.1,
+                                  velocityGain=0.1,
+                                  force=0)
diff --git a/examples/pybullet/gym/pybullet_envs/robot_locomotors.py b/examples/pybullet/gym/pybullet_envs/robot_locomotors.py
index 7ed7a1272..43ff604ac 100644
--- a/examples/pybullet/gym/pybullet_envs/robot_locomotors.py
+++ b/examples/pybullet/gym/pybullet_envs/robot_locomotors.py
@@ -1,321 +1,344 @@
 from robot_bases import XmlBasedRobot, MJCFBasedRobot, URDFBasedRobot
 import numpy as np
-import pybullet 
+import pybullet
 import os
 import pybullet_data
 from robot_bases import BodyPart
 
+
 class WalkerBase(MJCFBasedRobot):
-	def __init__(self,  fn, robot_name, action_dim, obs_dim, power):
-		MJCFBasedRobot.__init__(self, fn, robot_name, action_dim, obs_dim)
-		self.power = power
-		self.camera_x = 0
-		self.start_pos_x, self.start_pos_y, self.start_pos_z = 0, 0, 0
-		self.walk_target_x = 1e3  # kilometer away
-		self.walk_target_y = 0
-		self.body_xyz=[0,0,0]
-
-	def robot_specific_reset(self, bullet_client):
-		self._p = bullet_client
-		for j in self.ordered_joints:
-			j.reset_current_position(self.np_random.uniform(low=-0.1, high=0.1), 0)
-
-		self.feet = [self.parts[f] for f in self.foot_list]
-		self.feet_contact = np.array([0.0 for f in self.foot_list], dtype=np.float32)
-		self.scene.actor_introduce(self)
-		self.initial_z = None
-
-	def apply_action(self, a):
-		assert (np.isfinite(a).all())
-		for n, j in enumerate(self.ordered_joints):
-			j.set_motor_torque(self.power * j.power_coef * float(np.clip(a[n], -1, +1)))
-
-	def calc_state(self):
-		j = np.array([j.current_relative_position() for j in self.ordered_joints], dtype=np.float32).flatten()
-		# even elements [0::2] position, scaled to -1..+1 between limits
-		# odd elements  [1::2] angular speed, scaled to show -1..+1
-		self.joint_speeds = j[1::2]
-		self.joints_at_limit = np.count_nonzero(np.abs(j[0::2]) > 0.99)
-
-		body_pose = self.robot_body.pose()
-		parts_xyz = np.array([p.pose().xyz() for p in self.parts.values()]).flatten()
-		self.body_xyz = (
-		parts_xyz[0::3].mean(), parts_xyz[1::3].mean(), body_pose.xyz()[2])  # torso z is more informative than mean z
-		self.body_rpy = body_pose.rpy()
-		z = self.body_xyz[2]
-		if self.initial_z == None:
-			self.initial_z = z
-		r, p, yaw = self.body_rpy
-		self.walk_target_theta = np.arctan2(self.walk_target_y - self.body_xyz[1],
-											self.walk_target_x - self.body_xyz[0])
-		self.walk_target_dist = np.linalg.norm(
-			[self.walk_target_y - self.body_xyz[1], self.walk_target_x - self.body_xyz[0]])
-		angle_to_target = self.walk_target_theta - yaw
-
-		rot_speed = np.array(
-			[[np.cos(-yaw), -np.sin(-yaw), 0],
-			 [np.sin(-yaw), np.cos(-yaw), 0],
-			 [		0,			 0, 1]]
-		)
-		vx, vy, vz = np.dot(rot_speed, self.robot_body.speed())  # rotate speed back to body point of view
-
-		more = np.array([ z-self.initial_z,
-			np.sin(angle_to_target), np.cos(angle_to_target),
-			0.3* vx , 0.3* vy , 0.3* vz ,  # 0.3 is just scaling typical speed into -1..+1, no physical sense here
-			r, p], dtype=np.float32)
-		return np.clip( np.concatenate([more] + [j] + [self.feet_contact]), -5, +5)
-
-	def calc_potential(self):
-		# progress in potential field is speed*dt, typical speed is about 2-3 meter per second, this potential will change 2-3 per frame (not per second),
-		# all rewards have rew/frame units and close to 1.0
-		debugmode=0
-		if (debugmode):
-			print("calc_potential: self.walk_target_dist")
-			print(self.walk_target_dist)
-			print("self.scene.dt")
-			print(self.scene.dt)
-			print("self.scene.frame_skip")
-			print(self.scene.frame_skip)
-			print("self.scene.timestep")
-			print(self.scene.timestep)
-		return - self.walk_target_dist / self.scene.dt
+
+  def __init__(self, fn, robot_name, action_dim, obs_dim, power):
+    MJCFBasedRobot.__init__(self, fn, robot_name, action_dim, obs_dim)
+    self.power = power
+    self.camera_x = 0
+    self.start_pos_x, self.start_pos_y, self.start_pos_z = 0, 0, 0
+    self.walk_target_x = 1e3  # kilometer away
+    self.walk_target_y = 0
+    self.body_xyz = [0, 0, 0]
+
+  def robot_specific_reset(self, bullet_client):
+    self._p = bullet_client
+    for j in self.ordered_joints:
+      j.reset_current_position(self.np_random.uniform(low=-0.1, high=0.1), 0)
+
+    self.feet = [self.parts[f] for f in self.foot_list]
+    self.feet_contact = np.array([0.0 for f in self.foot_list], dtype=np.float32)
+    self.scene.actor_introduce(self)
+    self.initial_z = None
+
+  def apply_action(self, a):
+    assert (np.isfinite(a).all())
+    for n, j in enumerate(self.ordered_joints):
+      j.set_motor_torque(self.power * j.power_coef * float(np.clip(a[n], -1, +1)))
+
+  def calc_state(self):
+    j = np.array([j.current_relative_position() for j in self.ordered_joints],
+                 dtype=np.float32).flatten()
+    # even elements [0::2] position, scaled to -1..+1 between limits
+    # odd elements  [1::2] angular speed, scaled to show -1..+1
+    self.joint_speeds = j[1::2]
+    self.joints_at_limit = np.count_nonzero(np.abs(j[0::2]) > 0.99)
+
+    body_pose = self.robot_body.pose()
+    parts_xyz = np.array([p.pose().xyz() for p in self.parts.values()]).flatten()
+    self.body_xyz = (parts_xyz[0::3].mean(), parts_xyz[1::3].mean(), body_pose.xyz()[2]
+                    )  # torso z is more informative than mean z
+    self.body_rpy = body_pose.rpy()
+    z = self.body_xyz[2]
+    if self.initial_z == None:
+      self.initial_z = z
+    r, p, yaw = self.body_rpy
+    self.walk_target_theta = np.arctan2(self.walk_target_y - self.body_xyz[1],
+                                        self.walk_target_x - self.body_xyz[0])
+    self.walk_target_dist = np.linalg.norm(
+        [self.walk_target_y - self.body_xyz[1], self.walk_target_x - self.body_xyz[0]])
+    angle_to_target = self.walk_target_theta - yaw
+
+    rot_speed = np.array([[np.cos(-yaw), -np.sin(-yaw), 0], [np.sin(-yaw),
+                                                             np.cos(-yaw), 0], [0, 0, 1]])
+    vx, vy, vz = np.dot(rot_speed,
+                        self.robot_body.speed())  # rotate speed back to body point of view
+
+    more = np.array(
+        [
+            z - self.initial_z,
+            np.sin(angle_to_target),
+            np.cos(angle_to_target),
+            0.3 * vx,
+            0.3 * vy,
+            0.3 * vz,  # 0.3 is just scaling typical speed into -1..+1, no physical sense here
+            r,
+            p
+        ],
+        dtype=np.float32)
+    return np.clip(np.concatenate([more] + [j] + [self.feet_contact]), -5, +5)
+
+  def calc_potential(self):
+    # progress in potential field is speed*dt, typical speed is about 2-3 meter per second, this potential will change 2-3 per frame (not per second),
+    # all rewards have rew/frame units and close to 1.0
+    debugmode = 0
+    if (debugmode):
+      print("calc_potential: self.walk_target_dist")
+      print(self.walk_target_dist)
+      print("self.scene.dt")
+      print(self.scene.dt)
+      print("self.scene.frame_skip")
+      print(self.scene.frame_skip)
+      print("self.scene.timestep")
+      print(self.scene.timestep)
+    return -self.walk_target_dist / self.scene.dt
 
 
 class Hopper(WalkerBase):
-	foot_list = ["foot"]
+  foot_list = ["foot"]
 
-	def __init__(self):
-		WalkerBase.__init__(self, "hopper.xml", "torso", action_dim=3, obs_dim=15, power=0.75)
+  def __init__(self):
+    WalkerBase.__init__(self, "hopper.xml", "torso", action_dim=3, obs_dim=15, power=0.75)
 
-	def alive_bonus(self, z, pitch):
-		return +1 if z > 0.8 and abs(pitch) < 1.0 else -1
+  def alive_bonus(self, z, pitch):
+    return +1 if z > 0.8 and abs(pitch) < 1.0 else -1
 
 
 class Walker2D(WalkerBase):
-	foot_list = ["foot", "foot_left"]
+  foot_list = ["foot", "foot_left"]
 
-	def __init__(self):
-		WalkerBase.__init__(self,  "walker2d.xml", "torso", action_dim=6, obs_dim=22, power=0.40)
+  def __init__(self):
+    WalkerBase.__init__(self, "walker2d.xml", "torso", action_dim=6, obs_dim=22, power=0.40)
 
-	def alive_bonus(self, z, pitch):
-		return +1 if z > 0.8 and abs(pitch) < 1.0 else -1
+  def alive_bonus(self, z, pitch):
+    return +1 if z > 0.8 and abs(pitch) < 1.0 else -1
 
-	def robot_specific_reset(self, bullet_client):
-		WalkerBase.robot_specific_reset(self, bullet_client)
-		for n in ["foot_joint", "foot_left_joint"]:
-			self.jdict[n].power_coef = 30.0
+  def robot_specific_reset(self, bullet_client):
+    WalkerBase.robot_specific_reset(self, bullet_client)
+    for n in ["foot_joint", "foot_left_joint"]:
+      self.jdict[n].power_coef = 30.0
 
 
 class HalfCheetah(WalkerBase):
-	foot_list = ["ffoot", "fshin", "fthigh",  "bfoot", "bshin", "bthigh"]  # track these contacts with ground
+  foot_list = ["ffoot", "fshin", "fthigh", "bfoot", "bshin",
+               "bthigh"]  # track these contacts with ground
 
-	def __init__(self):
-		WalkerBase.__init__(self, "half_cheetah.xml", "torso", action_dim=6, obs_dim=26, power=0.90)
+  def __init__(self):
+    WalkerBase.__init__(self, "half_cheetah.xml", "torso", action_dim=6, obs_dim=26, power=0.90)
 
-	def alive_bonus(self, z, pitch):
-		# Use contact other than feet to terminate episode: due to a lot of strange walks using knees
-		return +1 if np.abs(pitch) < 1.0 and not self.feet_contact[1] and not self.feet_contact[2] and not self.feet_contact[4] and not self.feet_contact[5] else -1
+  def alive_bonus(self, z, pitch):
+    # Use contact other than feet to terminate episode: due to a lot of strange walks using knees
+    return +1 if np.abs(pitch) < 1.0 and not self.feet_contact[1] and not self.feet_contact[
+        2] and not self.feet_contact[4] and not self.feet_contact[5] else -1
 
-	def robot_specific_reset(self, bullet_client):
-		WalkerBase.robot_specific_reset(self, bullet_client)
-		self.jdict["bthigh"].power_coef = 120.0
-		self.jdict["bshin"].power_coef  = 90.0
-		self.jdict["bfoot"].power_coef  = 60.0
-		self.jdict["fthigh"].power_coef = 140.0
-		self.jdict["fshin"].power_coef  = 60.0
-		self.jdict["ffoot"].power_coef  = 30.0
+  def robot_specific_reset(self, bullet_client):
+    WalkerBase.robot_specific_reset(self, bullet_client)
+    self.jdict["bthigh"].power_coef = 120.0
+    self.jdict["bshin"].power_coef = 90.0
+    self.jdict["bfoot"].power_coef = 60.0
+    self.jdict["fthigh"].power_coef = 140.0
+    self.jdict["fshin"].power_coef = 60.0
+    self.jdict["ffoot"].power_coef = 30.0
 
 
 class Ant(WalkerBase):
-	foot_list = ['front_left_foot', 'front_right_foot', 'left_back_foot', 'right_back_foot']
+  foot_list = ['front_left_foot', 'front_right_foot', 'left_back_foot', 'right_back_foot']
 
-	def __init__(self):
-		WalkerBase.__init__(self, "ant.xml", "torso", action_dim=8, obs_dim=28, power=2.5)
+  def __init__(self):
+    WalkerBase.__init__(self, "ant.xml", "torso", action_dim=8, obs_dim=28, power=2.5)
 
-	def alive_bonus(self, z, pitch):
-		return +1 if z > 0.26 else -1  # 0.25 is central sphere rad, die if it scrapes the ground
+  def alive_bonus(self, z, pitch):
+    return +1 if z > 0.26 else -1  # 0.25 is central sphere rad, die if it scrapes the ground
 
 
 class Humanoid(WalkerBase):
-	self_collision = True
-	foot_list = ["right_foot", "left_foot"]  # "left_hand", "right_hand"
-
-	def __init__(self):
-		WalkerBase.__init__(self,  'humanoid_symmetric.xml', 'torso', action_dim=17, obs_dim=44, power=0.41)
-		# 17 joints, 4 of them important for walking (hip, knee), others may as well be turned off, 17/4 = 4.25
-
-	def robot_specific_reset(self, bullet_client):
-		WalkerBase.robot_specific_reset(self, bullet_client)
-		self.motor_names  = ["abdomen_z", "abdomen_y", "abdomen_x"]
-		self.motor_power  = [100, 100, 100]
-		self.motor_names += ["right_hip_x", "right_hip_z", "right_hip_y", "right_knee"]
-		self.motor_power += [100, 100, 300, 200]
-		self.motor_names += ["left_hip_x", "left_hip_z", "left_hip_y", "left_knee"]
-		self.motor_power += [100, 100, 300, 200]
-		self.motor_names += ["right_shoulder1", "right_shoulder2", "right_elbow"]
-		self.motor_power += [75, 75, 75]
-		self.motor_names += ["left_shoulder1", "left_shoulder2", "left_elbow"]
-		self.motor_power += [75, 75, 75]
-		self.motors = [self.jdict[n] for n in self.motor_names]
-		if self.random_yaw:
-			position = [0,0,0]
-			orientation = [0,0,0]
-			yaw = self.np_random.uniform(low=-3.14, high=3.14)
-			if self.random_lean and self.np_random.randint(2)==0:
-				cpose.set_xyz(0, 0, 1.4)
-				if self.np_random.randint(2)==0:
-					pitch = np.pi/2
-					position = [0, 0, 0.45]
-				else:
-					pitch = np.pi*3/2
-					position = [0, 0, 0.25]
-				roll = 0
-				orientation = [roll, pitch, yaw]
-			else:
-				position = [0, 0, 1.4]
-				orientation = [0, 0, yaw]  # just face random direction, but stay straight otherwise
-			self.robot_body.reset_position(position)
-			self.robot_body.reset_orientation(orientation)
-		self.initial_z = 0.8
-
-	random_yaw = False
-	random_lean = False
-
-	def apply_action(self, a):
-		assert( np.isfinite(a).all() )
-		force_gain = 1
-		for i, m, power in zip(range(17), self.motors, self.motor_power):
-			m.set_motor_torque(float(force_gain * power * self.power * np.clip(a[i], -1, +1)))
-
-	def alive_bonus(self, z, pitch):
-		return +2 if z > 0.78 else -1   # 2 here because 17 joints produce a lot of electricity cost just from policy noise, living must be better than dying
-
-
-
-
-def get_cube(_p, x, y, z):	
-	body = _p.loadURDF(os.path.join(pybullet_data.getDataPath(),"cube_small.urdf"), [x, y, z])
-	_p.changeDynamics(body,-1, mass=1.2)#match Roboschool
-	part_name, _ = _p.getBodyInfo(body)
-	part_name = part_name.decode("utf8")
-	bodies = [body]
-	return BodyPart(_p, part_name, bodies, 0, -1)
+  self_collision = True
+  foot_list = ["right_foot", "left_foot"]  # "left_hand", "right_hand"
+
+  def __init__(self):
+    WalkerBase.__init__(self,
+                        'humanoid_symmetric.xml',
+                        'torso',
+                        action_dim=17,
+                        obs_dim=44,
+                        power=0.41)
+    # 17 joints, 4 of them important for walking (hip, knee), others may as well be turned off, 17/4 = 4.25
+
+  def robot_specific_reset(self, bullet_client):
+    WalkerBase.robot_specific_reset(self, bullet_client)
+    self.motor_names = ["abdomen_z", "abdomen_y", "abdomen_x"]
+    self.motor_power = [100, 100, 100]
+    self.motor_names += ["right_hip_x", "right_hip_z", "right_hip_y", "right_knee"]
+    self.motor_power += [100, 100, 300, 200]
+    self.motor_names += ["left_hip_x", "left_hip_z", "left_hip_y", "left_knee"]
+    self.motor_power += [100, 100, 300, 200]
+    self.motor_names += ["right_shoulder1", "right_shoulder2", "right_elbow"]
+    self.motor_power += [75, 75, 75]
+    self.motor_names += ["left_shoulder1", "left_shoulder2", "left_elbow"]
+    self.motor_power += [75, 75, 75]
+    self.motors = [self.jdict[n] for n in self.motor_names]
+    if self.random_yaw:
+      position = [0, 0, 0]
+      orientation = [0, 0, 0]
+      yaw = self.np_random.uniform(low=-3.14, high=3.14)
+      if self.random_lean and self.np_random.randint(2) == 0:
+        cpose.set_xyz(0, 0, 1.4)
+        if self.np_random.randint(2) == 0:
+          pitch = np.pi / 2
+          position = [0, 0, 0.45]
+        else:
+          pitch = np.pi * 3 / 2
+          position = [0, 0, 0.25]
+        roll = 0
+        orientation = [roll, pitch, yaw]
+      else:
+        position = [0, 0, 1.4]
+        orientation = [0, 0, yaw]  # just face random direction, but stay straight otherwise
+      self.robot_body.reset_position(position)
+      self.robot_body.reset_orientation(orientation)
+    self.initial_z = 0.8
+
+  random_yaw = False
+  random_lean = False
+
+  def apply_action(self, a):
+    assert (np.isfinite(a).all())
+    force_gain = 1
+    for i, m, power in zip(range(17), self.motors, self.motor_power):
+      m.set_motor_torque(float(force_gain * power * self.power * np.clip(a[i], -1, +1)))
+
+  def alive_bonus(self, z, pitch):
+    return +2 if z > 0.78 else -1  # 2 here because 17 joints produce a lot of electricity cost just from policy noise, living must be better than dying
+
+
+def get_cube(_p, x, y, z):
+  body = _p.loadURDF(os.path.join(pybullet_data.getDataPath(), "cube_small.urdf"), [x, y, z])
+  _p.changeDynamics(body, -1, mass=1.2)  #match Roboschool
+  part_name, _ = _p.getBodyInfo(body)
+  part_name = part_name.decode("utf8")
+  bodies = [body]
+  return BodyPart(_p, part_name, bodies, 0, -1)
 
 
 def get_sphere(_p, x, y, z):
-	body = _p.loadURDF(os.path.join(pybullet_data.getDataPath(),"sphere2red_nocol.urdf"), [x, y, z])
-	part_name, _ = _p.getBodyInfo(body)
-	part_name = part_name.decode("utf8")
-	bodies = [body]
-	return BodyPart(_p, part_name, bodies, 0, -1)
+  body = _p.loadURDF(os.path.join(pybullet_data.getDataPath(), "sphere2red_nocol.urdf"), [x, y, z])
+  part_name, _ = _p.getBodyInfo(body)
+  part_name = part_name.decode("utf8")
+  bodies = [body]
+  return BodyPart(_p, part_name, bodies, 0, -1)
+
 
 class HumanoidFlagrun(Humanoid):
-	def __init__(self):
-		Humanoid.__init__(self)
-		self.flag = None
-		
-	def robot_specific_reset(self, bullet_client):
-		Humanoid.robot_specific_reset(self, bullet_client)
-		self.flag_reposition()
-
-	def flag_reposition(self):
-		self.walk_target_x = self.np_random.uniform(low=-self.scene.stadium_halflen,   high=+self.scene.stadium_halflen)
-		self.walk_target_y = self.np_random.uniform(low=-self.scene.stadium_halfwidth, high=+self.scene.stadium_halfwidth)
-		more_compact = 0.5  # set to 1.0 whole football field
-		self.walk_target_x *= more_compact
-		self.walk_target_y *= more_compact
-		
-		if (self.flag):
-			#for b in self.flag.bodies:
-			#	print("remove body uid",b)
-			#	p.removeBody(b)
-			self._p.resetBasePositionAndOrientation(self.flag.bodies[0],[self.walk_target_x, self.walk_target_y, 0.7],[0,0,0,1])
-		else:
-			self.flag = get_sphere(self._p, self.walk_target_x, self.walk_target_y, 0.7)
-		self.flag_timeout = 600/self.scene.frame_skip #match Roboschool 
-
-	def calc_state(self):
-		self.flag_timeout -= 1
-		state = Humanoid.calc_state(self)
-		if self.walk_target_dist < 1 or self.flag_timeout <= 0:
-			self.flag_reposition()
-			state = Humanoid.calc_state(self)  # caclulate state again, against new flag pos
-			self.potential = self.calc_potential()	   # avoid reward jump
-		return state
+
+  def __init__(self):
+    Humanoid.__init__(self)
+    self.flag = None
+
+  def robot_specific_reset(self, bullet_client):
+    Humanoid.robot_specific_reset(self, bullet_client)
+    self.flag_reposition()
+
+  def flag_reposition(self):
+    self.walk_target_x = self.np_random.uniform(low=-self.scene.stadium_halflen,
+                                                high=+self.scene.stadium_halflen)
+    self.walk_target_y = self.np_random.uniform(low=-self.scene.stadium_halfwidth,
+                                                high=+self.scene.stadium_halfwidth)
+    more_compact = 0.5  # set to 1.0 whole football field
+    self.walk_target_x *= more_compact
+    self.walk_target_y *= more_compact
+
+    if (self.flag):
+      #for b in self.flag.bodies:
+      #	print("remove body uid",b)
+      #	p.removeBody(b)
+      self._p.resetBasePositionAndOrientation(self.flag.bodies[0],
+                                              [self.walk_target_x, self.walk_target_y, 0.7],
+                                              [0, 0, 0, 1])
+    else:
+      self.flag = get_sphere(self._p, self.walk_target_x, self.walk_target_y, 0.7)
+    self.flag_timeout = 600 / self.scene.frame_skip  #match Roboschool
+
+  def calc_state(self):
+    self.flag_timeout -= 1
+    state = Humanoid.calc_state(self)
+    if self.walk_target_dist < 1 or self.flag_timeout <= 0:
+      self.flag_reposition()
+      state = Humanoid.calc_state(self)  # caclulate state again, against new flag pos
+      self.potential = self.calc_potential()  # avoid reward jump
+    return state
 
 
 class HumanoidFlagrunHarder(HumanoidFlagrun):
-	def __init__(self):
-		HumanoidFlagrun.__init__(self)
-		self.flag = None
-		self.aggressive_cube = None
-		self.frame = 0
-
-	def robot_specific_reset(self, bullet_client):
-
-		HumanoidFlagrun.robot_specific_reset(self, bullet_client)
-		
-		self.frame = 0
-		if (self.aggressive_cube):
-			self._p.resetBasePositionAndOrientation(self.aggressive_cube.bodies[0],[-1.5,0,0.05],[0,0,0,1])
-		else:
-			self.aggressive_cube = get_cube(self._p, -1.5,0,0.05)
-		self.on_ground_frame_counter = 0
-		self.crawl_start_potential = None
-		self.crawl_ignored_potential = 0.0
-		self.initial_z = 0.8
-
-	def alive_bonus(self, z, pitch):
-		if self.frame%30==0 and self.frame>100 and self.on_ground_frame_counter==0:
-			target_xyz  = np.array(self.body_xyz)
-			robot_speed = np.array(self.robot_body.speed())
-			angle = self.np_random.uniform(low=-3.14, high=3.14)
-			from_dist   = 4.0
-			attack_speed   = self.np_random.uniform(low=20.0, high=30.0)  # speed 20..30 (* mass in cube.urdf = impulse)
-			time_to_travel = from_dist / attack_speed
-			target_xyz += robot_speed*time_to_travel  # predict future position at the moment the cube hits the robot
-			position = [target_xyz[0] + from_dist*np.cos(angle),
-				target_xyz[1] + from_dist*np.sin(angle),
-				target_xyz[2] + 1.0]
-			attack_speed_vector = target_xyz - np.array(position)
-			attack_speed_vector *= attack_speed / np.linalg.norm(attack_speed_vector)
-			attack_speed_vector += self.np_random.uniform(low=-1.0, high=+1.0, size=(3,))
-			self.aggressive_cube.reset_position(position)
-			self.aggressive_cube.reset_velocity(linearVelocity=attack_speed_vector)
-		if z < 0.8:
-			self.on_ground_frame_counter += 1
-		elif self.on_ground_frame_counter > 0:
-			self.on_ground_frame_counter -= 1
-		# End episode if the robot can't get up in 170 frames, to save computation and decorrelate observations.
-		self.frame += 1
-		return self.potential_leak() if self.on_ground_frame_counter<170 else -1
-
-	def potential_leak(self):
-		z = self.body_xyz[2]		  # 0.00 .. 0.8 .. 1.05 normal walk, 1.2 when jumping
-		z = np.clip(z, 0, 0.8)
-		return z/0.8 + 1.0			# 1.00 .. 2.0
-
-	def calc_potential(self):
-		# We see alive bonus here as a leak from potential field. Value V(s) of a given state equals
-		# potential, if it is topped up with gamma*potential every frame. Gamma is assumed 0.99.
-		#
-		# 2.0 alive bonus if z>0.8, potential is 200, leak gamma=0.99, (1-0.99)*200==2.0
-		# 1.0 alive bonus on the ground z==0, potential is 100, leak (1-0.99)*100==1.0
-		#
-		# Why robot whould stand up: to receive 100 points in potential field difference.
-		flag_running_progress = Humanoid.calc_potential(self)
-
-		# This disables crawl.
-		if self.body_xyz[2] < 0.8:
-			if self.crawl_start_potential is None:
-				self.crawl_start_potential = flag_running_progress - self.crawl_ignored_potential
-				#print("CRAWL START %+0.1f %+0.1f" % (self.crawl_start_potential, flag_running_progress))
-			self.crawl_ignored_potential = flag_running_progress - self.crawl_start_potential
-			flag_running_progress  = self.crawl_start_potential
-		else:
-			#print("CRAWL STOP %+0.1f %+0.1f" % (self.crawl_ignored_potential, flag_running_progress))
-			flag_running_progress -= self.crawl_ignored_potential
-			self.crawl_start_potential = None
-
-		return flag_running_progress + self.potential_leak()*100
 
+  def __init__(self):
+    HumanoidFlagrun.__init__(self)
+    self.flag = None
+    self.aggressive_cube = None
+    self.frame = 0
+
+  def robot_specific_reset(self, bullet_client):
+
+    HumanoidFlagrun.robot_specific_reset(self, bullet_client)
+
+    self.frame = 0
+    if (self.aggressive_cube):
+      self._p.resetBasePositionAndOrientation(self.aggressive_cube.bodies[0], [-1.5, 0, 0.05],
+                                              [0, 0, 0, 1])
+    else:
+      self.aggressive_cube = get_cube(self._p, -1.5, 0, 0.05)
+    self.on_ground_frame_counter = 0
+    self.crawl_start_potential = None
+    self.crawl_ignored_potential = 0.0
+    self.initial_z = 0.8
+
+  def alive_bonus(self, z, pitch):
+    if self.frame % 30 == 0 and self.frame > 100 and self.on_ground_frame_counter == 0:
+      target_xyz = np.array(self.body_xyz)
+      robot_speed = np.array(self.robot_body.speed())
+      angle = self.np_random.uniform(low=-3.14, high=3.14)
+      from_dist = 4.0
+      attack_speed = self.np_random.uniform(
+          low=20.0, high=30.0)  # speed 20..30 (* mass in cube.urdf = impulse)
+      time_to_travel = from_dist / attack_speed
+      target_xyz += robot_speed * time_to_travel  # predict future position at the moment the cube hits the robot
+      position = [
+          target_xyz[0] + from_dist * np.cos(angle), target_xyz[1] + from_dist * np.sin(angle),
+          target_xyz[2] + 1.0
+      ]
+      attack_speed_vector = target_xyz - np.array(position)
+      attack_speed_vector *= attack_speed / np.linalg.norm(attack_speed_vector)
+      attack_speed_vector += self.np_random.uniform(low=-1.0, high=+1.0, size=(3,))
+      self.aggressive_cube.reset_position(position)
+      self.aggressive_cube.reset_velocity(linearVelocity=attack_speed_vector)
+    if z < 0.8:
+      self.on_ground_frame_counter += 1
+    elif self.on_ground_frame_counter > 0:
+      self.on_ground_frame_counter -= 1
+    # End episode if the robot can't get up in 170 frames, to save computation and decorrelate observations.
+    self.frame += 1
+    return self.potential_leak() if self.on_ground_frame_counter < 170 else -1
+
+  def potential_leak(self):
+    z = self.body_xyz[2]  # 0.00 .. 0.8 .. 1.05 normal walk, 1.2 when jumping
+    z = np.clip(z, 0, 0.8)
+    return z / 0.8 + 1.0  # 1.00 .. 2.0
+
+  def calc_potential(self):
+    # We see alive bonus here as a leak from potential field. Value V(s) of a given state equals
+    # potential, if it is topped up with gamma*potential every frame. Gamma is assumed 0.99.
+    #
+    # 2.0 alive bonus if z>0.8, potential is 200, leak gamma=0.99, (1-0.99)*200==2.0
+    # 1.0 alive bonus on the ground z==0, potential is 100, leak (1-0.99)*100==1.0
+    #
+    # Why robot whould stand up: to receive 100 points in potential field difference.
+    flag_running_progress = Humanoid.calc_potential(self)
+
+    # This disables crawl.
+    if self.body_xyz[2] < 0.8:
+      if self.crawl_start_potential is None:
+        self.crawl_start_potential = flag_running_progress - self.crawl_ignored_potential
+        #print("CRAWL START %+0.1f %+0.1f" % (self.crawl_start_potential, flag_running_progress))
+      self.crawl_ignored_potential = flag_running_progress - self.crawl_start_potential
+      flag_running_progress = self.crawl_start_potential
+    else:
+      #print("CRAWL STOP %+0.1f %+0.1f" % (self.crawl_ignored_potential, flag_running_progress))
+      flag_running_progress -= self.crawl_ignored_potential
+      self.crawl_start_potential = None
+
+    return flag_running_progress + self.potential_leak() * 100
diff --git a/examples/pybullet/gym/pybullet_envs/robot_manipulators.py b/examples/pybullet/gym/pybullet_envs/robot_manipulators.py
index 07e41888e..30f2a3805 100644
--- a/examples/pybullet/gym/pybullet_envs/robot_manipulators.py
+++ b/examples/pybullet/gym/pybullet_envs/robot_manipulators.py
@@ -3,308 +3,317 @@ import numpy as np
 
 
 class Reacher(MJCFBasedRobot):
-	TARG_LIMIT = 0.27
-
-	def __init__(self):
-		MJCFBasedRobot.__init__(self, 'reacher.xml', 'body0', action_dim=2, obs_dim=9)
-
-	def robot_specific_reset(self, bullet_client):
-		self.jdict["target_x"].reset_current_position(
-			self.np_random.uniform(low=-self.TARG_LIMIT, high=self.TARG_LIMIT), 0)
-		self.jdict["target_y"].reset_current_position(
-			self.np_random.uniform(low=-self.TARG_LIMIT, high=self.TARG_LIMIT), 0)
-		self.fingertip = self.parts["fingertip"]
-		self.target = self.parts["target"]
-		self.central_joint = self.jdict["joint0"]
-		self.elbow_joint = self.jdict["joint1"]
-		self.central_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.elbow_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-
-	def apply_action(self, a):
-		assert (np.isfinite(a).all())
-		self.central_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1)))
-		self.elbow_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1)))
-
-	def calc_state(self):
-		theta, self.theta_dot = self.central_joint.current_relative_position()
-		self.gamma, self.gamma_dot = self.elbow_joint.current_relative_position()
-		target_x, _ = self.jdict["target_x"].current_position()
-		target_y, _ = self.jdict["target_y"].current_position()
-		self.to_target_vec = np.array(self.fingertip.pose().xyz()) - np.array(self.target.pose().xyz())
-		return np.array([
-			target_x,
-			target_y,
-			self.to_target_vec[0],
-			self.to_target_vec[1],
-			np.cos(theta),
-			np.sin(theta),
-			self.theta_dot,
-			self.gamma,
-			self.gamma_dot,
-		])
-
-	def calc_potential(self):
-		return -100 * np.linalg.norm(self.to_target_vec)
+  TARG_LIMIT = 0.27
+
+  def __init__(self):
+    MJCFBasedRobot.__init__(self, 'reacher.xml', 'body0', action_dim=2, obs_dim=9)
+
+  def robot_specific_reset(self, bullet_client):
+    self.jdict["target_x"].reset_current_position(
+        self.np_random.uniform(low=-self.TARG_LIMIT, high=self.TARG_LIMIT), 0)
+    self.jdict["target_y"].reset_current_position(
+        self.np_random.uniform(low=-self.TARG_LIMIT, high=self.TARG_LIMIT), 0)
+    self.fingertip = self.parts["fingertip"]
+    self.target = self.parts["target"]
+    self.central_joint = self.jdict["joint0"]
+    self.elbow_joint = self.jdict["joint1"]
+    self.central_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+    self.elbow_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+
+  def apply_action(self, a):
+    assert (np.isfinite(a).all())
+    self.central_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1)))
+    self.elbow_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1)))
+
+  def calc_state(self):
+    theta, self.theta_dot = self.central_joint.current_relative_position()
+    self.gamma, self.gamma_dot = self.elbow_joint.current_relative_position()
+    target_x, _ = self.jdict["target_x"].current_position()
+    target_y, _ = self.jdict["target_y"].current_position()
+    self.to_target_vec = np.array(self.fingertip.pose().xyz()) - np.array(self.target.pose().xyz())
+    return np.array([
+        target_x,
+        target_y,
+        self.to_target_vec[0],
+        self.to_target_vec[1],
+        np.cos(theta),
+        np.sin(theta),
+        self.theta_dot,
+        self.gamma,
+        self.gamma_dot,
+    ])
+
+  def calc_potential(self):
+    return -100 * np.linalg.norm(self.to_target_vec)
 
 
 class Pusher(MJCFBasedRobot):
-	min_target_placement_radius = 0.5
-	max_target_placement_radius = 0.8
-	min_object_to_target_distance = 0.1
-	max_object_to_target_distance = 0.4
-
-	def __init__(self):
-		MJCFBasedRobot.__init__(self, 'pusher.xml', 'body0', action_dim=7, obs_dim=55)
-
-	def robot_specific_reset(self, bullet_client):
-		# parts
-		self.fingertip = self.parts["fingertip"]
-		self.target = self.parts["target"]
-		self.object = self.parts["object"]
-
-		# joints
-		self.shoulder_pan_joint = self.jdict["shoulder_pan_joint"]
-		self.shoulder_lift_joint = self.jdict["shoulder_lift_joint"]
-		self.upper_arm_roll_joint = self.jdict["upper_arm_roll_joint"]
-		self.elbow_flex_joint = self.jdict["elbow_flex_joint"]
-		self.forearm_roll_joint = self.jdict["forearm_roll_joint"]
-		self.wrist_flex_joint = self.jdict["wrist_flex_joint"]
-		self.wrist_roll_joint = self.jdict["wrist_roll_joint"]
-
-		self.target_pos = np.concatenate([
-			self.np_random.uniform(low=-1, high=1, size=1),
-			self.np_random.uniform(low=-1, high=1, size=1)
-		])
-
-		# make length of vector between min and max_target_placement_radius
-		self.target_pos = self.target_pos \
-						  / np.linalg.norm(self.target_pos) \
-						  * self.np_random.uniform(low=self.min_target_placement_radius,
-												   high=self.max_target_placement_radius, size=1)
-
-		self.object_pos = np.concatenate([
-			self.np_random.uniform(low=-1, high=1, size=1),
-			self.np_random.uniform(low=-1, high=1, size=1)
-		])
-
-		# make length of vector between min and max_object_to_target_distance
-		self.object_pos = self.object_pos \
-						  / np.linalg.norm(self.object_pos - self.target_pos) \
-						  * self.np_random.uniform(low=self.min_object_to_target_distance,
-												   high=self.max_object_to_target_distance, size=1)
-
-		# set position of objects
-		self.zero_offset = np.array([0.45, 0.55])
-		self.jdict["target_x"].reset_current_position(self.target_pos[0] - self.zero_offset[0], 0)
-		self.jdict["target_y"].reset_current_position(self.target_pos[1] - self.zero_offset[1], 0)
-		self.jdict["object_x"].reset_current_position(self.object_pos[0] - self.zero_offset[0], 0)
-		self.jdict["object_y"].reset_current_position(self.object_pos[1] - self.zero_offset[1], 0)
-
-		# randomize all joints TODO: Will this work or do we have to constrain this resetting in some way?
-		self.shoulder_pan_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.shoulder_lift_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.upper_arm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.elbow_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.forearm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.wrist_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.wrist_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-
-	def apply_action(self, a):
-		assert (np.isfinite(a).all())
-		self.shoulder_pan_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1)))
-		self.shoulder_lift_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1)))
-		self.upper_arm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[2], -1, +1)))
-		self.elbow_flex_joint.set_motor_torque(0.05 * float(np.clip(a[3], -1, +1)))
-		self.forearm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[4], -1, +1)))
-		self.wrist_flex_joint.set_motor_torque(0.05 * float(np.clip(a[5], -1, +1)))
-		self.wrist_roll_joint.set_motor_torque(0.05 * float(np.clip(a[6], -1, +1)))
-
-	def calc_state(self):
-		self.to_target_vec = self.target_pos - self.object_pos
-		return np.concatenate([
-			np.array([j.current_position() for j in self.ordered_joints]).flatten(),  # all positions
-			np.array([j.current_relative_position() for j in self.ordered_joints]).flatten(),  # all speeds
-			self.to_target_vec,
-			self.fingertip.pose().xyz(),
-			self.object.pose().xyz(),
-			self.target.pose().xyz(),
-		])
+  min_target_placement_radius = 0.5
+  max_target_placement_radius = 0.8
+  min_object_to_target_distance = 0.1
+  max_object_to_target_distance = 0.4
+
+  def __init__(self):
+    MJCFBasedRobot.__init__(self, 'pusher.xml', 'body0', action_dim=7, obs_dim=55)
+
+  def robot_specific_reset(self, bullet_client):
+    # parts
+    self.fingertip = self.parts["fingertip"]
+    self.target = self.parts["target"]
+    self.object = self.parts["object"]
+
+    # joints
+    self.shoulder_pan_joint = self.jdict["shoulder_pan_joint"]
+    self.shoulder_lift_joint = self.jdict["shoulder_lift_joint"]
+    self.upper_arm_roll_joint = self.jdict["upper_arm_roll_joint"]
+    self.elbow_flex_joint = self.jdict["elbow_flex_joint"]
+    self.forearm_roll_joint = self.jdict["forearm_roll_joint"]
+    self.wrist_flex_joint = self.jdict["wrist_flex_joint"]
+    self.wrist_roll_joint = self.jdict["wrist_roll_joint"]
+
+    self.target_pos = np.concatenate([
+        self.np_random.uniform(low=-1, high=1, size=1),
+        self.np_random.uniform(low=-1, high=1, size=1)
+    ])
+
+    # make length of vector between min and max_target_placement_radius
+    self.target_pos = self.target_pos \
+          / np.linalg.norm(self.target_pos) \
+          * self.np_random.uniform(low=self.min_target_placement_radius,
+                 high=self.max_target_placement_radius, size=1)
+
+    self.object_pos = np.concatenate([
+        self.np_random.uniform(low=-1, high=1, size=1),
+        self.np_random.uniform(low=-1, high=1, size=1)
+    ])
+
+    # make length of vector between min and max_object_to_target_distance
+    self.object_pos = self.object_pos \
+          / np.linalg.norm(self.object_pos - self.target_pos) \
+          * self.np_random.uniform(low=self.min_object_to_target_distance,
+                 high=self.max_object_to_target_distance, size=1)
+
+    # set position of objects
+    self.zero_offset = np.array([0.45, 0.55])
+    self.jdict["target_x"].reset_current_position(self.target_pos[0] - self.zero_offset[0], 0)
+    self.jdict["target_y"].reset_current_position(self.target_pos[1] - self.zero_offset[1], 0)
+    self.jdict["object_x"].reset_current_position(self.object_pos[0] - self.zero_offset[0], 0)
+    self.jdict["object_y"].reset_current_position(self.object_pos[1] - self.zero_offset[1], 0)
+
+    # randomize all joints TODO: Will this work or do we have to constrain this resetting in some way?
+    self.shoulder_pan_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+    self.shoulder_lift_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14),
+                                                    0)
+    self.upper_arm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14),
+                                                     0)
+    self.elbow_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+    self.forearm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+    self.wrist_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+    self.wrist_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+
+  def apply_action(self, a):
+    assert (np.isfinite(a).all())
+    self.shoulder_pan_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1)))
+    self.shoulder_lift_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1)))
+    self.upper_arm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[2], -1, +1)))
+    self.elbow_flex_joint.set_motor_torque(0.05 * float(np.clip(a[3], -1, +1)))
+    self.forearm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[4], -1, +1)))
+    self.wrist_flex_joint.set_motor_torque(0.05 * float(np.clip(a[5], -1, +1)))
+    self.wrist_roll_joint.set_motor_torque(0.05 * float(np.clip(a[6], -1, +1)))
+
+  def calc_state(self):
+    self.to_target_vec = self.target_pos - self.object_pos
+    return np.concatenate([
+        np.array([j.current_position() for j in self.ordered_joints]).flatten(),  # all positions
+        np.array([j.current_relative_position() for j in self.ordered_joints
+                 ]).flatten(),  # all speeds
+        self.to_target_vec,
+        self.fingertip.pose().xyz(),
+        self.object.pose().xyz(),
+        self.target.pose().xyz(),
+    ])
 
 
 class Striker(MJCFBasedRobot):
-	min_target_placement_radius = 0.1
-	max_target_placement_radius = 0.8
-	min_object_placement_radius = 0.1
-	max_object_placement_radius = 0.8
-
-	def __init__(self):
-		MJCFBasedRobot.__init__(self, 'striker.xml', 'body0', action_dim=7, obs_dim=55)
-
-	def robot_specific_reset(self, bullet_client):
-		# parts
-		self.fingertip = self.parts["fingertip"]
-		self.target = self.parts["target"]
-		self.object = self.parts["object"]
-
-		# joints
-		self.shoulder_pan_joint = self.jdict["shoulder_pan_joint"]
-		self.shoulder_lift_joint = self.jdict["shoulder_lift_joint"]
-		self.upper_arm_roll_joint = self.jdict["upper_arm_roll_joint"]
-		self.elbow_flex_joint = self.jdict["elbow_flex_joint"]
-		self.forearm_roll_joint = self.jdict["forearm_roll_joint"]
-		self.wrist_flex_joint = self.jdict["wrist_flex_joint"]
-		self.wrist_roll_joint = self.jdict["wrist_roll_joint"]
-
-		self._min_strike_dist = np.inf
-		self._striked = False
-		self._strike_pos = None
-
-		# reset position and speed of manipulator
-		# TODO: Will this work or do we have to constrain this resetting in some way?
-		self.shoulder_pan_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.shoulder_lift_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.upper_arm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.elbow_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.forearm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.wrist_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.wrist_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-
-		self.zero_offset = np.array([0.45, 0.55, 0])
-		self.object_pos = np.concatenate([
-			self.np_random.uniform(low=-1, high=1, size=1),
-			self.np_random.uniform(low=-1, high=1, size=1)
-		])
-
-		# make length of vector between min and max_object_placement_radius
-		self.object_pos = self.object_pos \
-						  / np.linalg.norm(self.object_pos) \
-						  * self.np_random.uniform(low=self.min_object_placement_radius,
-												   high=self.max_object_placement_radius, size=1)
-
-		# reset object position
-		self.jdict["object_x"].reset_current_position(self.object_pos[0] - self.zero_offset[0], 0)
-		self.jdict["object_y"].reset_current_position(self.object_pos[1] - self.zero_offset[1], 0)
-
-		self.target_pos = np.concatenate([
-			self.np_random.uniform(low=-1, high=1, size=1),
-			self.np_random.uniform(low=-1, high=1, size=1),
-			self.np_random.uniform(low=-1, high=1, size=1)
-		])
-
-		# make length of vector between min and max_target_placement_radius
-		self.target_pos = self.target_pos \
-						  / np.linalg.norm(self.target_pos) \
-						  * self.np_random.uniform(low=self.min_target_placement_radius,
-												   high=self.max_target_placement_radius, size=1)
-
-		self.target.reset_pose(self.target_pos - self.zero_offset, np.array([0, 0, 0, 1]))
-
-	def apply_action(self, a):
-		assert (np.isfinite(a).all())
-		self.shoulder_pan_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1)))
-		self.shoulder_lift_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1)))
-		self.upper_arm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[2], -1, +1)))
-		self.elbow_flex_joint.set_motor_torque(0.05 * float(np.clip(a[3], -1, +1)))
-		self.forearm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[4], -1, +1)))
-		self.wrist_flex_joint.set_motor_torque(0.05 * float(np.clip(a[5], -1, +1)))
-		self.wrist_roll_joint.set_motor_torque(0.05 * float(np.clip(a[6], -1, +1)))
-
-	def calc_state(self):
-		self.to_target_vec = self.target_pos - self.object_pos
-		return np.concatenate([
-			np.array([j.current_position() for j in self.ordered_joints]).flatten(),  # all positions
-			np.array([j.current_relative_position() for j in self.ordered_joints]).flatten(),  # all speeds
-			self.to_target_vec,
-			self.fingertip.pose().xyz(),
-			self.object.pose().xyz(),
-			self.target.pose().xyz(),
-		])
+  min_target_placement_radius = 0.1
+  max_target_placement_radius = 0.8
+  min_object_placement_radius = 0.1
+  max_object_placement_radius = 0.8
+
+  def __init__(self):
+    MJCFBasedRobot.__init__(self, 'striker.xml', 'body0', action_dim=7, obs_dim=55)
+
+  def robot_specific_reset(self, bullet_client):
+    # parts
+    self.fingertip = self.parts["fingertip"]
+    self.target = self.parts["target"]
+    self.object = self.parts["object"]
+
+    # joints
+    self.shoulder_pan_joint = self.jdict["shoulder_pan_joint"]
+    self.shoulder_lift_joint = self.jdict["shoulder_lift_joint"]
+    self.upper_arm_roll_joint = self.jdict["upper_arm_roll_joint"]
+    self.elbow_flex_joint = self.jdict["elbow_flex_joint"]
+    self.forearm_roll_joint = self.jdict["forearm_roll_joint"]
+    self.wrist_flex_joint = self.jdict["wrist_flex_joint"]
+    self.wrist_roll_joint = self.jdict["wrist_roll_joint"]
+
+    self._min_strike_dist = np.inf
+    self._striked = False
+    self._strike_pos = None
+
+    # reset position and speed of manipulator
+    # TODO: Will this work or do we have to constrain this resetting in some way?
+    self.shoulder_pan_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+    self.shoulder_lift_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14),
+                                                    0)
+    self.upper_arm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14),
+                                                     0)
+    self.elbow_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+    self.forearm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+    self.wrist_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+    self.wrist_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+
+    self.zero_offset = np.array([0.45, 0.55, 0])
+    self.object_pos = np.concatenate([
+        self.np_random.uniform(low=-1, high=1, size=1),
+        self.np_random.uniform(low=-1, high=1, size=1)
+    ])
+
+    # make length of vector between min and max_object_placement_radius
+    self.object_pos = self.object_pos \
+          / np.linalg.norm(self.object_pos) \
+          * self.np_random.uniform(low=self.min_object_placement_radius,
+                 high=self.max_object_placement_radius, size=1)
+
+    # reset object position
+    self.jdict["object_x"].reset_current_position(self.object_pos[0] - self.zero_offset[0], 0)
+    self.jdict["object_y"].reset_current_position(self.object_pos[1] - self.zero_offset[1], 0)
+
+    self.target_pos = np.concatenate([
+        self.np_random.uniform(low=-1, high=1, size=1),
+        self.np_random.uniform(low=-1, high=1, size=1),
+        self.np_random.uniform(low=-1, high=1, size=1)
+    ])
+
+    # make length of vector between min and max_target_placement_radius
+    self.target_pos = self.target_pos \
+          / np.linalg.norm(self.target_pos) \
+          * self.np_random.uniform(low=self.min_target_placement_radius,
+                 high=self.max_target_placement_radius, size=1)
+
+    self.target.reset_pose(self.target_pos - self.zero_offset, np.array([0, 0, 0, 1]))
+
+  def apply_action(self, a):
+    assert (np.isfinite(a).all())
+    self.shoulder_pan_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1)))
+    self.shoulder_lift_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1)))
+    self.upper_arm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[2], -1, +1)))
+    self.elbow_flex_joint.set_motor_torque(0.05 * float(np.clip(a[3], -1, +1)))
+    self.forearm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[4], -1, +1)))
+    self.wrist_flex_joint.set_motor_torque(0.05 * float(np.clip(a[5], -1, +1)))
+    self.wrist_roll_joint.set_motor_torque(0.05 * float(np.clip(a[6], -1, +1)))
+
+  def calc_state(self):
+    self.to_target_vec = self.target_pos - self.object_pos
+    return np.concatenate([
+        np.array([j.current_position() for j in self.ordered_joints]).flatten(),  # all positions
+        np.array([j.current_relative_position() for j in self.ordered_joints
+                 ]).flatten(),  # all speeds
+        self.to_target_vec,
+        self.fingertip.pose().xyz(),
+        self.object.pose().xyz(),
+        self.target.pose().xyz(),
+    ])
 
 
 class Thrower(MJCFBasedRobot):
-	min_target_placement_radius = 0.1
-	max_target_placement_radius = 0.8
-	min_object_placement_radius = 0.1
-	max_object_placement_radius = 0.8
-
-	def __init__(self):
-		MJCFBasedRobot.__init__(self, 'thrower.xml', 'body0', action_dim=7, obs_dim=48)
-
-	def robot_specific_reset(self, bullet_client):
-		# parts
-		self.fingertip = self.parts["fingertip"]
-		self.target = self.parts["target"]
-		self.object = self.parts["object"]
-
-		# joints
-		self.shoulder_pan_joint = self.jdict["shoulder_pan_joint"]
-		self.shoulder_lift_joint = self.jdict["shoulder_lift_joint"]
-		self.upper_arm_roll_joint = self.jdict["upper_arm_roll_joint"]
-		self.elbow_flex_joint = self.jdict["elbow_flex_joint"]
-		self.forearm_roll_joint = self.jdict["forearm_roll_joint"]
-		self.wrist_flex_joint = self.jdict["wrist_flex_joint"]
-		self.wrist_roll_joint = self.jdict["wrist_roll_joint"]
-
-		self._object_hit_ground = False
-		self._object_hit_location = None
-
-		# reset position and speed of manipulator
-		# TODO: Will this work or do we have to constrain this resetting in some way?
-		self.shoulder_pan_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.shoulder_lift_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.upper_arm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.elbow_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.forearm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.wrist_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-		self.wrist_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
-
-		self.zero_offset = np.array([0.45, 0.55, 0])
-		self.object_pos = np.concatenate([
-			self.np_random.uniform(low=-1, high=1, size=1),
-			self.np_random.uniform(low=-1, high=1, size=1),
-			self.np_random.uniform(low=-1, high=1, size=1)
-		])
-
-		# make length of vector between min and max_object_placement_radius
-		self.object_pos = self.object_pos \
-						  / np.linalg.norm(self.object_pos) \
-						  * self.np_random.uniform(low=self.min_object_placement_radius,
-												   high=self.max_object_placement_radius, size=1)
-
-		# reset object position
-		self.parts["object"].reset_pose(self.object_pos - self.zero_offset, np.array([0, 0, 0, 1]))
-
-		self.target_pos = np.concatenate([
-			self.np_random.uniform(low=-1, high=1, size=1),
-			self.np_random.uniform(low=-1, high=1, size=1),
-			self.np_random.uniform(low=-1, high=1, size=1)
-		])
-
-		# make length of vector between min and max_target_placement_radius
-		self.target_pos = self.target_pos \
-						  / np.linalg.norm(self.target_pos) \
-						  * self.np_random.uniform(low=self.min_target_placement_radius,
-												   high=self.max_target_placement_radius, size=1)
-
-		self.parts["target"].reset_pose(self.target_pos - self.zero_offset, np.array([0, 0, 0, 1]))
-
-	def apply_action(self, a):
-		assert (np.isfinite(a).all())
-		self.shoulder_pan_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1)))
-		self.shoulder_lift_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1)))
-		self.upper_arm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[2], -1, +1)))
-		self.elbow_flex_joint.set_motor_torque(0.05 * float(np.clip(a[3], -1, +1)))
-		self.forearm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[4], -1, +1)))
-		self.wrist_flex_joint.set_motor_torque(0.05 * float(np.clip(a[5], -1, +1)))
-		self.wrist_roll_joint.set_motor_torque(0.05 * float(np.clip(a[6], -1, +1)))
-
-	def calc_state(self):
-		self.to_target_vec = self.target_pos - self.object_pos
-		return np.concatenate([
-			np.array([j.current_position() for j in self.ordered_joints]).flatten(),  # all positions
-			np.array([j.current_relative_position() for j in self.ordered_joints]).flatten(),  # all speeds
-			self.to_target_vec,
-			self.fingertip.pose().xyz(),
-			self.object.pose().xyz(),
-			self.target.pose().xyz(),
-		])
+  min_target_placement_radius = 0.1
+  max_target_placement_radius = 0.8
+  min_object_placement_radius = 0.1
+  max_object_placement_radius = 0.8
+
+  def __init__(self):
+    MJCFBasedRobot.__init__(self, 'thrower.xml', 'body0', action_dim=7, obs_dim=48)
+
+  def robot_specific_reset(self, bullet_client):
+    # parts
+    self.fingertip = self.parts["fingertip"]
+    self.target = self.parts["target"]
+    self.object = self.parts["object"]
+
+    # joints
+    self.shoulder_pan_joint = self.jdict["shoulder_pan_joint"]
+    self.shoulder_lift_joint = self.jdict["shoulder_lift_joint"]
+    self.upper_arm_roll_joint = self.jdict["upper_arm_roll_joint"]
+    self.elbow_flex_joint = self.jdict["elbow_flex_joint"]
+    self.forearm_roll_joint = self.jdict["forearm_roll_joint"]
+    self.wrist_flex_joint = self.jdict["wrist_flex_joint"]
+    self.wrist_roll_joint = self.jdict["wrist_roll_joint"]
+
+    self._object_hit_ground = False
+    self._object_hit_location = None
+
+    # reset position and speed of manipulator
+    # TODO: Will this work or do we have to constrain this resetting in some way?
+    self.shoulder_pan_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+    self.shoulder_lift_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14),
+                                                    0)
+    self.upper_arm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14),
+                                                     0)
+    self.elbow_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+    self.forearm_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+    self.wrist_flex_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+    self.wrist_roll_joint.reset_current_position(self.np_random.uniform(low=-3.14, high=3.14), 0)
+
+    self.zero_offset = np.array([0.45, 0.55, 0])
+    self.object_pos = np.concatenate([
+        self.np_random.uniform(low=-1, high=1, size=1),
+        self.np_random.uniform(low=-1, high=1, size=1),
+        self.np_random.uniform(low=-1, high=1, size=1)
+    ])
+
+    # make length of vector between min and max_object_placement_radius
+    self.object_pos = self.object_pos \
+          / np.linalg.norm(self.object_pos) \
+          * self.np_random.uniform(low=self.min_object_placement_radius,
+                 high=self.max_object_placement_radius, size=1)
+
+    # reset object position
+    self.parts["object"].reset_pose(self.object_pos - self.zero_offset, np.array([0, 0, 0, 1]))
+
+    self.target_pos = np.concatenate([
+        self.np_random.uniform(low=-1, high=1, size=1),
+        self.np_random.uniform(low=-1, high=1, size=1),
+        self.np_random.uniform(low=-1, high=1, size=1)
+    ])
+
+    # make length of vector between min and max_target_placement_radius
+    self.target_pos = self.target_pos \
+          / np.linalg.norm(self.target_pos) \
+          * self.np_random.uniform(low=self.min_target_placement_radius,
+                 high=self.max_target_placement_radius, size=1)
+
+    self.parts["target"].reset_pose(self.target_pos - self.zero_offset, np.array([0, 0, 0, 1]))
+
+  def apply_action(self, a):
+    assert (np.isfinite(a).all())
+    self.shoulder_pan_joint.set_motor_torque(0.05 * float(np.clip(a[0], -1, +1)))
+    self.shoulder_lift_joint.set_motor_torque(0.05 * float(np.clip(a[1], -1, +1)))
+    self.upper_arm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[2], -1, +1)))
+    self.elbow_flex_joint.set_motor_torque(0.05 * float(np.clip(a[3], -1, +1)))
+    self.forearm_roll_joint.set_motor_torque(0.05 * float(np.clip(a[4], -1, +1)))
+    self.wrist_flex_joint.set_motor_torque(0.05 * float(np.clip(a[5], -1, +1)))
+    self.wrist_roll_joint.set_motor_torque(0.05 * float(np.clip(a[6], -1, +1)))
+
+  def calc_state(self):
+    self.to_target_vec = self.target_pos - self.object_pos
+    return np.concatenate([
+        np.array([j.current_position() for j in self.ordered_joints]).flatten(),  # all positions
+        np.array([j.current_relative_position() for j in self.ordered_joints
+                 ]).flatten(),  # all speeds
+        self.to_target_vec,
+        self.fingertip.pose().xyz(),
+        self.object.pose().xyz(),
+        self.target.pose().xyz(),
+    ])
diff --git a/examples/pybullet/gym/pybullet_envs/robot_pendula.py b/examples/pybullet/gym/pybullet_envs/robot_pendula.py
index a90728128..924d84292 100644
--- a/examples/pybullet/gym/pybullet_envs/robot_pendula.py
+++ b/examples/pybullet/gym/pybullet_envs/robot_pendula.py
@@ -1,86 +1,92 @@
 from robot_bases import MJCFBasedRobot
 import numpy as np
 
+
 class InvertedPendulum(MJCFBasedRobot):
-	swingup = False
-	def __init__(self):
-		MJCFBasedRobot.__init__(self, 'inverted_pendulum.xml', 'cart', action_dim=1, obs_dim=5)
-
-	def robot_specific_reset(self, bullet_client):
-		self._p = bullet_client
-		self.pole = self.parts["pole"]
-		self.slider = self.jdict["slider"]
-		self.j1 = self.jdict["hinge"]
-		u = self.np_random.uniform(low=-.1, high=.1)
-		self.j1.reset_current_position( u if not self.swingup else 3.1415+u , 0)
-		self.j1.set_motor_torque(0)
-
-	def apply_action(self, a):
-		assert( np.isfinite(a).all() )
-		if not np.isfinite(a).all():
-			print("a is inf")
-			a[0] = 0
-		self.slider.set_motor_torque(  100*float(np.clip(a[0], -1, +1)) )
-
-	def calc_state(self):
-		self.theta, theta_dot = self.j1.current_position()
-		x, vx = self.slider.current_position()
-		assert( np.isfinite(x) )
-
-		if not np.isfinite(x):
-			print("x is inf")
-			x = 0
-
-		if not np.isfinite(vx):
-			print("vx is inf")
-			vx = 0
-
-		if not np.isfinite(self.theta):
-			print("theta is inf")
-			self.theta = 0
-
-		if not np.isfinite(theta_dot):
-			print("theta_dot is inf")
-			theta_dot = 0
-
-		return np.array([
-			x, vx,
-			np.cos(self.theta), np.sin(self.theta), theta_dot
-			])
+  swingup = False
+
+  def __init__(self):
+    MJCFBasedRobot.__init__(self, 'inverted_pendulum.xml', 'cart', action_dim=1, obs_dim=5)
+
+  def robot_specific_reset(self, bullet_client):
+    self._p = bullet_client
+    self.pole = self.parts["pole"]
+    self.slider = self.jdict["slider"]
+    self.j1 = self.jdict["hinge"]
+    u = self.np_random.uniform(low=-.1, high=.1)
+    self.j1.reset_current_position(u if not self.swingup else 3.1415 + u, 0)
+    self.j1.set_motor_torque(0)
+
+  def apply_action(self, a):
+    assert (np.isfinite(a).all())
+    if not np.isfinite(a).all():
+      print("a is inf")
+      a[0] = 0
+    self.slider.set_motor_torque(100 * float(np.clip(a[0], -1, +1)))
+
+  def calc_state(self):
+    self.theta, theta_dot = self.j1.current_position()
+    x, vx = self.slider.current_position()
+    assert (np.isfinite(x))
+
+    if not np.isfinite(x):
+      print("x is inf")
+      x = 0
+
+    if not np.isfinite(vx):
+      print("vx is inf")
+      vx = 0
+
+    if not np.isfinite(self.theta):
+      print("theta is inf")
+      self.theta = 0
+
+    if not np.isfinite(theta_dot):
+      print("theta_dot is inf")
+      theta_dot = 0
+
+    return np.array([x, vx, np.cos(self.theta), np.sin(self.theta), theta_dot])
+
 
 class InvertedPendulumSwingup(InvertedPendulum):
-	swingup = True
+  swingup = True
 
 
 class InvertedDoublePendulum(MJCFBasedRobot):
-	def __init__(self):
-		MJCFBasedRobot.__init__(self,  'inverted_double_pendulum.xml', 'cart', action_dim=1, obs_dim=9)
-
-	def robot_specific_reset(self, bullet_client):
-		self._p = bullet_client
-		self.pole2 = self.parts["pole2"]
-		self.slider = self.jdict["slider"]
-		self.j1 = self.jdict["hinge"]
-		self.j2 = self.jdict["hinge2"]
-		u = self.np_random.uniform(low=-.1, high=.1, size=[2])
-		self.j1.reset_current_position(float(u[0]), 0)
-		self.j2.reset_current_position(float(u[1]), 0)
-		self.j1.set_motor_torque(0)
-		self.j2.set_motor_torque(0)
-
-	def apply_action(self, a):
-		assert( np.isfinite(a).all() )
-		self.slider.set_motor_torque( 200*float(np.clip(a[0], -1, +1)) )
-
-	def calc_state(self):
-		theta, theta_dot = self.j1.current_position()
-		gamma, gamma_dot = self.j2.current_position()
-		x, vx = self.slider.current_position()
-		self.pos_x, _, self.pos_y = self.pole2.pose().xyz()
-		assert( np.isfinite(x) )
-		return np.array([
-			x, vx,
-			self.pos_x,
-			np.cos(theta), np.sin(theta), theta_dot,
-			np.cos(gamma), np.sin(gamma), gamma_dot,
-			])
+
+  def __init__(self):
+    MJCFBasedRobot.__init__(self, 'inverted_double_pendulum.xml', 'cart', action_dim=1, obs_dim=9)
+
+  def robot_specific_reset(self, bullet_client):
+    self._p = bullet_client
+    self.pole2 = self.parts["pole2"]
+    self.slider = self.jdict["slider"]
+    self.j1 = self.jdict["hinge"]
+    self.j2 = self.jdict["hinge2"]
+    u = self.np_random.uniform(low=-.1, high=.1, size=[2])
+    self.j1.reset_current_position(float(u[0]), 0)
+    self.j2.reset_current_position(float(u[1]), 0)
+    self.j1.set_motor_torque(0)
+    self.j2.set_motor_torque(0)
+
+  def apply_action(self, a):
+    assert (np.isfinite(a).all())
+    self.slider.set_motor_torque(200 * float(np.clip(a[0], -1, +1)))
+
+  def calc_state(self):
+    theta, theta_dot = self.j1.current_position()
+    gamma, gamma_dot = self.j2.current_position()
+    x, vx = self.slider.current_position()
+    self.pos_x, _, self.pos_y = self.pole2.pose().xyz()
+    assert (np.isfinite(x))
+    return np.array([
+        x,
+        vx,
+        self.pos_x,
+        np.cos(theta),
+        np.sin(theta),
+        theta_dot,
+        np.cos(gamma),
+        np.sin(gamma),
+        gamma_dot,
+    ])
diff --git a/examples/pybullet/gym/pybullet_envs/scene_abstract.py b/examples/pybullet/gym/pybullet_envs/scene_abstract.py
index b50dd524b..f4bbfae0c 100644
--- a/examples/pybullet/gym/pybullet_envs/scene_abstract.py
+++ b/examples/pybullet/gym/pybullet_envs/scene_abstract.py
@@ -1,78 +1,79 @@
 import sys, os
 sys.path.append(os.path.dirname(__file__))
-import pybullet 
+import pybullet
 
 import gym
 
 
 class Scene:
-    "A base class for single- and multiplayer scenes"
+  "A base class for single- and multiplayer scenes"
 
-    def __init__(self, bullet_client, gravity, timestep, frame_skip):
-        self._p = bullet_client
-        self.np_random, seed = gym.utils.seeding.np_random(None)
-        self.timestep = timestep
-        self.frame_skip = frame_skip
+  def __init__(self, bullet_client, gravity, timestep, frame_skip):
+    self._p = bullet_client
+    self.np_random, seed = gym.utils.seeding.np_random(None)
+    self.timestep = timestep
+    self.frame_skip = frame_skip
 
-        self.dt = self.timestep * self.frame_skip
-        self.cpp_world = World(self._p, gravity, timestep, frame_skip)
+    self.dt = self.timestep * self.frame_skip
+    self.cpp_world = World(self._p, gravity, timestep, frame_skip)
 
-        self.test_window_still_open = True  # or never opened
-        self.human_render_detected = False  # if user wants render("human"), we open test window
+    self.test_window_still_open = True  # or never opened
+    self.human_render_detected = False  # if user wants render("human"), we open test window
 
-        self.multiplayer_robots = {}
+    self.multiplayer_robots = {}
 
-    def test_window(self):
-        "Call this function every frame, to see what's going on. Not necessary in learning."
-        self.human_render_detected = True
-        return self.test_window_still_open
+  def test_window(self):
+    "Call this function every frame, to see what's going on. Not necessary in learning."
+    self.human_render_detected = True
+    return self.test_window_still_open
 
-    def actor_introduce(self, robot):
-        "Usually after scene reset"
-        if not self.multiplayer: return
-        self.multiplayer_robots[robot.player_n] = robot
+  def actor_introduce(self, robot):
+    "Usually after scene reset"
+    if not self.multiplayer: return
+    self.multiplayer_robots[robot.player_n] = robot
 
-    def actor_is_active(self, robot):
-        """
+  def actor_is_active(self, robot):
+    """
         Used by robots to see if they are free to exclusiveley put their HUD on the test window.
         Later can be used for click-focus robots.
         """
-        return not self.multiplayer
+    return not self.multiplayer
 
-    def episode_restart(self, bullet_client):
-        "This function gets overridden by specific scene, to reset specific objects into their start positions"
-        self.cpp_world.clean_everything()
-        #self.cpp_world.test_window_history_reset()
+  def episode_restart(self, bullet_client):
+    "This function gets overridden by specific scene, to reset specific objects into their start positions"
+    self.cpp_world.clean_everything()
+    #self.cpp_world.test_window_history_reset()
 
-    def global_step(self):
-        """
+  def global_step(self):
+    """
         The idea is: apply motor torques for all robots, then call global_step(), then collect
         observations from robots using step() with the same action.
         """
-        self.cpp_world.step(self.frame_skip)
+    self.cpp_world.step(self.frame_skip)
 
-class SingleRobotEmptyScene(Scene):
-    multiplayer = False  # this class is used "as is" for InvertedPendulum, Reacher
 
-class World:
+class SingleRobotEmptyScene(Scene):
+  multiplayer = False  # this class is used "as is" for InvertedPendulum, Reacher
 
-	def __init__(self, bullet_client, gravity, timestep, frame_skip):
-		self._p = bullet_client
-		self.gravity = gravity
-		self.timestep = timestep
-		self.frame_skip = frame_skip
-		self.numSolverIterations = 5
-		self.clean_everything()
-		
-		
-	def clean_everything(self):
-		#p.resetSimulation()
-		self._p.setGravity(0, 0, -self.gravity)
-		self._p.setDefaultContactERP(0.9)
-		#print("self.numSolverIterations=",self.numSolverIterations)
-		self._p.setPhysicsEngineParameter(fixedTimeStep=self.timestep*self.frame_skip, numSolverIterations=self.numSolverIterations, numSubSteps=self.frame_skip)
-
-	def step(self, frame_skip):
-		self._p.stepSimulation()
 
+class World:
 
+  def __init__(self, bullet_client, gravity, timestep, frame_skip):
+    self._p = bullet_client
+    self.gravity = gravity
+    self.timestep = timestep
+    self.frame_skip = frame_skip
+    self.numSolverIterations = 5
+    self.clean_everything()
+
+  def clean_everything(self):
+    #p.resetSimulation()
+    self._p.setGravity(0, 0, -self.gravity)
+    self._p.setDefaultContactERP(0.9)
+    #print("self.numSolverIterations=",self.numSolverIterations)
+    self._p.setPhysicsEngineParameter(fixedTimeStep=self.timestep * self.frame_skip,
+                                      numSolverIterations=self.numSolverIterations,
+                                      numSubSteps=self.frame_skip)
+
+  def step(self, frame_skip):
+    self._p.stepSimulation()
diff --git a/examples/pybullet/gym/pybullet_envs/scene_stadium.py b/examples/pybullet/gym/pybullet_envs/scene_stadium.py
index c105983db..0ab34f202 100644
--- a/examples/pybullet/gym/pybullet_envs/scene_stadium.py
+++ b/examples/pybullet/gym/pybullet_envs/scene_stadium.py
@@ -1,52 +1,54 @@
-import os,  inspect
+import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(currentdir)
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 import pybullet_data
 
-
 from pybullet_envs.scene_abstract import Scene
-import pybullet 
+import pybullet
 
 
 class StadiumScene(Scene):
-	zero_at_running_strip_start_line = True   # if False, center of coordinates (0,0,0) will be at the middle of the stadium
-	stadium_halflen   = 105*0.25	# FOOBALL_FIELD_HALFLEN
-	stadium_halfwidth = 50*0.25	 # FOOBALL_FIELD_HALFWID
-	stadiumLoaded=0
-	
-	def episode_restart(self, bullet_client):
-		self._p = bullet_client
-		Scene.episode_restart(self, bullet_client)   # contains cpp_world.clean_everything()
-		if (self.stadiumLoaded==0):
-			self.stadiumLoaded=1
-			
-			# stadium_pose = cpp_household.Pose()
-			# if self.zero_at_running_strip_start_line:
-			#	 stadium_pose.set_xyz(27, 21, 0)  # see RUN_STARTLINE, RUN_RAD constants
-			
-			filename = os.path.join(pybullet_data.getDataPath(),"plane_stadium.sdf")
-			self.ground_plane_mjcf=self._p.loadSDF(filename)
-			#filename = os.path.join(pybullet_data.getDataPath(),"stadium_no_collision.sdf")
-			#self.ground_plane_mjcf = self._p.loadSDF(filename)
-			#	
-			for i in self.ground_plane_mjcf:
-				self._p.changeDynamics(i,-1,lateralFriction=0.8, restitution=0.5)
-				self._p.changeVisualShape(i,-1,rgbaColor=[1,1,1,0.8])
-				self._p.configureDebugVisualizer(pybullet.COV_ENABLE_PLANAR_REFLECTION,1)
-
-			#	for j in range(p.getNumJoints(i)):
-			#		self._p.changeDynamics(i,j,lateralFriction=0)
-			#despite the name (stadium_no_collision), it DID have collision, so don't add duplicate ground
-	
+  zero_at_running_strip_start_line = True  # if False, center of coordinates (0,0,0) will be at the middle of the stadium
+  stadium_halflen = 105 * 0.25  # FOOBALL_FIELD_HALFLEN
+  stadium_halfwidth = 50 * 0.25  # FOOBALL_FIELD_HALFWID
+  stadiumLoaded = 0
+
+  def episode_restart(self, bullet_client):
+    self._p = bullet_client
+    Scene.episode_restart(self, bullet_client)  # contains cpp_world.clean_everything()
+    if (self.stadiumLoaded == 0):
+      self.stadiumLoaded = 1
+
+      # stadium_pose = cpp_household.Pose()
+      # if self.zero_at_running_strip_start_line:
+      #	 stadium_pose.set_xyz(27, 21, 0)  # see RUN_STARTLINE, RUN_RAD constants
+
+      filename = os.path.join(pybullet_data.getDataPath(), "plane_stadium.sdf")
+      self.ground_plane_mjcf = self._p.loadSDF(filename)
+      #filename = os.path.join(pybullet_data.getDataPath(),"stadium_no_collision.sdf")
+      #self.ground_plane_mjcf = self._p.loadSDF(filename)
+      #
+      for i in self.ground_plane_mjcf:
+        self._p.changeDynamics(i, -1, lateralFriction=0.8, restitution=0.5)
+        self._p.changeVisualShape(i, -1, rgbaColor=[1, 1, 1, 0.8])
+        self._p.configureDebugVisualizer(pybullet.COV_ENABLE_PLANAR_REFLECTION, 1)
+
+      #	for j in range(p.getNumJoints(i)):
+      #		self._p.changeDynamics(i,j,lateralFriction=0)
+      #despite the name (stadium_no_collision), it DID have collision, so don't add duplicate ground
+
+
 class SinglePlayerStadiumScene(StadiumScene):
-	"This scene created by environment, to work in a way as if there was no concept of scene visible to user."
-	multiplayer = False
+  "This scene created by environment, to work in a way as if there was no concept of scene visible to user."
+  multiplayer = False
+
 
 class MultiplayerStadiumScene(StadiumScene):
-	multiplayer = True
-	players_count = 3
-	def actor_introduce(self, robot):
-		StadiumScene.actor_introduce(self, robot)
-		i = robot.player_n - 1  # 0 1 2 => -1 0 +1
-		robot.move_robot(0, i, 0)
+  multiplayer = True
+  players_count = 3
+
+  def actor_introduce(self, robot):
+    StadiumScene.actor_introduce(self, robot)
+    i = robot.player_n - 1  # 0 1 2 => -1 0 +1
+    robot.move_robot(0, i, 0)
diff --git a/examples/pybullet/gym/pybullet_utils/arg_parser.py b/examples/pybullet/gym/pybullet_utils/arg_parser.py
index 5461a5bf2..00f4cdde9 100644
--- a/examples/pybullet/gym/pybullet_utils/arg_parser.py
+++ b/examples/pybullet/gym/pybullet_utils/arg_parser.py
@@ -1,124 +1,124 @@
 import re as RE
 
+
 class ArgParser(object):
-    global_parser = None
-
-    def __init__(self):
-        self._table = dict()
-        return
-
-    def clear(self):
-        self._table.clear()
-        return
-
-    def load_args(self, arg_strs):
-        succ = True
-        vals = []
-        curr_key = ''
-
-        for str in arg_strs:
-            if not (self._is_comment(str)):
-                is_key = self._is_key(str)
-                if (is_key):
-                    if (curr_key != ''):
-                        if (curr_key not in self._table):
-                            self._table[curr_key] = vals
-
-                    vals = []
-                    curr_key = str[2::]
-                else:
-                    vals.append(str)
-
-        if (curr_key != ''):
+  global_parser = None
+
+  def __init__(self):
+    self._table = dict()
+    return
+
+  def clear(self):
+    self._table.clear()
+    return
+
+  def load_args(self, arg_strs):
+    succ = True
+    vals = []
+    curr_key = ''
+
+    for str in arg_strs:
+      if not (self._is_comment(str)):
+        is_key = self._is_key(str)
+        if (is_key):
+          if (curr_key != ''):
             if (curr_key not in self._table):
-                self._table[curr_key] = vals
-
-            vals = []
-
-        return succ
-
-    def load_file(self, filename):
-        succ = False
-        with open(filename, 'r') as file:
-            lines = RE.split(r'[\n\r]+', file.read())
-            file.close()
-
-            arg_strs = []
-            for line in lines:
-                if (len(line) > 0 and not self._is_comment(line)):
-                    arg_strs += line.split()
-
-            succ = self.load_args(arg_strs)
-        return succ
-
-    def has_key(self, key):
-        return key in self._table
-
-    def parse_string(self, key, default=''):
-        str = default
-        if self.has_key(key):
-            str = self._table[key][0]
-        return str
-
-    def parse_strings(self, key, default=[]):
-        arr = default
-        if self.has_key(key):
-            arr = self._table[key]
-        return arr
-
-    def parse_int(self, key, default=0):
-        val = default
-        if self.has_key(key):
-            val = int(self._table[key][0])
-        return val
-
-    def parse_ints(self, key, default=[]):
-        arr = default
-        if self.has_key(key):
-            arr = [int(str) for str in self._table[key]]
-        return arr
-
-    def parse_float(self, key, default=0.0):
-        val = default
-        if self.has_key(key):
-            val = float(self._table[key][0])
-        return val
-
-    def parse_floats(self, key, default=[]):
-        arr = default
-        if self.has_key(key):
-            arr = [float(str) for str in self._table[key]]
-        return arr
-
-    def parse_bool(self, key, default=False):
-        val = default
-        if self.has_key(key):
-            val = self._parse_bool(self._table[key][0])
-        return val
-
-    def parse_bools(self, key, default=[]):
-        arr = default
-        if self.has_key(key):
-            arr = [self._parse_bool(str) for str in self._table[key]]
-        return arr
-
-    def _is_comment(self, str):
-        is_comment = False
-        if (len(str) > 0):
-            is_comment = str[0] == '#'
-
-        return is_comment
-        
-    def _is_key(self, str):
-        is_key = False
-        if (len(str) >= 3):
-            is_key = str[0] == '-' and str[1] == '-'
-
-        return is_key
-
-    def _parse_bool(self, str):
-        val = False
-        if (str == 'true' or str == 'True' or str == '1' 
-            or str == 'T' or str == 't'):
-            val = True
-        return val
\ No newline at end of file
+              self._table[curr_key] = vals
+
+          vals = []
+          curr_key = str[2::]
+        else:
+          vals.append(str)
+
+    if (curr_key != ''):
+      if (curr_key not in self._table):
+        self._table[curr_key] = vals
+
+      vals = []
+
+    return succ
+
+  def load_file(self, filename):
+    succ = False
+    with open(filename, 'r') as file:
+      lines = RE.split(r'[\n\r]+', file.read())
+      file.close()
+
+      arg_strs = []
+      for line in lines:
+        if (len(line) > 0 and not self._is_comment(line)):
+          arg_strs += line.split()
+
+      succ = self.load_args(arg_strs)
+    return succ
+
+  def has_key(self, key):
+    return key in self._table
+
+  def parse_string(self, key, default=''):
+    str = default
+    if self.has_key(key):
+      str = self._table[key][0]
+    return str
+
+  def parse_strings(self, key, default=[]):
+    arr = default
+    if self.has_key(key):
+      arr = self._table[key]
+    return arr
+
+  def parse_int(self, key, default=0):
+    val = default
+    if self.has_key(key):
+      val = int(self._table[key][0])
+    return val
+
+  def parse_ints(self, key, default=[]):
+    arr = default
+    if self.has_key(key):
+      arr = [int(str) for str in self._table[key]]
+    return arr
+
+  def parse_float(self, key, default=0.0):
+    val = default
+    if self.has_key(key):
+      val = float(self._table[key][0])
+    return val
+
+  def parse_floats(self, key, default=[]):
+    arr = default
+    if self.has_key(key):
+      arr = [float(str) for str in self._table[key]]
+    return arr
+
+  def parse_bool(self, key, default=False):
+    val = default
+    if self.has_key(key):
+      val = self._parse_bool(self._table[key][0])
+    return val
+
+  def parse_bools(self, key, default=[]):
+    arr = default
+    if self.has_key(key):
+      arr = [self._parse_bool(str) for str in self._table[key]]
+    return arr
+
+  def _is_comment(self, str):
+    is_comment = False
+    if (len(str) > 0):
+      is_comment = str[0] == '#'
+
+    return is_comment
+
+  def _is_key(self, str):
+    is_key = False
+    if (len(str) >= 3):
+      is_key = str[0] == '-' and str[1] == '-'
+
+    return is_key
+
+  def _parse_bool(self, str):
+    val = False
+    if (str == 'true' or str == 'True' or str == '1' or str == 'T' or str == 't'):
+      val = True
+    return val
diff --git a/examples/pybullet/gym/pybullet_utils/bullet_client.py b/examples/pybullet/gym/pybullet_utils/bullet_client.py
index 9b7f907f3..54e0bd09e 100644
--- a/examples/pybullet/gym/pybullet_utils/bullet_client.py
+++ b/examples/pybullet/gym/pybullet_utils/bullet_client.py
@@ -3,7 +3,7 @@ from __future__ import absolute_import
 from __future__ import division
 import functools
 import inspect
-import pybullet 
+import pybullet
 
 
 class BulletClient(object):
@@ -42,9 +42,13 @@ class BulletClient(object):
     attribute = getattr(pybullet, name)
     if inspect.isbuiltin(attribute):
       if name not in [
-          "invertTransform", "multiplyTransforms", "getMatrixFromQuaternion",
-          "getEulerFromQuaternion", "computeViewMatrixFromYawPitchRoll",
-          "computeProjectionMatrixFOV", "getQuaternionFromEuler",
+          "invertTransform",
+          "multiplyTransforms",
+          "getMatrixFromQuaternion",
+          "getEulerFromQuaternion",
+          "computeViewMatrixFromYawPitchRoll",
+          "computeProjectionMatrixFOV",
+          "getQuaternionFromEuler",
       ]:  # A temporary hack for now.
         attribute = functools.partial(attribute, physicsClientId=self._client)
     return attribute
diff --git a/examples/pybullet/gym/pybullet_utils/examples/combineUrdf.py b/examples/pybullet/gym/pybullet_utils/examples/combineUrdf.py
index 8f9653884..f4baa07b4 100644
--- a/examples/pybullet/gym/pybullet_utils/examples/combineUrdf.py
+++ b/examples/pybullet/gym/pybullet_utils/examples/combineUrdf.py
@@ -10,7 +10,6 @@ p0.setAdditionalSearchPath(pybullet_data.getDataPath())
 p1 = bc.BulletClient(connection_mode=pybullet.DIRECT)
 p1.setAdditionalSearchPath(pybullet_data.getDataPath())
 
-
 #can also connect using different modes, GUI, SHARED_MEMORY, TCP, UDP, SHARED_MEMORY_SERVER, GUI_SERVER
 
 husky = p1.loadURDF("husky/husky.urdf", flags=p0.URDF_USE_IMPLICIT_CYLINDER)
@@ -22,36 +21,34 @@ ed1 = ed.UrdfEditor()
 ed1.initializeFromBulletBody(kuka, p0._client)
 #ed1.saveUrdf("combined.urdf")
 
-
 parentLinkIndex = 0
 
-jointPivotXYZInParent = [0,0,0]
-jointPivotRPYInParent = [0,0,0]
+jointPivotXYZInParent = [0, 0, 0]
+jointPivotRPYInParent = [0, 0, 0]
 
+jointPivotXYZInChild = [0, 0, 0]
+jointPivotRPYInChild = [0, 0, 0]
 
-jointPivotXYZInChild = [0,0,0]
-jointPivotRPYInChild = [0,0,0]
-
-newjoint = ed0.joinUrdf(ed1, parentLinkIndex , jointPivotXYZInParent, jointPivotRPYInParent, jointPivotXYZInChild, jointPivotRPYInChild,  p0._client, p1._client)
+newjoint = ed0.joinUrdf(ed1, parentLinkIndex, jointPivotXYZInParent, jointPivotRPYInParent,
+                        jointPivotXYZInChild, jointPivotRPYInChild, p0._client, p1._client)
 newjoint.joint_type = p0.JOINT_FIXED
 
 ed0.saveUrdf("combined.urdf")
 
 print(p0._client)
 print(p1._client)
-print("p0.getNumBodies()=",p0.getNumBodies())
-print("p1.getNumBodies()=",p1.getNumBodies())
+print("p0.getNumBodies()=", p0.getNumBodies())
+print("p1.getNumBodies()=", p1.getNumBodies())
 
 pgui = bc.BulletClient(connection_mode=pybullet.GUI)
 pgui.configureDebugVisualizer(pgui.COV_ENABLE_RENDERING, 0)
 
-orn=[0,0,0,1]
-ed0.createMultiBody([0,0,0],orn, pgui._client)
+orn = [0, 0, 0, 1]
+ed0.createMultiBody([0, 0, 0], orn, pgui._client)
 pgui.setRealTimeSimulation(1)
 
 pgui.configureDebugVisualizer(pgui.COV_ENABLE_RENDERING, 1)
 
-
 while (pgui.isConnected()):
-	pgui.getCameraImage(320,200, renderer=pgui.ER_BULLET_HARDWARE_OPENGL)
-	time.sleep(1./240.)
+  pgui.getCameraImage(320, 200, renderer=pgui.ER_BULLET_HARDWARE_OPENGL)
+  time.sleep(1. / 240.)
diff --git a/examples/pybullet/gym/pybullet_utils/examples/combined.py b/examples/pybullet/gym/pybullet_utils/examples/combined.py
index 3a7538442..dcb2c4e5f 100644
--- a/examples/pybullet/gym/pybullet_utils/examples/combined.py
+++ b/examples/pybullet/gym/pybullet_utils/examples/combined.py
@@ -2,12 +2,10 @@ import pybullet as p
 p.connect(p.GUI)
 p.loadURDF("combined.urdf", useFixedBase=True)
 
-
-
 #for j in range (p.getNumJoints(0)):
 #	p.setJointMotorControl2(0,j,p.VELOCITY_CONTROL,targetVelocity=0.1)
 p.setRealTimeSimulation(1)
 while (p.isConnected()):
-	p.getCameraImage(320,200)
-	import time
-	time.sleep(1./240.)
+  p.getCameraImage(320, 200)
+  import time
+  time.sleep(1. / 240.)
diff --git a/examples/pybullet/gym/pybullet_utils/examples/mjcf2urdf.py b/examples/pybullet/gym/pybullet_utils/examples/mjcf2urdf.py
index f7903262f..ebd21fcf3 100644
--- a/examples/pybullet/gym/pybullet_utils/examples/mjcf2urdf.py
+++ b/examples/pybullet/gym/pybullet_utils/examples/mjcf2urdf.py
@@ -6,7 +6,9 @@ import pybullet_data as pd
 import pybullet_utils.urdfEditor as ed
 import argparse
 parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-parser.add_argument('--mjcf', help='MuJoCo xml file to be converted to URDF', default='mjcf/humanoid.xml')
+parser.add_argument('--mjcf',
+                    help='MuJoCo xml file to be converted to URDF',
+                    default='mjcf/humanoid.xml')
 args = parser.parse_args()
 
 p = bc.BulletClient()
@@ -14,15 +16,15 @@ p.setAdditionalSearchPath(pd.getDataPath())
 objs = p.loadMJCF(args.mjcf, flags=p.URDF_USE_IMPLICIT_CYLINDER)
 
 for o in objs:
-	#print("o=",o, p.getBodyInfo(o), p.getNumJoints(o))
-	humanoid = objs[o]
-	ed0 = ed.UrdfEditor()
-	ed0.initializeFromBulletBody(humanoid, p._client)
-	robotName = str(p.getBodyInfo(o)[1],'utf-8')
-	partName = str(p.getBodyInfo(o)[0], 'utf-8')
-	
-	print("robotName=",robotName)
-	print("partName=",partName)
-	
-	saveVisuals=False
-	ed0.saveUrdf(robotName+"_"+partName+".urdf", saveVisuals)
+  #print("o=",o, p.getBodyInfo(o), p.getNumJoints(o))
+  humanoid = objs[o]
+  ed0 = ed.UrdfEditor()
+  ed0.initializeFromBulletBody(humanoid, p._client)
+  robotName = str(p.getBodyInfo(o)[1], 'utf-8')
+  partName = str(p.getBodyInfo(o)[0], 'utf-8')
+
+  print("robotName=", robotName)
+  print("partName=", partName)
+
+  saveVisuals = False
+  ed0.saveUrdf(robotName + "_" + partName + ".urdf", saveVisuals)
diff --git a/examples/pybullet/gym/pybullet_utils/examples/multipleScenes.py b/examples/pybullet/gym/pybullet_utils/examples/multipleScenes.py
index 7018cfead..a2858ee87 100644
--- a/examples/pybullet/gym/pybullet_utils/examples/multipleScenes.py
+++ b/examples/pybullet/gym/pybullet_utils/examples/multipleScenes.py
@@ -15,6 +15,5 @@ p0.loadURDF("r2d2.urdf")
 p1.loadSDF("stadium.sdf")
 print(p0._client)
 print(p1._client)
-print("p0.getNumBodies()=",p0.getNumBodies())
-print("p1.getNumBodies()=",p1.getNumBodies())
-
+print("p0.getNumBodies()=", p0.getNumBodies())
+print("p1.getNumBodies()=", p1.getNumBodies())
diff --git a/examples/pybullet/gym/pybullet_utils/examples/testargs.py b/examples/pybullet/gym/pybullet_utils/examples/testargs.py
index d03a82041..0f512a943 100644
--- a/examples/pybullet/gym/pybullet_utils/examples/testargs.py
+++ b/examples/pybullet/gym/pybullet_utils/examples/testargs.py
@@ -1,26 +1,28 @@
 import pybullet_data
-from pybullet_utils.arg_parser import	ArgParser
-from pybullet_utils.logger import	Logger
+from pybullet_utils.arg_parser import ArgParser
+from pybullet_utils.logger import Logger
 import sys
 
-def	build_arg_parser(args):
-		arg_parser = ArgParser()
-		arg_parser.load_args(args)
 
-		arg_file = arg_parser.parse_string('arg_file', '')
-		if (arg_file !=	''):
-				path = pybullet_data.getDataPath()+"/args/"+arg_file
-				succ = arg_parser.load_file(path)
-				Logger.print2(arg_file)
-				assert succ, Logger.print2('Failed to	load args	from:	'	+	arg_file)
+def build_arg_parser(args):
+  arg_parser = ArgParser()
+  arg_parser.load_args(args)
+
+  arg_file = arg_parser.parse_string('arg_file', '')
+  if (arg_file != ''):
+    path = pybullet_data.getDataPath() + "/args/" + arg_file
+    succ = arg_parser.load_file(path)
+    Logger.print2(arg_file)
+    assert succ, Logger.print2('Failed to	load args	from:	' + arg_file)
+
+  return arg_parser
 
-		return arg_parser
 
 args = sys.argv[1:]
 arg_parser = build_arg_parser(args)
-motion_file	=	arg_parser.parse_string("motion_file")
-print("motion_file=",motion_file)
+motion_file = arg_parser.parse_string("motion_file")
+print("motion_file=", motion_file)
 bodies = arg_parser.parse_ints("fall_contact_bodies")
-print("bodies=",bodies)
+print("bodies=", bodies)
 int_output_path = arg_parser.parse_string("int_output_path")
-print("int_output_path=",int_output_path)
\ No newline at end of file
+print("int_output_path=", int_output_path)
diff --git a/examples/pybullet/gym/pybullet_utils/examples/testlog.py b/examples/pybullet/gym/pybullet_utils/examples/testlog.py
index 9a275160a..cd39376a9 100644
--- a/examples/pybullet/gym/pybullet_utils/examples/testlog.py
+++ b/examples/pybullet/gym/pybullet_utils/examples/testlog.py
@@ -1,9 +1,9 @@
 from pybullet_utils.logger import Logger
 logger = Logger()
 logger.configure_output_file("e:/mylog.txt")
-for i in range (10):
-	logger.log_tabular("Iteration", 1)
+for i in range(10):
+  logger.log_tabular("Iteration", 1)
 Logger.print2("hello world")
 
 logger.print_tabular()
-logger.dump_tabular()
\ No newline at end of file
+logger.dump_tabular()
diff --git a/examples/pybullet/gym/pybullet_utils/logger.py b/examples/pybullet/gym/pybullet_utils/logger.py
index ceb2605ba..3c757623e 100644
--- a/examples/pybullet/gym/pybullet_utils/logger.py
+++ b/examples/pybullet/gym/pybullet_utils/logger.py
@@ -1,5 +1,4 @@
 import pybullet_utils.mpi_util as MPIUtil
-
 """
 
 Some simple logging functionality, inspired by rllab's logging.
@@ -17,112 +16,114 @@ A['EpRewMean']
 
 import os.path as osp, shutil, time, atexit, os, subprocess
 
+
 class Logger:
-    def print2(str):
-        if (MPIUtil.is_root_proc()):
-            print(str)
-        return
-
-    def __init__(self):
-        self.output_file = None
-        self.first_row = True
-        self.log_headers = []
-        self.log_current_row = {}
-        self._dump_str_template = ""
-        return
-
-    def reset(self):
-        self.first_row = True
-        self.log_headers = []
-        self.log_current_row = {}
-        if self.output_file is not None:
-            self.output_file = open(output_path, 'w')
-        return
-
-    def configure_output_file(self, filename=None):
-        """
+
+  def print2(str):
+    if (MPIUtil.is_root_proc()):
+      print(str)
+    return
+
+  def __init__(self):
+    self.output_file = None
+    self.first_row = True
+    self.log_headers = []
+    self.log_current_row = {}
+    self._dump_str_template = ""
+    return
+
+  def reset(self):
+    self.first_row = True
+    self.log_headers = []
+    self.log_current_row = {}
+    if self.output_file is not None:
+      self.output_file = open(output_path, 'w')
+    return
+
+  def configure_output_file(self, filename=None):
+    """
         Set output directory to d, or to /tmp/somerandomnumber if d is None
         """
-        self.first_row = True
-        self.log_headers = []
-        self.log_current_row = {}
+    self.first_row = True
+    self.log_headers = []
+    self.log_current_row = {}
 
-        output_path = filename or "output/log_%i.txt"%int(time.time())
+    output_path = filename or "output/log_%i.txt" % int(time.time())
 
-        out_dir = os.path.dirname(output_path)
-        if not os.path.exists(out_dir) and MPIUtil.is_root_proc():
-            os.makedirs(out_dir)
+    out_dir = os.path.dirname(output_path)
+    if not os.path.exists(out_dir) and MPIUtil.is_root_proc():
+      os.makedirs(out_dir)
 
-        if (MPIUtil.is_root_proc()):
-            self.output_file = open(output_path, 'w')
-            assert osp.exists(output_path)
-            atexit.register(self.output_file.close)
+    if (MPIUtil.is_root_proc()):
+      self.output_file = open(output_path, 'w')
+      assert osp.exists(output_path)
+      atexit.register(self.output_file.close)
 
-            Logger.print2("Logging data to " + self.output_file.name)
-        return
+      Logger.print2("Logging data to " + self.output_file.name)
+    return
 
-    def log_tabular(self, key, val):
-        """
+  def log_tabular(self, key, val):
+    """
         Log a value of some diagnostic
         Call this once for each diagnostic quantity, each iteration
         """
-        if self.first_row and key not in self.log_headers:
-            self.log_headers.append(key)
-        else:
-            assert key in self.log_headers, "Trying to introduce a new key %s that you didn't include in the first iteration"%key
-        self.log_current_row[key] = val
-        return
-
-    def get_num_keys(self):
-        return len(self.log_headers)
-
-    def print_tabular(self):
-        """
+    if self.first_row and key not in self.log_headers:
+      self.log_headers.append(key)
+    else:
+      assert key in self.log_headers, "Trying to introduce a new key %s that you didn't include in the first iteration" % key
+    self.log_current_row[key] = val
+    return
+
+  def get_num_keys(self):
+    return len(self.log_headers)
+
+  def print_tabular(self):
+    """
         Print all of the diagnostics from the current iteration
         """
-        if (MPIUtil.is_root_proc()):
-            vals = []
-            Logger.print2("-"*37)
-            for key in self.log_headers:
-                val = self.log_current_row.get(key, "")
-                if isinstance(val, float):
-                    valstr = "%8.3g"%val
-                elif isinstance(val, int):
-                    valstr = str(val)
-                else: 
-                    valstr = val
-                Logger.print2("| %15s | %15s |"%(key, valstr))
-                vals.append(val)
-            Logger.print2("-" * 37)
-        return
-
-    def dump_tabular(self):
-        """
+    if (MPIUtil.is_root_proc()):
+      vals = []
+      Logger.print2("-" * 37)
+      for key in self.log_headers:
+        val = self.log_current_row.get(key, "")
+        if isinstance(val, float):
+          valstr = "%8.3g" % val
+        elif isinstance(val, int):
+          valstr = str(val)
+        else:
+          valstr = val
+        Logger.print2("| %15s | %15s |" % (key, valstr))
+        vals.append(val)
+      Logger.print2("-" * 37)
+    return
+
+  def dump_tabular(self):
+    """
         Write all of the diagnostics from the current iteration
         """
-        if (MPIUtil.is_root_proc()):
-            if (self.first_row):
-                self._dump_str_template = self._build_str_template()
-
-            vals = []
-            for key in self.log_headers:
-                val = self.log_current_row.get(key, "")
-                vals.append(val)
-            
-            if self.output_file is not None:
-                if self.first_row:
-                    header_str = self._dump_str_template.format(*self.log_headers)
-                    self.output_file.write(header_str + "\n")
-
-                val_str = self._dump_str_template.format(*map(str,vals))
-                self.output_file.write(val_str + "\n")
-                self.output_file.flush()
-
-        self.log_current_row.clear()
-        self.first_row=False
-        return
-
-    def _build_str_template(self):
-        num_keys = self.get_num_keys()
-        template = "{:<25}" * num_keys
-        return template
+    if (MPIUtil.is_root_proc()):
+      if (self.first_row):
+        self._dump_str_template = self._build_str_template()
+
+      vals = []
+      for key in self.log_headers:
+        val = self.log_current_row.get(key, "")
+        vals.append(val)
+
+      if self.output_file is not None:
+        if self.first_row:
+          header_str = self._dump_str_template.format(*self.log_headers)
+          self.output_file.write(header_str + "\n")
+
+        val_str = self._dump_str_template.format(*map(str, vals))
+        self.output_file.write(val_str + "\n")
+        self.output_file.flush()
+
+    self.log_current_row.clear()
+    self.first_row = False
+    return
+
+  def _build_str_template(self):
+    num_keys = self.get_num_keys()
+    template = "{:<25}" * num_keys
+    return template
diff --git a/examples/pybullet/gym/pybullet_utils/math_util.py b/examples/pybullet/gym/pybullet_utils/math_util.py
index d0f333b3f..260a5da36 100644
--- a/examples/pybullet/gym/pybullet_utils/math_util.py
+++ b/examples/pybullet/gym/pybullet_utils/math_util.py
@@ -4,15 +4,19 @@ RAD_TO_DEG = 57.2957795
 DEG_TO_RAD = 1.0 / RAD_TO_DEG
 INVALID_IDX = -1
 
+
 def lerp(x, y, t):
-    return (1 - t) * x + t * y
+  return (1 - t) * x + t * y
+
 
 def log_lerp(x, y, t):
-    return np.exp(lerp(np.log(x), np.log(y), t))
+  return np.exp(lerp(np.log(x), np.log(y), t))
+
 
 def flatten(arr_list):
-    return np.concatenate([np.reshape(a, [-1]) for a in arr_list], axis=0)
+  return np.concatenate([np.reshape(a, [-1]) for a in arr_list], axis=0)
+
 
 def flip_coin(p):
-    rand_num = np.random.binomial(1, p, 1)
-    return rand_num[0] == 1
\ No newline at end of file
+  rand_num = np.random.binomial(1, p, 1)
+  return rand_num[0] == 1
diff --git a/examples/pybullet/gym/pybullet_utils/mpi_util.py b/examples/pybullet/gym/pybullet_utils/mpi_util.py
index 30ca6aed0..9d8f5f90f 100644
--- a/examples/pybullet/gym/pybullet_utils/mpi_util.py
+++ b/examples/pybullet/gym/pybullet_utils/mpi_util.py
@@ -3,50 +3,61 @@ from mpi4py import MPI
 
 ROOT_PROC_RANK = 0
 
+
 def get_num_procs():
-    return MPI.COMM_WORLD.Get_size()
+  return MPI.COMM_WORLD.Get_size()
+
 
 def get_proc_rank():
-    return MPI.COMM_WORLD.Get_rank()
+  return MPI.COMM_WORLD.Get_rank()
+
 
 def is_root_proc():
-    rank = get_proc_rank()
-    return rank == ROOT_PROC_RANK
+  rank = get_proc_rank()
+  return rank == ROOT_PROC_RANK
+
 
 def bcast(x):
-    MPI.COMM_WORLD.Bcast(x, root=ROOT_PROC_RANK)
-    return
+  MPI.COMM_WORLD.Bcast(x, root=ROOT_PROC_RANK)
+  return
+
 
 def reduce_sum(x):
-    return reduce_all(x, MPI.SUM)
+  return reduce_all(x, MPI.SUM)
+
 
 def reduce_prod(x):
-    return reduce_all(x, MPI.PROD)
+  return reduce_all(x, MPI.PROD)
+
 
 def reduce_avg(x):
-    buffer = reduce_sum(x)
-    buffer /= get_num_procs()
-    return buffer
+  buffer = reduce_sum(x)
+  buffer /= get_num_procs()
+  return buffer
+
 
 def reduce_min(x):
-    return reduce_all(x, MPI.MIN)
+  return reduce_all(x, MPI.MIN)
+
 
 def reduce_max(x):
-    return reduce_all(x, MPI.MAX)
+  return reduce_all(x, MPI.MAX)
+
 
 def reduce_all(x, op):
-    is_array = isinstance(x, np.ndarray)
-    x_buf = x if is_array else np.array([x])
-    buffer = np.zeros_like(x_buf)
-    MPI.COMM_WORLD.Allreduce(x_buf, buffer, op=op)
-    buffer = buffer if is_array else buffer[0]
-    return buffer
+  is_array = isinstance(x, np.ndarray)
+  x_buf = x if is_array else np.array([x])
+  buffer = np.zeros_like(x_buf)
+  MPI.COMM_WORLD.Allreduce(x_buf, buffer, op=op)
+  buffer = buffer if is_array else buffer[0]
+  return buffer
+
 
 def gather_all(x):
-    is_array = isinstance(x, np.ndarray)
-    x_buf = np.array([x])
-    buffer = np.zeros_like(x_buf)
-    buffer = np.repeat(buffer, get_num_procs(), axis=0)
-    MPI.COMM_WORLD.Allgather(x_buf, buffer)
-    buffer = list(buffer)
-    return buffer
\ No newline at end of file
+  is_array = isinstance(x, np.ndarray)
+  x_buf = np.array([x])
+  buffer = np.zeros_like(x_buf)
+  buffer = np.repeat(buffer, get_num_procs(), axis=0)
+  MPI.COMM_WORLD.Allgather(x_buf, buffer)
+  buffer = list(buffer)
+  return buffer
diff --git a/examples/pybullet/gym/pybullet_utils/pd_controller_stable.py b/examples/pybullet/gym/pybullet_utils/pd_controller_stable.py
index 2171b34bd..87b68d462 100644
--- a/examples/pybullet/gym/pybullet_utils/pd_controller_stable.py
+++ b/examples/pybullet/gym/pybullet_utils/pd_controller_stable.py
@@ -1,218 +1,232 @@
 import numpy as np
 
 
-
 class PDControllerStableMultiDof(object):
-    def __init__(self, pb):
-      self._pb = pb
-
-    def computeAngVel(self,ornStart, ornEnd, deltaTime, bullet_client):
-      dorn = bullet_client.getDifferenceQuaternion(ornStart,ornEnd)
-      axis,angle = bullet_client.getAxisAngleFromQuaternion(dorn)
-      angVel = [(axis[0]*angle)/deltaTime,(axis[1]*angle)/deltaTime,(axis[2]*angle)/deltaTime]
-      return angVel
-    
-    def quatMul(self, q1, q2):
-      return [ q1[3] * q2[0] + q1[0] * q2[3] + q1[1] * q2[2] - q1[2] * q2[1],
-		           q1[3] * q2[1] + q1[1] * q2[3] + q1[2] * q2[0] - q1[0] * q2[2],
-           		q1[3] * q2[2] + q1[2] * q2[3] + q1[0] * q2[1] - q1[1] * q2[0],
-		          q1[3] * q2[3] - q1[0] * q2[0] - q1[1] * q2[1] - q1[2] * q2[2]]
-		
-    def computeAngVelRel(self,ornStart, ornEnd, deltaTime, bullet_client):
-      ornStartConjugate = [-ornStart[0],-ornStart[1],-ornStart[2],ornStart[3]]
-      q_diff = self.quatMul(ornStartConjugate, ornEnd)#bullet_client.multiplyTransforms([0,0,0], ornStartConjugate, [0,0,0], ornEnd)
-      
-      axis,angle = bullet_client.getAxisAngleFromQuaternion(q_diff)
-      angVel = [(axis[0]*angle)/deltaTime,(axis[1]*angle)/deltaTime,(axis[2]*angle)/deltaTime]
-      return angVel
-    
-    
-    def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds, maxForces, timeStep):
-      
-      numJoints = len(jointIndices)#self._pb.getNumJoints(bodyUniqueId)
-      curPos,curOrn = self._pb.getBasePositionAndOrientation(bodyUniqueId)
-      q1 = [curPos[0],curPos[1],curPos[2],curOrn[0],curOrn[1],curOrn[2],curOrn[3]]
-      #print("q1=",q1)
-      
-      
-      #qdot1 = [0,0,0, 0,0,0,0]
-      baseLinVel, baseAngVel = self._pb.getBaseVelocity(bodyUniqueId)
-      #print("baseLinVel=",baseLinVel)
-      qdot1 = [baseLinVel[0],baseLinVel[1],baseLinVel[2],baseAngVel[0],baseAngVel[1],baseAngVel[2],0]
-      #qError = [0,0,0, 0,0,0,0]
-      desiredOrn = [desiredPositions[3],desiredPositions[4],desiredPositions[5],desiredPositions[6]]
-      axis1 = self._pb.getAxisDifferenceQuaternion(desiredOrn,curOrn)
-      angDiff = [0,0,0]#self.computeAngVel(curOrn, desiredOrn, 1, self._pb)
-      qError=[ desiredPositions[0]-curPos[0], desiredPositions[1]-curPos[1], desiredPositions[2]-curPos[2],angDiff[0],angDiff[1],angDiff[2],0]
-      target_pos = np.array(desiredPositions)
-      #np.savetxt("pb_target_pos.csv", target_pos, delimiter=",")
-      
-      
-      qIndex=7
-      qdotIndex=7
-      zeroAccelerations=[0,0,0,  0,0,0,0]
-      for i in range (numJoints):
-        js = self._pb.getJointStateMultiDof(bodyUniqueId, jointIndices[i])
-        
-        jointPos=js[0]
-        jointVel = js[1]
-        q1+=jointPos
-        
-        if len(js[0])==1:
-          desiredPos=desiredPositions[qIndex]
-          
-          qdiff=desiredPos - jointPos[0]
-          qError.append(qdiff)
-          zeroAccelerations.append(0.)
-          qdot1+=jointVel
-          qIndex+=1
-          qdotIndex+=1
-        if len(js[0])==4:
-          desiredPos=[desiredPositions[qIndex],desiredPositions[qIndex+1],desiredPositions[qIndex+2],desiredPositions[qIndex+3]]
-          #axis = self._pb.getAxisDifferenceQuaternion(desiredPos,jointPos)
-          angDiff = self.computeAngVelRel(jointPos, desiredPos, 1, self._pb)
-          #angDiff = self._pb.computeAngVelRel(jointPos, desiredPos, 1)
-          
-          jointVelNew = [jointVel[0],jointVel[1],jointVel[2],0]
-          qdot1+=jointVelNew
-          qError.append(angDiff[0])
-          qError.append(angDiff[1])
-          qError.append(angDiff[2])
-          qError.append(0)
-          desiredVel=[desiredVelocities[qdotIndex],desiredVelocities[qdotIndex+1],desiredVelocities[qdotIndex+2]]
-          zeroAccelerations+=[0.,0.,0.,0.]
-          qIndex+=4
-          qdotIndex+=4
-      
-      q = np.array(q1)
-      
-      qerr = np.array(qError)
-      
-      #np.savetxt("pb_qerro.csv",qerr,delimiter=",")
-      
-      #np.savetxt("pb_q.csv", q, delimiter=",")
-      
-      qdot=np.array(qdot1)
-      #np.savetxt("qdot.csv", qdot, delimiter=",")
-      
-      qdotdesired = np.array(desiredVelocities)
-      qdoterr = qdotdesired-qdot
-      
-      
-      Kp = np.diagflat(kps)
-      Kd = np.diagflat(kds)
-      
-      p =  Kp.dot(qError)
-      
-      #np.savetxt("pb_qError.csv", qError, delimiter=",")
-      #np.savetxt("pb_p.csv", p, delimiter=",")
-      
-      d = Kd.dot(qdoterr)
-
-      #np.savetxt("pb_d.csv", d, delimiter=",")
-      #np.savetxt("pbqdoterr.csv", qdoterr, delimiter=",")
-      
-      
-      M1 = self._pb.calculateMassMatrix(bodyUniqueId,q1, flags=1)
-      
-      
-      M2 = np.array(M1)
-      #np.savetxt("M2.csv", M2, delimiter=",")
-      
-      M = (M2 + Kd * timeStep)
-      
-      #np.savetxt("pbM_tKd.csv",M, delimiter=",")
-      
-    
-      
-      c1 = self._pb.calculateInverseDynamics(bodyUniqueId, q1, qdot1, zeroAccelerations, flags=1)
-      
-      
-      c = np.array(c1)
-      #np.savetxt("pb_C.csv",c, delimiter=",")
-      A = M
-      #p = [0]*43
-      #np.savetxt("pb_kp_dot_qError.csv", p)
-      #np.savetxt("pb_kd_dot_vError.csv", d)
-      
-      b =  p + d -c
-      #np.savetxt("pb_b_acc.csv",b, delimiter=",")
-      
-      
-      useNumpySolver = True
-      if useNumpySolver:
-        qddot = np.linalg.solve(A, b)
-      else:
-        dofCount = len(b)
-        print(dofCount)
-        qddot = self._pb.ldltSolve(bodyUniqueId, jointPositions=q1, b=b.tolist(), kd=kds, t=timeStep)
-      
-      tau = p + d - Kd.dot(qddot) * timeStep
-      #print("len(tau)=",len(tau))
-      #np.savetxt("pb_tau_not_clamped.csv", tau, delimiter=",")
-      
-      maxF = np.array(maxForces)
-      #print("maxF",maxF)
-      forces = np.clip(tau, -maxF , maxF )
-      
-      #np.savetxt("pb_tau_clamped.csv", tau, delimiter=",")
-      return forces
-      
-    
-      
-class PDControllerStable(object):
-    def __init__(self, pb):
-      self._pb = pb
-
-    def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds, maxForces, timeStep):
-      numBaseDofs = 0
-      numPosBaseDofs=0
-      baseMass = self._pb.getDynamicsInfo(bodyUniqueId,-1)[0]
-      curPos,curOrn = self._pb.getBasePositionAndOrientation(bodyUniqueId)
-      q1 = []
-      qdot1 = []
-      zeroAccelerations = []
-      qError=[]
-      if (baseMass>0):
-        numBaseDofs=6
-        numPosBaseDofs=7
-        q1 = [curPos[0],curPos[1],curPos[2],curOrn[0],curOrn[1],curOrn[2],curOrn[3]]
-        qdot1=[0]*numBaseDofs
-        zeroAccelerations = [0]*numBaseDofs
-        angDiff=[0,0,0]
-        qError=[ desiredPositions[0]-curPos[0], desiredPositions[1]-curPos[1], desiredPositions[2]-curPos[2],angDiff[0],angDiff[1],angDiff[2]]
-      numJoints = self._pb.getNumJoints(bodyUniqueId)
-      jointStates = self._pb.getJointStates(bodyUniqueId, jointIndices)
-      
-      for i in range (numJoints):
-        q1.append(jointStates[i][0])
-        qdot1.append(jointStates[i][1])
-        zeroAccelerations.append(0)
-      q = np.array(q1)
-      qdot=np.array(qdot1)
-      qdes = np.array(desiredPositions)
-      qdotdes = np.array(desiredVelocities)
-      #qError = qdes - q
-      for j in range(numJoints):
-        qError.append(desiredPositions[j+numPosBaseDofs]-q1[j+numPosBaseDofs])
-      #print("qError=",qError)
-      qdotError = qdotdes - qdot
-      Kp = np.diagflat(kps)
-      Kd = np.diagflat(kds)
-      p =  Kp.dot(qError)
-      d = Kd.dot(qdotError)
-      forces = p + d
-      
-      M1 = self._pb.calculateMassMatrix(bodyUniqueId,q1)
-      M2 = np.array(M1)
-      M = (M2 + Kd * timeStep)
-      c1 = self._pb.calculateInverseDynamics(bodyUniqueId, q1, qdot1, zeroAccelerations)
-      c = np.array(c1)
-      A = M
-      b = -c + p + d
+
+  def __init__(self, pb):
+    self._pb = pb
+
+  def computeAngVel(self, ornStart, ornEnd, deltaTime, bullet_client):
+    dorn = bullet_client.getDifferenceQuaternion(ornStart, ornEnd)
+    axis, angle = bullet_client.getAxisAngleFromQuaternion(dorn)
+    angVel = [(axis[0] * angle) / deltaTime, (axis[1] * angle) / deltaTime,
+              (axis[2] * angle) / deltaTime]
+    return angVel
+
+  def quatMul(self, q1, q2):
+    return [
+        q1[3] * q2[0] + q1[0] * q2[3] + q1[1] * q2[2] - q1[2] * q2[1],
+        q1[3] * q2[1] + q1[1] * q2[3] + q1[2] * q2[0] - q1[0] * q2[2],
+        q1[3] * q2[2] + q1[2] * q2[3] + q1[0] * q2[1] - q1[1] * q2[0],
+        q1[3] * q2[3] - q1[0] * q2[0] - q1[1] * q2[1] - q1[2] * q2[2]
+    ]
+
+  def computeAngVelRel(self, ornStart, ornEnd, deltaTime, bullet_client):
+    ornStartConjugate = [-ornStart[0], -ornStart[1], -ornStart[2], ornStart[3]]
+    q_diff = self.quatMul(
+        ornStartConjugate,
+        ornEnd)  #bullet_client.multiplyTransforms([0,0,0], ornStartConjugate, [0,0,0], ornEnd)
+
+    axis, angle = bullet_client.getAxisAngleFromQuaternion(q_diff)
+    angVel = [(axis[0] * angle) / deltaTime, (axis[1] * angle) / deltaTime,
+              (axis[2] * angle) / deltaTime]
+    return angVel
+
+  def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds,
+                maxForces, timeStep):
+
+    numJoints = len(jointIndices)  #self._pb.getNumJoints(bodyUniqueId)
+    curPos, curOrn = self._pb.getBasePositionAndOrientation(bodyUniqueId)
+    q1 = [curPos[0], curPos[1], curPos[2], curOrn[0], curOrn[1], curOrn[2], curOrn[3]]
+    #print("q1=",q1)
+
+    #qdot1 = [0,0,0, 0,0,0,0]
+    baseLinVel, baseAngVel = self._pb.getBaseVelocity(bodyUniqueId)
+    #print("baseLinVel=",baseLinVel)
+    qdot1 = [
+        baseLinVel[0], baseLinVel[1], baseLinVel[2], baseAngVel[0], baseAngVel[1], baseAngVel[2], 0
+    ]
+    #qError = [0,0,0, 0,0,0,0]
+    desiredOrn = [
+        desiredPositions[3], desiredPositions[4], desiredPositions[5], desiredPositions[6]
+    ]
+    axis1 = self._pb.getAxisDifferenceQuaternion(desiredOrn, curOrn)
+    angDiff = [0, 0, 0]  #self.computeAngVel(curOrn, desiredOrn, 1, self._pb)
+    qError = [
+        desiredPositions[0] - curPos[0], desiredPositions[1] - curPos[1],
+        desiredPositions[2] - curPos[2], angDiff[0], angDiff[1], angDiff[2], 0
+    ]
+    target_pos = np.array(desiredPositions)
+    #np.savetxt("pb_target_pos.csv", target_pos, delimiter=",")
+
+    qIndex = 7
+    qdotIndex = 7
+    zeroAccelerations = [0, 0, 0, 0, 0, 0, 0]
+    for i in range(numJoints):
+      js = self._pb.getJointStateMultiDof(bodyUniqueId, jointIndices[i])
+
+      jointPos = js[0]
+      jointVel = js[1]
+      q1 += jointPos
+
+      if len(js[0]) == 1:
+        desiredPos = desiredPositions[qIndex]
+
+        qdiff = desiredPos - jointPos[0]
+        qError.append(qdiff)
+        zeroAccelerations.append(0.)
+        qdot1 += jointVel
+        qIndex += 1
+        qdotIndex += 1
+      if len(js[0]) == 4:
+        desiredPos = [
+            desiredPositions[qIndex], desiredPositions[qIndex + 1], desiredPositions[qIndex + 2],
+            desiredPositions[qIndex + 3]
+        ]
+        #axis = self._pb.getAxisDifferenceQuaternion(desiredPos,jointPos)
+        angDiff = self.computeAngVelRel(jointPos, desiredPos, 1, self._pb)
+        #angDiff = self._pb.computeAngVelRel(jointPos, desiredPos, 1)
+
+        jointVelNew = [jointVel[0], jointVel[1], jointVel[2], 0]
+        qdot1 += jointVelNew
+        qError.append(angDiff[0])
+        qError.append(angDiff[1])
+        qError.append(angDiff[2])
+        qError.append(0)
+        desiredVel = [
+            desiredVelocities[qdotIndex], desiredVelocities[qdotIndex + 1],
+            desiredVelocities[qdotIndex + 2]
+        ]
+        zeroAccelerations += [0., 0., 0., 0.]
+        qIndex += 4
+        qdotIndex += 4
+
+    q = np.array(q1)
+
+    qerr = np.array(qError)
+
+    #np.savetxt("pb_qerro.csv",qerr,delimiter=",")
+
+    #np.savetxt("pb_q.csv", q, delimiter=",")
+
+    qdot = np.array(qdot1)
+    #np.savetxt("qdot.csv", qdot, delimiter=",")
+
+    qdotdesired = np.array(desiredVelocities)
+    qdoterr = qdotdesired - qdot
+
+    Kp = np.diagflat(kps)
+    Kd = np.diagflat(kds)
+
+    p = Kp.dot(qError)
+
+    #np.savetxt("pb_qError.csv", qError, delimiter=",")
+    #np.savetxt("pb_p.csv", p, delimiter=",")
+
+    d = Kd.dot(qdoterr)
+
+    #np.savetxt("pb_d.csv", d, delimiter=",")
+    #np.savetxt("pbqdoterr.csv", qdoterr, delimiter=",")
+
+    M1 = self._pb.calculateMassMatrix(bodyUniqueId, q1, flags=1)
+
+    M2 = np.array(M1)
+    #np.savetxt("M2.csv", M2, delimiter=",")
+
+    M = (M2 + Kd * timeStep)
+
+    #np.savetxt("pbM_tKd.csv",M, delimiter=",")
+
+    c1 = self._pb.calculateInverseDynamics(bodyUniqueId, q1, qdot1, zeroAccelerations, flags=1)
+
+    c = np.array(c1)
+    #np.savetxt("pb_C.csv",c, delimiter=",")
+    A = M
+    #p = [0]*43
+    #np.savetxt("pb_kp_dot_qError.csv", p)
+    #np.savetxt("pb_kd_dot_vError.csv", d)
+
+    b = p + d - c
+    #np.savetxt("pb_b_acc.csv",b, delimiter=",")
+
+    useNumpySolver = True
+    if useNumpySolver:
       qddot = np.linalg.solve(A, b)
-      tau = p + d - Kd.dot(qddot) * timeStep
-      maxF = np.array(maxForces)
-      forces = np.clip(tau, -maxF , maxF )
-      #print("c=",c)
-      return tau
+    else:
+      dofCount = len(b)
+      print(dofCount)
+      qddot = self._pb.ldltSolve(bodyUniqueId, jointPositions=q1, b=b.tolist(), kd=kds, t=timeStep)
+
+    tau = p + d - Kd.dot(qddot) * timeStep
+    #print("len(tau)=",len(tau))
+    #np.savetxt("pb_tau_not_clamped.csv", tau, delimiter=",")
+
+    maxF = np.array(maxForces)
+    #print("maxF",maxF)
+    forces = np.clip(tau, -maxF, maxF)
+
+    #np.savetxt("pb_tau_clamped.csv", tau, delimiter=",")
+    return forces
+
+
+class PDControllerStable(object):
+
+  def __init__(self, pb):
+    self._pb = pb
+
+  def computePD(self, bodyUniqueId, jointIndices, desiredPositions, desiredVelocities, kps, kds,
+                maxForces, timeStep):
+    numBaseDofs = 0
+    numPosBaseDofs = 0
+    baseMass = self._pb.getDynamicsInfo(bodyUniqueId, -1)[0]
+    curPos, curOrn = self._pb.getBasePositionAndOrientation(bodyUniqueId)
+    q1 = []
+    qdot1 = []
+    zeroAccelerations = []
+    qError = []
+    if (baseMass > 0):
+      numBaseDofs = 6
+      numPosBaseDofs = 7
+      q1 = [curPos[0], curPos[1], curPos[2], curOrn[0], curOrn[1], curOrn[2], curOrn[3]]
+      qdot1 = [0] * numBaseDofs
+      zeroAccelerations = [0] * numBaseDofs
+      angDiff = [0, 0, 0]
+      qError = [
+          desiredPositions[0] - curPos[0], desiredPositions[1] - curPos[1],
+          desiredPositions[2] - curPos[2], angDiff[0], angDiff[1], angDiff[2]
+      ]
+    numJoints = self._pb.getNumJoints(bodyUniqueId)
+    jointStates = self._pb.getJointStates(bodyUniqueId, jointIndices)
+
+    for i in range(numJoints):
+      q1.append(jointStates[i][0])
+      qdot1.append(jointStates[i][1])
+      zeroAccelerations.append(0)
+    q = np.array(q1)
+    qdot = np.array(qdot1)
+    qdes = np.array(desiredPositions)
+    qdotdes = np.array(desiredVelocities)
+    #qError = qdes - q
+    for j in range(numJoints):
+      qError.append(desiredPositions[j + numPosBaseDofs] - q1[j + numPosBaseDofs])
+    #print("qError=",qError)
+    qdotError = qdotdes - qdot
+    Kp = np.diagflat(kps)
+    Kd = np.diagflat(kds)
+    p = Kp.dot(qError)
+    d = Kd.dot(qdotError)
+    forces = p + d
+
+    M1 = self._pb.calculateMassMatrix(bodyUniqueId, q1)
+    M2 = np.array(M1)
+    M = (M2 + Kd * timeStep)
+    c1 = self._pb.calculateInverseDynamics(bodyUniqueId, q1, qdot1, zeroAccelerations)
+    c = np.array(c1)
+    A = M
+    b = -c + p + d
+    qddot = np.linalg.solve(A, b)
+    tau = p + d - Kd.dot(qddot) * timeStep
+    maxF = np.array(maxForces)
+    forces = np.clip(tau, -maxF, maxF)
+    #print("c=",c)
+    return tau
diff --git a/examples/pybullet/gym/pybullet_utils/urdfEditor.py b/examples/pybullet/gym/pybullet_utils/urdfEditor.py
index 3edca4596..fc971ae3f 100644
--- a/examples/pybullet/gym/pybullet_utils/urdfEditor.py
+++ b/examples/pybullet/gym/pybullet_utils/urdfEditor.py
@@ -1,603 +1,640 @@
 import pybullet as p
 import time
 
+
 class UrdfInertial(object):
-	def __init__(self):
-		self.mass = 1
-		self.inertia_xxyyzz=[7,8,9]
-		self.origin_rpy=[1,2,3]
-		self.origin_xyz=[4,5,6]
+
+  def __init__(self):
+    self.mass = 1
+    self.inertia_xxyyzz = [7, 8, 9]
+    self.origin_rpy = [1, 2, 3]
+    self.origin_xyz = [4, 5, 6]
+
 
 class UrdfContact(object):
-	def __init__(self):
-		self.lateral_friction = 1
-		self.rolling_friction = 0
-		self.spinning_friction = 0
+
+  def __init__(self):
+    self.lateral_friction = 1
+    self.rolling_friction = 0
+    self.spinning_friction = 0
+
 
 class UrdfLink(object):
-	def __init__(self):
-		self.link_name = "dummy"
-		self.urdf_inertial = UrdfInertial()
-		self.urdf_visual_shapes=[]
-		self.urdf_collision_shapes=[]
+
+  def __init__(self):
+    self.link_name = "dummy"
+    self.urdf_inertial = UrdfInertial()
+    self.urdf_visual_shapes = []
+    self.urdf_collision_shapes = []
+
 
 class UrdfVisual(object):
-	def __init__(self):
-		self.origin_rpy = [1,2,3]
-		self.origin_xyz = [4,5,6]
-		self.geom_type = p.GEOM_BOX
-		self.geom_radius = 1
-		self.geom_extents = [7,8,9]
-		self.geom_length=10
-		self.geom_meshfilename = "meshfile"
-		self.geom_meshscale=[1,1,1]
-		self.material_rgba = [1,0,0,1]
-		self.material_name = ""
+
+  def __init__(self):
+    self.origin_rpy = [1, 2, 3]
+    self.origin_xyz = [4, 5, 6]
+    self.geom_type = p.GEOM_BOX
+    self.geom_radius = 1
+    self.geom_extents = [7, 8, 9]
+    self.geom_length = 10
+    self.geom_meshfilename = "meshfile"
+    self.geom_meshscale = [1, 1, 1]
+    self.material_rgba = [1, 0, 0, 1]
+    self.material_name = ""
+
 
 class UrdfCollision(object):
-	def __init__(self):
-		self.origin_rpy = [1,2,3]
-		self.origin_xyz = [4,5,6]
-		self.geom_type = p.GEOM_BOX
-		self.geom_radius = 1
-		self.geom_length = 2
-		self.geom_extents = [7,8,9]
-		self.geom_meshfilename = "meshfile"
-		self.geom_meshscale = [1,1,1]
+
+  def __init__(self):
+    self.origin_rpy = [1, 2, 3]
+    self.origin_xyz = [4, 5, 6]
+    self.geom_type = p.GEOM_BOX
+    self.geom_radius = 1
+    self.geom_length = 2
+    self.geom_extents = [7, 8, 9]
+    self.geom_meshfilename = "meshfile"
+    self.geom_meshscale = [1, 1, 1]
+
+
 class UrdfJoint(object):
-	def __init__(self):
-		self.link = UrdfLink()
-		self.joint_name = "joint_dummy"
-		self.joint_type = p.JOINT_REVOLUTE
-		self.joint_lower_limit = 0
-		self.joint_upper_limit = -1
-		self.parent_name = "parentName"
-		self.child_name = "childName"
-		self.joint_origin_xyz = [1,2,3]
-		self.joint_origin_rpy = [1,2,3]
-		self.joint_axis_xyz = [1,2,3]
 
-class UrdfEditor(object):
-	def __init__(self):
-		self.initialize()
-
-	def initialize(self):
-		self.urdfLinks=[]
-		self.urdfJoints=[]
-		self.robotName = ""
-		self.linkNameToIndex={}
-		self.jointTypeToName={p.JOINT_FIXED:"JOINT_FIXED" ,\
-													p.JOINT_REVOLUTE:"JOINT_REVOLUTE",\
-													p.JOINT_PRISMATIC:"JOINT_PRISMATIC" }
-
-
-	def convertLinkFromMultiBody(self, bodyUid, linkIndex, urdfLink, physicsClientId):
-		dyn = p.getDynamicsInfo(bodyUid,linkIndex,physicsClientId=physicsClientId)
-		urdfLink.urdf_inertial.mass = dyn[0]
-		urdfLink.urdf_inertial.inertia_xxyyzz = dyn[2]
-		#todo
-		urdfLink.urdf_inertial.origin_xyz = dyn[3]
-		rpy = p.getEulerFromQuaternion(dyn[4])
-		urdfLink.urdf_inertial.origin_rpy = rpy
-
-		visualShapes = p.getVisualShapeData(bodyUid,physicsClientId=physicsClientId)
-		matIndex = 0
-		for v in visualShapes:
-			if (v[1]==linkIndex):
-				urdfVisual = UrdfVisual()
-				urdfVisual.geom_type = v[2]
-				if (v[2]==p.GEOM_BOX):
-					urdfVisual.geom_extents = v[3]
-				if (v[2]==p.GEOM_SPHERE):
-					urdfVisual.geom_radius = v[3][0]
-				if (v[2]==p.GEOM_MESH):
-					urdfVisual.geom_meshfilename = v[4].decode("utf-8")
-					urdfVisual.geom_meshscale = v[3]
-				if (v[2]==p.GEOM_CYLINDER):
-					urdfVisual.geom_length=v[3][0]
-					urdfVisual.geom_radius=v[3][1]
-				if (v[2]==p.GEOM_CAPSULE):
-					urdfVisual.geom_length=v[3][0]
-					urdfVisual.geom_radius=v[3][1]
-				urdfVisual.origin_xyz = v[5]
-				urdfVisual.origin_rpy = p.getEulerFromQuaternion(v[6])
-				urdfVisual.material_rgba = v[7]
-				name = 'mat_{}_{}'.format(linkIndex,matIndex)
-				urdfVisual.material_name = name
-				urdfLink.urdf_visual_shapes.append(urdfVisual)
-				matIndex=matIndex+1
-
-		collisionShapes = p.getCollisionShapeData(bodyUid, linkIndex,physicsClientId=physicsClientId)
-		for v in collisionShapes:
-			urdfCollision = UrdfCollision()
-			urdfCollision.geom_type = v[2]
-			if (v[2]==p.GEOM_BOX):
-				urdfCollision.geom_extents = v[3]
-			if (v[2]==p.GEOM_SPHERE):
-				urdfCollision.geom_radius = v[3][0]
-			if (v[2]==p.GEOM_MESH):
-				urdfCollision.geom_meshfilename = v[4].decode("utf-8")
-				urdfCollision.geom_meshscale = v[3]
-			if (v[2]==p.GEOM_CYLINDER):
-				urdfCollision.geom_length=v[3][0]
-				urdfCollision.geom_radius=v[3][1]
-			if (v[2]==p.GEOM_CAPSULE):
-					urdfCollision.geom_length=v[3][0]
-					urdfCollision.geom_radius=v[3][1]
-			pos,orn = p.multiplyTransforms(dyn[3],dyn[4],\
-				v[5], v[6])
-			urdfCollision.origin_xyz = pos
-			urdfCollision.origin_rpy = p.getEulerFromQuaternion(orn)
-			urdfLink.urdf_collision_shapes.append(urdfCollision)
-
-	def initializeFromBulletBody(self, bodyUid, physicsClientId):
-		self.initialize()
-
-		#always create a base link
-		baseLink = UrdfLink()
-		baseLinkIndex = -1
-		self.convertLinkFromMultiBody(bodyUid, baseLinkIndex, baseLink, physicsClientId)
-		baseLink.link_name = 	p.getBodyInfo(bodyUid, physicsClientId=physicsClientId)[0].decode("utf-8")
-		self.linkNameToIndex[baseLink.link_name]=len(self.urdfLinks)
-		self.urdfLinks.append(baseLink)
-
-		#optionally create child links and joints
-		for j in range(p.getNumJoints(bodyUid,physicsClientId=physicsClientId)):
-			jointInfo = p.getJointInfo(bodyUid,j,physicsClientId=physicsClientId)
-			urdfLink = UrdfLink()
-			self.convertLinkFromMultiBody(bodyUid, j, urdfLink,physicsClientId)
-			urdfLink.link_name = jointInfo[12].decode("utf-8")
-			self.linkNameToIndex[urdfLink.link_name]=len(self.urdfLinks)
-			self.urdfLinks.append(urdfLink)
-
-			urdfJoint = UrdfJoint()
-			urdfJoint.link = urdfLink
-			urdfJoint.joint_name = jointInfo[1].decode("utf-8")
-			urdfJoint.joint_type = jointInfo[2]
-			urdfJoint.joint_axis_xyz = jointInfo[13]
-			orgParentIndex = jointInfo[16]
-			if (orgParentIndex<0):
-				urdfJoint.parent_name = baseLink.link_name
-			else:
-				parentJointInfo = p.getJointInfo(bodyUid,orgParentIndex,physicsClientId=physicsClientId)
-				urdfJoint.parent_name = parentJointInfo[12].decode("utf-8")
-			urdfJoint.child_name = urdfLink.link_name
-
-			#todo, compensate for inertia/link frame offset
-
-			dynChild = p.getDynamicsInfo(bodyUid,j,physicsClientId=physicsClientId)
-			childInertiaPos = dynChild[3]
-			childInertiaOrn = dynChild[4]
-			parentCom2JointPos=jointInfo[14]
-			parentCom2JointOrn=jointInfo[15]
-			tmpPos,tmpOrn = p.multiplyTransforms(childInertiaPos,childInertiaOrn,parentCom2JointPos,parentCom2JointOrn)
-			tmpPosInv,tmpOrnInv = p.invertTransform(tmpPos,tmpOrn)
-			dynParent = p.getDynamicsInfo(bodyUid,orgParentIndex,physicsClientId=physicsClientId)
-			parentInertiaPos = dynParent[3]
-			parentInertiaOrn = dynParent[4]
-
-			pos,orn = p.multiplyTransforms(parentInertiaPos,parentInertiaOrn, tmpPosInv, tmpOrnInv)
-			pos,orn_unused=p.multiplyTransforms(parentInertiaPos,parentInertiaOrn, parentCom2JointPos,[0,0,0,1])
-
-			urdfJoint.joint_origin_xyz = pos
-			urdfJoint.joint_origin_rpy = p.getEulerFromQuaternion(orn)
-
-			self.urdfJoints.append(urdfJoint)
-
-	def writeInertial(self,file,urdfInertial, precision=5):
-		file.write("\t\t<inertial>\n")
-		str = '\t\t\t<origin rpy=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\" xyz=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(\
-		urdfInertial.origin_rpy[0],urdfInertial.origin_rpy[1],urdfInertial.origin_rpy[2],\
-		urdfInertial.origin_xyz[0],urdfInertial.origin_xyz[1],urdfInertial.origin_xyz[2], prec=precision)
-		file.write(str)
-		str = '\t\t\t<mass value=\"{:.{prec}f}\"/>\n'.format(urdfInertial.mass,prec=precision)
-		file.write(str)
-		str = '\t\t\t<inertia ixx=\"{:.{prec}f}\" ixy=\"0\" ixz=\"0\" iyy=\"{:.{prec}f}\" iyz=\"0\" izz=\"{:.{prec}f}\"/>\n'.format(\
-		urdfInertial.inertia_xxyyzz[0],\
-		urdfInertial.inertia_xxyyzz[1],\
-		urdfInertial.inertia_xxyyzz[2],prec=precision)
-		file.write(str)
-		file.write("\t\t</inertial>\n")
-
-	def writeVisualShape(self,file,urdfVisual, precision=5):
-		#we don't support loading capsule types from visuals, so auto-convert from collision shape
-		if urdfVisual.geom_type == p.GEOM_CAPSULE:
-			return
-                
-		file.write("\t\t<visual>\n")
-		str = '\t\t\t<origin rpy="{:.{prec}f} {:.{prec}f} {:.{prec}f}" xyz="{:.{prec}f} {:.{prec}f} {:.{prec}f}"/>\n'.format(\
-			urdfVisual.origin_rpy[0],urdfVisual.origin_rpy[1],urdfVisual.origin_rpy[2],
-			urdfVisual.origin_xyz[0],urdfVisual.origin_xyz[1],urdfVisual.origin_xyz[2], prec=precision)
-		file.write(str)
-		file.write("\t\t\t<geometry>\n")
-		if urdfVisual.geom_type == p.GEOM_BOX:
-			str = '\t\t\t\t<box size=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(urdfVisual.geom_extents[0],\
-				urdfVisual.geom_extents[1],urdfVisual.geom_extents[2], prec=precision)
-			file.write(str)
-		if urdfVisual.geom_type == p.GEOM_SPHERE:
-			str = '\t\t\t\t<sphere radius=\"{:.{prec}f}\"/>\n'.format(urdfVisual.geom_radius,\
-				prec=precision)
-			file.write(str)
-		if urdfVisual.geom_type == p.GEOM_MESH:
-			
-			str = '\t\t\t\t<mesh filename=\"{}\" scale=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(
-			urdfVisual.geom_meshfilename,urdfVisual.geom_meshscale[0],urdfVisual.geom_meshscale[1],urdfVisual.geom_meshscale[2],\
-				prec=precision)
-			file.write(str)
-		if urdfVisual.geom_type == p.GEOM_CYLINDER:
-			str = '\t\t\t\t<cylinder length=\"{:.{prec}f}\" radius=\"{:.{prec}f}\"/>\n'.format(\
-				urdfVisual.geom_length, urdfVisual.geom_radius, prec=precision)
-			file.write(str)
-		if urdfVisual.geom_type == p.GEOM_CAPSULE:
-			str = '\t\t\t\t<capsule length=\"{:.{prec}f}\" radius=\"{:.{prec}f}\"/>\n'.format(\
-				urdfVisual.geom_length, urdfVisual.geom_radius, prec=precision)
-			file.write(str)
-
-		file.write("\t\t\t</geometry>\n")
-		str = '\t\t\t<material name=\"{}\">\n'.format(urdfVisual.material_name)
-		file.write(str)
-		str = '\t\t\t\t<color rgba="{:.{prec}f} {:.{prec}f} {:.{prec}f} {:.{prec}f}" />\n'.format(urdfVisual.material_rgba[0],\
-			urdfVisual.material_rgba[1],urdfVisual.material_rgba[2],urdfVisual.material_rgba[3],prec=precision)
-		file.write(str)
-		file.write("\t\t\t</material>\n")
-		file.write("\t\t</visual>\n")
-
-	def writeCollisionShape(self,file,urdfCollision, precision=5):
-		file.write("\t\t<collision>\n")
-		str = '\t\t\t<origin rpy="{:.{prec}f} {:.{prec}f} {:.{prec}f}" xyz="{:.{prec}f} {:.{prec}f} {:.{prec}f}"/>\n'.format(\
-			urdfCollision.origin_rpy[0],urdfCollision.origin_rpy[1],urdfCollision.origin_rpy[2],
-			urdfCollision.origin_xyz[0],urdfCollision.origin_xyz[1],urdfCollision.origin_xyz[2], prec=precision)
-		file.write(str)
-		file.write("\t\t\t<geometry>\n")
-		if urdfCollision.geom_type == p.GEOM_BOX:
-			str = '\t\t\t\t<box size=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(urdfCollision.geom_extents[0],\
-				urdfCollision.geom_extents[1],urdfCollision.geom_extents[2], prec=precision)
-			file.write(str)
-		if urdfCollision.geom_type == p.GEOM_SPHERE:
-			str = '\t\t\t\t<sphere radius=\"{:.{prec}f}\"/>\n'.format(urdfCollision.geom_radius,\
-				prec=precision)
-			file.write(str)
-		if urdfCollision.geom_type == p.GEOM_MESH:
-			str = '\t\t\t\t<mesh filename=\"{}\" scale=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(urdfCollision.geom_meshfilename,\
-				urdfCollision.geom_meshscale[0],urdfCollision.geom_meshscale[1],urdfCollision.geom_meshscale[2],prec=precision)
-			file.write(str)
-		if urdfCollision.geom_type == p.GEOM_CYLINDER:
-			str = '\t\t\t\t<cylinder length=\"{:.{prec}f}\" radius=\"{:.{prec}f}\"/>\n'.format(\
-				urdfCollision.geom_length, urdfCollision.geom_radius, prec=precision)
-			file.write(str)
-		if urdfCollision.geom_type == p.GEOM_CAPSULE:
-			str = '\t\t\t\t<capsule length=\"{:.{prec}f}\" radius=\"{:.{prec}f}\"/>\n'.format(\
-				urdfCollision.geom_length, urdfCollision.geom_radius, prec=precision)
-			file.write(str)
-		file.write("\t\t\t</geometry>\n")
-		file.write("\t\t</collision>\n")
-
-
-	def writeLink(self, file, urdfLink,saveVisuals):
-		file.write("\t<link name=\"")
-		file.write(urdfLink.link_name)
-		file.write("\">\n")
-
-		self.writeInertial(file,urdfLink.urdf_inertial)
-		hasCapsules = False
-		for v in urdfLink.urdf_visual_shapes:
-			if (v.geom_type == p.GEOM_CAPSULE):
-				hasCapsules = True
-		if (saveVisuals and not hasCapsules):
-				for v in urdfLink.urdf_visual_shapes:
-					self.writeVisualShape(file,v)
-		for c in urdfLink.urdf_collision_shapes:
-			self.writeCollisionShape(file,c)
-		file.write("\t</link>\n")
-
-	def writeJoint(self, file, urdfJoint, precision=5):
-		jointTypeStr = "invalid"
-		if urdfJoint.joint_type == p.JOINT_REVOLUTE:
-			if urdfJoint.joint_upper_limit < urdfJoint.joint_lower_limit:
-				jointTypeStr = "continuous"
-			else:
-				jointTypeStr = "revolute"
-		if urdfJoint.joint_type == p.JOINT_FIXED:
-			jointTypeStr  = "fixed"
-		if urdfJoint.joint_type == p.JOINT_PRISMATIC:
-			jointTypeStr  = "prismatic"
-		str = '\t<joint name=\"{}\" type=\"{}\">\n'.format(urdfJoint.joint_name, jointTypeStr)
-		file.write(str)
-		str = '\t\t<parent link=\"{}\"/>\n'.format(urdfJoint.parent_name)
-		file.write(str)
-		str = '\t\t<child link=\"{}\"/>\n'.format(urdfJoint.child_name)
-		file.write(str)
-
-		if urdfJoint.joint_type == p.JOINT_PRISMATIC:
-			#todo: handle limits
-			lowerLimit=-0.5
-			upperLimit=0.5
-			str='<limit effort="1000.0" lower="{:.{prec}f}" upper="{:.{prec}f}" velocity="0.5"/>'.format(lowerLimit,upperLimit,prec=precision)
-			file.write(str)
-
-		file.write("\t\t<dynamics damping=\"1.0\" friction=\"0.0001\"/>\n")
-		str = '\t\t<origin xyz=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(urdfJoint.joint_origin_xyz[0],\
-			urdfJoint.joint_origin_xyz[1],urdfJoint.joint_origin_xyz[2], prec=precision)
-		str = '\t\t<origin rpy=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\" xyz=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(urdfJoint.joint_origin_rpy[0],\
-			urdfJoint.joint_origin_rpy[1],urdfJoint.joint_origin_rpy[2],\
-			urdfJoint.joint_origin_xyz[0],\
-			urdfJoint.joint_origin_xyz[1],urdfJoint.joint_origin_xyz[2], prec=precision)
-
-		file.write(str)
-		str = '\t\t<axis xyz=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(urdfJoint.joint_axis_xyz[0],\
-			urdfJoint.joint_axis_xyz[1],urdfJoint.joint_axis_xyz[2], prec=precision)
-		file.write(str)
-		file.write("\t</joint>\n")
-
-	def saveUrdf(self, fileName, saveVisuals=True):
-		file = open(fileName,"w")
-		file.write("<?xml version=\"0.0\" ?>\n")
-		file.write("<robot name=\"")
-		file.write(self.robotName)
-		file.write("\">\n")
-
-		for link in self.urdfLinks:
-			self.writeLink(file,link, saveVisuals)
-
-		for joint in self.urdfJoints:
-			self.writeJoint(file,joint)
-
-		file.write("</robot>\n")
-		file.close()
-
-  	#def addLink(...)
-
-
-
-
-	def joinUrdf(self, childEditor, parentLinkIndex=0, jointPivotXYZInParent=[0,0,0], jointPivotRPYInParent=[0,0,0], jointPivotXYZInChild=[0,0,0], jointPivotRPYInChild=[0,0,0], parentPhysicsClientId=0, childPhysicsClientId=0):
-
-		childLinkIndex = len(self.urdfLinks)
-		insertJointIndex = len(self.urdfJoints)
-		
-		#combine all links, and add a joint
-
-		for link in childEditor.urdfLinks:
-			self.linkNameToIndex[link.link_name]=len(self.urdfLinks)
-			self.urdfLinks.append(link)
-		for joint in childEditor.urdfJoints:
-			self.urdfJoints.append(joint)
-		#add a new joint between a particular
-
-
-		
-		
-		jointPivotQuatInChild = p.getQuaternionFromEuler(jointPivotRPYInChild)
-		invJointPivotXYZInChild, invJointPivotQuatInChild = p.invertTransform(jointPivotXYZInChild,jointPivotQuatInChild)
-
-		#apply this invJointPivot***InChild to all inertial, visual and collision element in the child link
-		#inertial
-		pos, orn = p.multiplyTransforms(self.urdfLinks[childLinkIndex].urdf_inertial.origin_xyz,p.getQuaternionFromEuler(self.urdfLinks[childLinkIndex].urdf_inertial.origin_rpy),invJointPivotXYZInChild,invJointPivotQuatInChild, physicsClientId=parentPhysicsClientId)
-		self.urdfLinks[childLinkIndex].urdf_inertial.origin_xyz = pos
-		self.urdfLinks[childLinkIndex].urdf_inertial.origin_rpy = p.getEulerFromQuaternion(orn)
-		#all visual
-		for v in self.urdfLinks[childLinkIndex].urdf_visual_shapes:
-			pos, orn = p.multiplyTransforms(v.origin_xyz,p.getQuaternionFromEuler(v.origin_rpy),invJointPivotXYZInChild,invJointPivotQuatInChild)
-			v.origin_xyz = pos
-			v.origin_rpy = p.getEulerFromQuaternion(orn)
-		#all collision
-		for c in self.urdfLinks[childLinkIndex].urdf_collision_shapes:
-			pos, orn = p.multiplyTransforms(c.origin_xyz,p.getQuaternionFromEuler(c.origin_rpy),invJointPivotXYZInChild,invJointPivotQuatInChild)
-			c.origin_xyz = pos
-			c.origin_rpy = p.getEulerFromQuaternion(orn)
-
-
-		childLink = self.urdfLinks[childLinkIndex]
-		parentLink = self.urdfLinks[parentLinkIndex]
-
-		joint = UrdfJoint()
-		joint.link = childLink
-		joint.joint_name = "joint_dummy1"
-		joint.joint_type = p.JOINT_REVOLUTE
-		joint.joint_lower_limit = 0
-		joint.joint_upper_limit = -1
-		joint.parent_name = parentLink.link_name
-		joint.child_name = childLink.link_name
-		joint.joint_origin_xyz = jointPivotXYZInParent
-		joint.joint_origin_rpy = jointPivotRPYInParent
-		joint.joint_axis_xyz = [0,0,1]
-
-		#the following commented line would crash PyBullet, it messes up the joint indexing/ordering
-		#self.urdfJoints.append(joint)
-
-		#so make sure to insert the joint in the right place, to links/joints match
-		self.urdfJoints.insert(insertJointIndex,joint)
-		return joint
-
-
-
-	def createMultiBody(self, basePosition=[0,0,0],baseOrientation=[0,0,0,1], physicsClientId=0):
-		#assume link[0] is base
-		if (len(self.urdfLinks)==0):
-			return -1
-
-		#for i in range (len(self.urdfLinks)):
-		#	print("link", i, "=",self.urdfLinks[i].link_name)
-
-
-		base = self.urdfLinks[0]
-
-		#v.tmp_collision_shape_ids=[]
-		baseMass = base.urdf_inertial.mass
-		baseCollisionShapeIndex = -1
-		baseShapeTypeArray=[]
-		baseRadiusArray=[]
-		baseHalfExtentsArray=[]
-		lengthsArray=[]
-		fileNameArray=[]
-		meshScaleArray=[]
-		basePositionsArray=[]
-		baseOrientationsArray=[]
-
-		for v in base.urdf_collision_shapes:
-			shapeType = v.geom_type
-			baseShapeTypeArray.append(shapeType)
-			baseHalfExtentsArray.append([0.5*v.geom_extents[0],0.5*v.geom_extents[1],0.5*v.geom_extents[2]])
-			baseRadiusArray.append(v.geom_radius)
-			lengthsArray.append(v.geom_length)
-			fileNameArray.append(v.geom_meshfilename)
-			meshScaleArray.append(v.geom_meshscale)
-			basePositionsArray.append(v.origin_xyz)
-			orn=p.getQuaternionFromEuler(v.origin_rpy)
-			baseOrientationsArray.append(orn)
-
-		if (len(baseShapeTypeArray)):
-			#print("fileNameArray=",fileNameArray)
-			baseCollisionShapeIndex = p.createCollisionShapeArray(shapeTypes=baseShapeTypeArray,
-					radii=baseRadiusArray,
-					halfExtents=baseHalfExtentsArray,
-					lengths=lengthsArray,
-					fileNames=fileNameArray,
-					meshScales=meshScaleArray,
-					collisionFramePositions=basePositionsArray,
-					collisionFrameOrientations=baseOrientationsArray,
-					physicsClientId=physicsClientId)
-
-
-		urdfVisuals = base.urdf_visual_shapes
-		
-		shapeTypes=[v.geom_type for v in urdfVisuals]
-		halfExtents=[[ext * 0.5 for ext in v.geom_extents] for v in urdfVisuals]
-		radii=[v.geom_radius for v in urdfVisuals]
-		lengths=[v.geom_length for v in urdfVisuals]
-		fileNames=[v.geom_meshfilename for v in urdfVisuals]
-		meshScales=[v.geom_meshscale for v in urdfVisuals]
-		rgbaColors=[v.material_rgba for v in urdfVisuals]
-		visualFramePositions=[v.origin_xyz for v in urdfVisuals]
-		visualFrameOrientations=[p.getQuaternionFromEuler(v.origin_rpy) for v in urdfVisuals]                                                       
-		baseVisualShapeIndex = -1
-
-		if (len(shapeTypes)):
-			#print("len(shapeTypes)=",len(shapeTypes))
-			#print("len(halfExtents)=",len(halfExtents))
-			#print("len(radii)=",len(radii))
-			#print("len(lengths)=",len(lengths))
-			#print("len(fileNames)=",len(fileNames))
-			#print("len(meshScales)=",len(meshScales))
-			#print("len(rgbaColors)=",len(rgbaColors))
-			#print("len(visualFramePositions)=",len(visualFramePositions))
-			#print("len(visualFrameOrientations)=",len(visualFrameOrientations))
-			
-                                                           
-			baseVisualShapeIndex = p.createVisualShapeArray(shapeTypes=shapeTypes,
-						halfExtents=halfExtents,radii=radii,lengths=lengths,fileNames=fileNames,
-						meshScales=meshScales,rgbaColors=rgbaColors,visualFramePositions=visualFramePositions,
-						visualFrameOrientations=visualFrameOrientations,physicsClientId=physicsClientId)
-
-		linkMasses=[]
-		linkCollisionShapeIndices=[]
-		linkVisualShapeIndices=[]
-		linkPositions=[]
-		linkOrientations=[]
-		
-		linkInertialFramePositions=[]
-		linkInertialFrameOrientations=[]
-		linkParentIndices=[]
-		linkJointTypes=[]
-		linkJointAxis=[]
-
-		for joint in self.urdfJoints:
-			link = joint.link
-			linkMass = link.urdf_inertial.mass
-			linkCollisionShapeIndex=-1
-			linkVisualShapeIndex=-1
-			linkPosition=[0,0,0]
-			linkOrientation=[0,0,0]
-			linkInertialFramePosition=[0,0,0]
-			linkInertialFrameOrientation=[0,0,0]
-			linkParentIndex=self.linkNameToIndex[joint.parent_name]
-			linkJointType=joint.joint_type
-			linkJointAx = joint.joint_axis_xyz
-			linkShapeTypeArray=[]
-			linkRadiusArray=[]
-			linkHalfExtentsArray=[]
-			lengthsArray=[]
-			fileNameArray=[]
-			linkMeshScaleArray=[]
-			linkPositionsArray=[]
-			linkOrientationsArray=[]
-			
-			for v in link.urdf_collision_shapes:
-				shapeType = v.geom_type
-				linkShapeTypeArray.append(shapeType)
-				linkHalfExtentsArray.append([0.5*v.geom_extents[0],0.5*v.geom_extents[1],0.5*v.geom_extents[2]])
-				linkRadiusArray.append(v.geom_radius)
-				lengthsArray.append(v.geom_length)
-				fileNameArray.append(v.geom_meshfilename)
-				linkMeshScaleArray.append(v.geom_meshscale)
-				linkPositionsArray.append(v.origin_xyz)
-				linkOrientationsArray.append(p.getQuaternionFromEuler(v.origin_rpy))
-
-			if (len(linkShapeTypeArray)):
-				linkCollisionShapeIndex = p.createCollisionShapeArray(shapeTypes=linkShapeTypeArray,
-					radii=linkRadiusArray,
-					halfExtents=linkHalfExtentsArray,
-					lengths=lengthsArray,
-					fileNames=fileNameArray,
-					meshScales=linkMeshScaleArray,
-					collisionFramePositions=linkPositionsArray,
-					collisionFrameOrientations=linkOrientationsArray,
-					physicsClientId=physicsClientId)
-					
-			urdfVisuals = link.urdf_visual_shapes
-			linkVisualShapeIndex = -1
-			shapeTypes=[v.geom_type for v in urdfVisuals]
-			halfExtents=[[ext * 0.5 for ext in v.geom_extents] for v in urdfVisuals]
-			radii=[v.geom_radius for v in urdfVisuals]
-			lengths=[v.geom_length for v in urdfVisuals]
-			fileNames=[v.geom_meshfilename for v in urdfVisuals]
-			meshScales=[v.geom_meshscale for v in urdfVisuals]
-			rgbaColors=[v.material_rgba for v in urdfVisuals]
-			visualFramePositions=[v.origin_xyz for v in urdfVisuals]
-			visualFrameOrientations=[p.getQuaternionFromEuler(v.origin_rpy) for v in urdfVisuals]
-				
-			if (len(shapeTypes)):
-				print("fileNames=",fileNames)
-
-				linkVisualShapeIndex = p.createVisualShapeArray(shapeTypes=shapeTypes,
-							halfExtents=halfExtents,radii=radii,lengths=lengths,
-							fileNames=fileNames,meshScales=meshScales,rgbaColors=rgbaColors,
-							visualFramePositions=visualFramePositions,
-							visualFrameOrientations=visualFrameOrientations,
-							physicsClientId=physicsClientId)
-
-			linkMasses.append(linkMass)
-			linkCollisionShapeIndices.append(linkCollisionShapeIndex)
-			linkVisualShapeIndices.append(linkVisualShapeIndex)
-			linkPositions.append(joint.joint_origin_xyz)
-			linkOrientations.append(p.getQuaternionFromEuler(joint.joint_origin_rpy))
-			linkInertialFramePositions.append(link.urdf_inertial.origin_xyz)
-			linkInertialFrameOrientations.append(p.getQuaternionFromEuler(link.urdf_inertial.origin_rpy))
-			linkParentIndices.append(linkParentIndex)
-			linkJointTypes.append(joint.joint_type)
-			linkJointAxis.append(joint.joint_axis_xyz)
-		obUid = p.createMultiBody(baseMass,\
-					baseCollisionShapeIndex=baseCollisionShapeIndex,
-                                        baseVisualShapeIndex=baseVisualShapeIndex,
-					basePosition=basePosition,
-					baseOrientation=baseOrientation,
-					baseInertialFramePosition=base.urdf_inertial.origin_xyz,
-					baseInertialFrameOrientation=p.getQuaternionFromEuler(base.urdf_inertial.origin_rpy),
-					linkMasses=linkMasses,
-					linkCollisionShapeIndices=linkCollisionShapeIndices,
-					linkVisualShapeIndices=linkVisualShapeIndices,
-					linkPositions=linkPositions,
-					linkOrientations=linkOrientations,
-					linkInertialFramePositions=linkInertialFramePositions,
-					linkInertialFrameOrientations=linkInertialFrameOrientations,
-					linkParentIndices=linkParentIndices,
-					linkJointTypes=linkJointTypes,
-					linkJointAxis=linkJointAxis,
-					physicsClientId=physicsClientId)
-		return obUid
-
-	def __del__(self):
-		pass
+  def __init__(self):
+    self.link = UrdfLink()
+    self.joint_name = "joint_dummy"
+    self.joint_type = p.JOINT_REVOLUTE
+    self.joint_lower_limit = 0
+    self.joint_upper_limit = -1
+    self.parent_name = "parentName"
+    self.child_name = "childName"
+    self.joint_origin_xyz = [1, 2, 3]
+    self.joint_origin_rpy = [1, 2, 3]
+    self.joint_axis_xyz = [1, 2, 3]
 
 
+class UrdfEditor(object):
 
+  def __init__(self):
+    self.initialize()
+
+  def initialize(self):
+    self.urdfLinks = []
+    self.urdfJoints = []
+    self.robotName = ""
+    self.linkNameToIndex = {}
+    self.jointTypeToName={p.JOINT_FIXED: "JOINT_FIXED" ,\
+               p.JOINT_REVOLUTE: "JOINT_REVOLUTE",\
+               p.JOINT_PRISMATIC: "JOINT_PRISMATIC" }
+
+  def convertLinkFromMultiBody(self, bodyUid, linkIndex, urdfLink, physicsClientId):
+    dyn = p.getDynamicsInfo(bodyUid, linkIndex, physicsClientId=physicsClientId)
+    urdfLink.urdf_inertial.mass = dyn[0]
+    urdfLink.urdf_inertial.inertia_xxyyzz = dyn[2]
+    #todo
+    urdfLink.urdf_inertial.origin_xyz = dyn[3]
+    rpy = p.getEulerFromQuaternion(dyn[4])
+    urdfLink.urdf_inertial.origin_rpy = rpy
+
+    visualShapes = p.getVisualShapeData(bodyUid, physicsClientId=physicsClientId)
+    matIndex = 0
+    for v in visualShapes:
+      if (v[1] == linkIndex):
+        urdfVisual = UrdfVisual()
+        urdfVisual.geom_type = v[2]
+        if (v[2] == p.GEOM_BOX):
+          urdfVisual.geom_extents = v[3]
+        if (v[2] == p.GEOM_SPHERE):
+          urdfVisual.geom_radius = v[3][0]
+        if (v[2] == p.GEOM_MESH):
+          urdfVisual.geom_meshfilename = v[4].decode("utf-8")
+          urdfVisual.geom_meshscale = v[3]
+        if (v[2] == p.GEOM_CYLINDER):
+          urdfVisual.geom_length = v[3][0]
+          urdfVisual.geom_radius = v[3][1]
+        if (v[2] == p.GEOM_CAPSULE):
+          urdfVisual.geom_length = v[3][0]
+          urdfVisual.geom_radius = v[3][1]
+        urdfVisual.origin_xyz = v[5]
+        urdfVisual.origin_rpy = p.getEulerFromQuaternion(v[6])
+        urdfVisual.material_rgba = v[7]
+        name = 'mat_{}_{}'.format(linkIndex, matIndex)
+        urdfVisual.material_name = name
+        urdfLink.urdf_visual_shapes.append(urdfVisual)
+        matIndex = matIndex + 1
+
+    collisionShapes = p.getCollisionShapeData(bodyUid, linkIndex, physicsClientId=physicsClientId)
+    for v in collisionShapes:
+      urdfCollision = UrdfCollision()
+      urdfCollision.geom_type = v[2]
+      if (v[2] == p.GEOM_BOX):
+        urdfCollision.geom_extents = v[3]
+      if (v[2] == p.GEOM_SPHERE):
+        urdfCollision.geom_radius = v[3][0]
+      if (v[2] == p.GEOM_MESH):
+        urdfCollision.geom_meshfilename = v[4].decode("utf-8")
+        urdfCollision.geom_meshscale = v[3]
+      if (v[2] == p.GEOM_CYLINDER):
+        urdfCollision.geom_length = v[3][0]
+        urdfCollision.geom_radius = v[3][1]
+      if (v[2] == p.GEOM_CAPSULE):
+        urdfCollision.geom_length = v[3][0]
+        urdfCollision.geom_radius = v[3][1]
+      pos,orn = p.multiplyTransforms(dyn[3],dyn[4],\
+       v[5], v[6])
+      urdfCollision.origin_xyz = pos
+      urdfCollision.origin_rpy = p.getEulerFromQuaternion(orn)
+      urdfLink.urdf_collision_shapes.append(urdfCollision)
+
+  def initializeFromBulletBody(self, bodyUid, physicsClientId):
+    self.initialize()
+
+    #always create a base link
+    baseLink = UrdfLink()
+    baseLinkIndex = -1
+    self.convertLinkFromMultiBody(bodyUid, baseLinkIndex, baseLink, physicsClientId)
+    baseLink.link_name = p.getBodyInfo(bodyUid, physicsClientId=physicsClientId)[0].decode("utf-8")
+    self.linkNameToIndex[baseLink.link_name] = len(self.urdfLinks)
+    self.urdfLinks.append(baseLink)
+
+    #optionally create child links and joints
+    for j in range(p.getNumJoints(bodyUid, physicsClientId=physicsClientId)):
+      jointInfo = p.getJointInfo(bodyUid, j, physicsClientId=physicsClientId)
+      urdfLink = UrdfLink()
+      self.convertLinkFromMultiBody(bodyUid, j, urdfLink, physicsClientId)
+      urdfLink.link_name = jointInfo[12].decode("utf-8")
+      self.linkNameToIndex[urdfLink.link_name] = len(self.urdfLinks)
+      self.urdfLinks.append(urdfLink)
+
+      urdfJoint = UrdfJoint()
+      urdfJoint.link = urdfLink
+      urdfJoint.joint_name = jointInfo[1].decode("utf-8")
+      urdfJoint.joint_type = jointInfo[2]
+      urdfJoint.joint_axis_xyz = jointInfo[13]
+      orgParentIndex = jointInfo[16]
+      if (orgParentIndex < 0):
+        urdfJoint.parent_name = baseLink.link_name
+      else:
+        parentJointInfo = p.getJointInfo(bodyUid, orgParentIndex, physicsClientId=physicsClientId)
+        urdfJoint.parent_name = parentJointInfo[12].decode("utf-8")
+      urdfJoint.child_name = urdfLink.link_name
+
+      #todo, compensate for inertia/link frame offset
+
+      dynChild = p.getDynamicsInfo(bodyUid, j, physicsClientId=physicsClientId)
+      childInertiaPos = dynChild[3]
+      childInertiaOrn = dynChild[4]
+      parentCom2JointPos = jointInfo[14]
+      parentCom2JointOrn = jointInfo[15]
+      tmpPos, tmpOrn = p.multiplyTransforms(childInertiaPos, childInertiaOrn, parentCom2JointPos,
+                                            parentCom2JointOrn)
+      tmpPosInv, tmpOrnInv = p.invertTransform(tmpPos, tmpOrn)
+      dynParent = p.getDynamicsInfo(bodyUid, orgParentIndex, physicsClientId=physicsClientId)
+      parentInertiaPos = dynParent[3]
+      parentInertiaOrn = dynParent[4]
+
+      pos, orn = p.multiplyTransforms(parentInertiaPos, parentInertiaOrn, tmpPosInv, tmpOrnInv)
+      pos, orn_unused = p.multiplyTransforms(parentInertiaPos, parentInertiaOrn,
+                                             parentCom2JointPos, [0, 0, 0, 1])
+
+      urdfJoint.joint_origin_xyz = pos
+      urdfJoint.joint_origin_rpy = p.getEulerFromQuaternion(orn)
+
+      self.urdfJoints.append(urdfJoint)
+
+  def writeInertial(self, file, urdfInertial, precision=5):
+    file.write("\t\t<inertial>\n")
+    str = '\t\t\t<origin rpy=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\" xyz=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(\
+    urdfInertial.origin_rpy[0],urdfInertial.origin_rpy[1],urdfInertial.origin_rpy[2],\
+    urdfInertial.origin_xyz[0],urdfInertial.origin_xyz[1],urdfInertial.origin_xyz[2], prec=precision)
+    file.write(str)
+    str = '\t\t\t<mass value=\"{:.{prec}f}\"/>\n'.format(urdfInertial.mass, prec=precision)
+    file.write(str)
+    str = '\t\t\t<inertia ixx=\"{:.{prec}f}\" ixy=\"0\" ixz=\"0\" iyy=\"{:.{prec}f}\" iyz=\"0\" izz=\"{:.{prec}f}\"/>\n'.format(\
+    urdfInertial.inertia_xxyyzz[0],\
+    urdfInertial.inertia_xxyyzz[1],\
+    urdfInertial.inertia_xxyyzz[2],prec=precision)
+    file.write(str)
+    file.write("\t\t</inertial>\n")
+
+  def writeVisualShape(self, file, urdfVisual, precision=5):
+    #we don't support loading capsule types from visuals, so auto-convert from collision shape
+    if urdfVisual.geom_type == p.GEOM_CAPSULE:
+      return
+
+    file.write("\t\t<visual>\n")
+    str = '\t\t\t<origin rpy="{:.{prec}f} {:.{prec}f} {:.{prec}f}" xyz="{:.{prec}f} {:.{prec}f} {:.{prec}f}"/>\n'.format(\
+     urdfVisual.origin_rpy[0],urdfVisual.origin_rpy[1],urdfVisual.origin_rpy[2],
+     urdfVisual.origin_xyz[0],urdfVisual.origin_xyz[1],urdfVisual.origin_xyz[2], prec=precision)
+    file.write(str)
+    file.write("\t\t\t<geometry>\n")
+    if urdfVisual.geom_type == p.GEOM_BOX:
+      str = '\t\t\t\t<box size=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(urdfVisual.geom_extents[0],\
+       urdfVisual.geom_extents[1],urdfVisual.geom_extents[2], prec=precision)
+      file.write(str)
+    if urdfVisual.geom_type == p.GEOM_SPHERE:
+      str = '\t\t\t\t<sphere radius=\"{:.{prec}f}\"/>\n'.format(urdfVisual.geom_radius,\
+       prec=precision)
+      file.write(str)
+    if urdfVisual.geom_type == p.GEOM_MESH:
+
+      str = '\t\t\t\t<mesh filename=\"{}\" scale=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(
+      urdfVisual.geom_meshfilename,urdfVisual.geom_meshscale[0],urdfVisual.geom_meshscale[1],urdfVisual.geom_meshscale[2],\
+       prec=precision)
+      file.write(str)
+    if urdfVisual.geom_type == p.GEOM_CYLINDER:
+      str = '\t\t\t\t<cylinder length=\"{:.{prec}f}\" radius=\"{:.{prec}f}\"/>\n'.format(\
+       urdfVisual.geom_length, urdfVisual.geom_radius, prec=precision)
+      file.write(str)
+    if urdfVisual.geom_type == p.GEOM_CAPSULE:
+      str = '\t\t\t\t<capsule length=\"{:.{prec}f}\" radius=\"{:.{prec}f}\"/>\n'.format(\
+       urdfVisual.geom_length, urdfVisual.geom_radius, prec=precision)
+      file.write(str)
+
+    file.write("\t\t\t</geometry>\n")
+    str = '\t\t\t<material name=\"{}\">\n'.format(urdfVisual.material_name)
+    file.write(str)
+    str = '\t\t\t\t<color rgba="{:.{prec}f} {:.{prec}f} {:.{prec}f} {:.{prec}f}" />\n'.format(urdfVisual.material_rgba[0],\
+     urdfVisual.material_rgba[1],urdfVisual.material_rgba[2],urdfVisual.material_rgba[3],prec=precision)
+    file.write(str)
+    file.write("\t\t\t</material>\n")
+    file.write("\t\t</visual>\n")
+
+  def writeCollisionShape(self, file, urdfCollision, precision=5):
+    file.write("\t\t<collision>\n")
+    str = '\t\t\t<origin rpy="{:.{prec}f} {:.{prec}f} {:.{prec}f}" xyz="{:.{prec}f} {:.{prec}f} {:.{prec}f}"/>\n'.format(\
+     urdfCollision.origin_rpy[0],urdfCollision.origin_rpy[1],urdfCollision.origin_rpy[2],
+     urdfCollision.origin_xyz[0],urdfCollision.origin_xyz[1],urdfCollision.origin_xyz[2], prec=precision)
+    file.write(str)
+    file.write("\t\t\t<geometry>\n")
+    if urdfCollision.geom_type == p.GEOM_BOX:
+      str = '\t\t\t\t<box size=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(urdfCollision.geom_extents[0],\
+       urdfCollision.geom_extents[1],urdfCollision.geom_extents[2], prec=precision)
+      file.write(str)
+    if urdfCollision.geom_type == p.GEOM_SPHERE:
+      str = '\t\t\t\t<sphere radius=\"{:.{prec}f}\"/>\n'.format(urdfCollision.geom_radius,\
+       prec=precision)
+      file.write(str)
+    if urdfCollision.geom_type == p.GEOM_MESH:
+      str = '\t\t\t\t<mesh filename=\"{}\" scale=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(urdfCollision.geom_meshfilename,\
+       urdfCollision.geom_meshscale[0],urdfCollision.geom_meshscale[1],urdfCollision.geom_meshscale[2],prec=precision)
+      file.write(str)
+    if urdfCollision.geom_type == p.GEOM_CYLINDER:
+      str = '\t\t\t\t<cylinder length=\"{:.{prec}f}\" radius=\"{:.{prec}f}\"/>\n'.format(\
+       urdfCollision.geom_length, urdfCollision.geom_radius, prec=precision)
+      file.write(str)
+    if urdfCollision.geom_type == p.GEOM_CAPSULE:
+      str = '\t\t\t\t<capsule length=\"{:.{prec}f}\" radius=\"{:.{prec}f}\"/>\n'.format(\
+       urdfCollision.geom_length, urdfCollision.geom_radius, prec=precision)
+      file.write(str)
+    file.write("\t\t\t</geometry>\n")
+    file.write("\t\t</collision>\n")
+
+  def writeLink(self, file, urdfLink, saveVisuals):
+    file.write("\t<link name=\"")
+    file.write(urdfLink.link_name)
+    file.write("\">\n")
+
+    self.writeInertial(file, urdfLink.urdf_inertial)
+    hasCapsules = False
+    for v in urdfLink.urdf_visual_shapes:
+      if (v.geom_type == p.GEOM_CAPSULE):
+        hasCapsules = True
+    if (saveVisuals and not hasCapsules):
+      for v in urdfLink.urdf_visual_shapes:
+        self.writeVisualShape(file, v)
+    for c in urdfLink.urdf_collision_shapes:
+      self.writeCollisionShape(file, c)
+    file.write("\t</link>\n")
+
+  def writeJoint(self, file, urdfJoint, precision=5):
+    jointTypeStr = "invalid"
+    if urdfJoint.joint_type == p.JOINT_REVOLUTE:
+      if urdfJoint.joint_upper_limit < urdfJoint.joint_lower_limit:
+        jointTypeStr = "continuous"
+      else:
+        jointTypeStr = "revolute"
+    if urdfJoint.joint_type == p.JOINT_FIXED:
+      jointTypeStr = "fixed"
+    if urdfJoint.joint_type == p.JOINT_PRISMATIC:
+      jointTypeStr = "prismatic"
+    str = '\t<joint name=\"{}\" type=\"{}\">\n'.format(urdfJoint.joint_name, jointTypeStr)
+    file.write(str)
+    str = '\t\t<parent link=\"{}\"/>\n'.format(urdfJoint.parent_name)
+    file.write(str)
+    str = '\t\t<child link=\"{}\"/>\n'.format(urdfJoint.child_name)
+    file.write(str)
+
+    if urdfJoint.joint_type == p.JOINT_PRISMATIC:
+      #todo: handle limits
+      lowerLimit = -0.5
+      upperLimit = 0.5
+      str = '<limit effort="1000.0" lower="{:.{prec}f}" upper="{:.{prec}f}" velocity="0.5"/>'.format(
+          lowerLimit, upperLimit, prec=precision)
+      file.write(str)
+
+    file.write("\t\t<dynamics damping=\"1.0\" friction=\"0.0001\"/>\n")
+    str = '\t\t<origin xyz=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(urdfJoint.joint_origin_xyz[0],\
+     urdfJoint.joint_origin_xyz[1],urdfJoint.joint_origin_xyz[2], prec=precision)
+    str = '\t\t<origin rpy=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\" xyz=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(urdfJoint.joint_origin_rpy[0],\
+     urdfJoint.joint_origin_rpy[1],urdfJoint.joint_origin_rpy[2],\
+     urdfJoint.joint_origin_xyz[0],\
+     urdfJoint.joint_origin_xyz[1],urdfJoint.joint_origin_xyz[2], prec=precision)
+
+    file.write(str)
+    str = '\t\t<axis xyz=\"{:.{prec}f} {:.{prec}f} {:.{prec}f}\"/>\n'.format(urdfJoint.joint_axis_xyz[0],\
+     urdfJoint.joint_axis_xyz[1],urdfJoint.joint_axis_xyz[2], prec=precision)
+    file.write(str)
+    file.write("\t</joint>\n")
+
+  def saveUrdf(self, fileName, saveVisuals=True):
+    file = open(fileName, "w")
+    file.write("<?xml version=\"0.0\" ?>\n")
+    file.write("<robot name=\"")
+    file.write(self.robotName)
+    file.write("\">\n")
+
+    for link in self.urdfLinks:
+      self.writeLink(file, link, saveVisuals)
+
+    for joint in self.urdfJoints:
+      self.writeJoint(file, joint)
+
+    file.write("</robot>\n")
+    file.close()
+
+
+#def addLink(...)
+
+  def joinUrdf(self,
+               childEditor,
+               parentLinkIndex=0,
+               jointPivotXYZInParent=[0, 0, 0],
+               jointPivotRPYInParent=[0, 0, 0],
+               jointPivotXYZInChild=[0, 0, 0],
+               jointPivotRPYInChild=[0, 0, 0],
+               parentPhysicsClientId=0,
+               childPhysicsClientId=0):
+
+    childLinkIndex = len(self.urdfLinks)
+    insertJointIndex = len(self.urdfJoints)
+
+    #combine all links, and add a joint
+
+    for link in childEditor.urdfLinks:
+      self.linkNameToIndex[link.link_name] = len(self.urdfLinks)
+      self.urdfLinks.append(link)
+    for joint in childEditor.urdfJoints:
+      self.urdfJoints.append(joint)
+    #add a new joint between a particular
+
+    jointPivotQuatInChild = p.getQuaternionFromEuler(jointPivotRPYInChild)
+    invJointPivotXYZInChild, invJointPivotQuatInChild = p.invertTransform(
+        jointPivotXYZInChild, jointPivotQuatInChild)
+
+    #apply this invJointPivot***InChild to all inertial, visual and collision element in the child link
+    #inertial
+    pos, orn = p.multiplyTransforms(self.urdfLinks[childLinkIndex].urdf_inertial.origin_xyz,
+                                    p.getQuaternionFromEuler(
+                                        self.urdfLinks[childLinkIndex].urdf_inertial.origin_rpy),
+                                    invJointPivotXYZInChild,
+                                    invJointPivotQuatInChild,
+                                    physicsClientId=parentPhysicsClientId)
+    self.urdfLinks[childLinkIndex].urdf_inertial.origin_xyz = pos
+    self.urdfLinks[childLinkIndex].urdf_inertial.origin_rpy = p.getEulerFromQuaternion(orn)
+    #all visual
+    for v in self.urdfLinks[childLinkIndex].urdf_visual_shapes:
+      pos, orn = p.multiplyTransforms(v.origin_xyz, p.getQuaternionFromEuler(v.origin_rpy),
+                                      invJointPivotXYZInChild, invJointPivotQuatInChild)
+      v.origin_xyz = pos
+      v.origin_rpy = p.getEulerFromQuaternion(orn)
+    #all collision
+    for c in self.urdfLinks[childLinkIndex].urdf_collision_shapes:
+      pos, orn = p.multiplyTransforms(c.origin_xyz, p.getQuaternionFromEuler(c.origin_rpy),
+                                      invJointPivotXYZInChild, invJointPivotQuatInChild)
+      c.origin_xyz = pos
+      c.origin_rpy = p.getEulerFromQuaternion(orn)
+
+    childLink = self.urdfLinks[childLinkIndex]
+    parentLink = self.urdfLinks[parentLinkIndex]
+
+    joint = UrdfJoint()
+    joint.link = childLink
+    joint.joint_name = "joint_dummy1"
+    joint.joint_type = p.JOINT_REVOLUTE
+    joint.joint_lower_limit = 0
+    joint.joint_upper_limit = -1
+    joint.parent_name = parentLink.link_name
+    joint.child_name = childLink.link_name
+    joint.joint_origin_xyz = jointPivotXYZInParent
+    joint.joint_origin_rpy = jointPivotRPYInParent
+    joint.joint_axis_xyz = [0, 0, 1]
+
+    #the following commented line would crash PyBullet, it messes up the joint indexing/ordering
+    #self.urdfJoints.append(joint)
+
+    #so make sure to insert the joint in the right place, to links/joints match
+    self.urdfJoints.insert(insertJointIndex, joint)
+    return joint
+
+  def createMultiBody(self,
+                      basePosition=[0, 0, 0],
+                      baseOrientation=[0, 0, 0, 1],
+                      physicsClientId=0):
+    #assume link[0] is base
+    if (len(self.urdfLinks) == 0):
+      return -1
+
+    #for i in range (len(self.urdfLinks)):
+    #	print("link", i, "=",self.urdfLinks[i].link_name)
+
+    base = self.urdfLinks[0]
+
+    #v.tmp_collision_shape_ids=[]
+    baseMass = base.urdf_inertial.mass
+    baseCollisionShapeIndex = -1
+    baseShapeTypeArray = []
+    baseRadiusArray = []
+    baseHalfExtentsArray = []
+    lengthsArray = []
+    fileNameArray = []
+    meshScaleArray = []
+    basePositionsArray = []
+    baseOrientationsArray = []
+
+    for v in base.urdf_collision_shapes:
+      shapeType = v.geom_type
+      baseShapeTypeArray.append(shapeType)
+      baseHalfExtentsArray.append(
+          [0.5 * v.geom_extents[0], 0.5 * v.geom_extents[1], 0.5 * v.geom_extents[2]])
+      baseRadiusArray.append(v.geom_radius)
+      lengthsArray.append(v.geom_length)
+      fileNameArray.append(v.geom_meshfilename)
+      meshScaleArray.append(v.geom_meshscale)
+      basePositionsArray.append(v.origin_xyz)
+      orn = p.getQuaternionFromEuler(v.origin_rpy)
+      baseOrientationsArray.append(orn)
+
+    if (len(baseShapeTypeArray)):
+      #print("fileNameArray=",fileNameArray)
+      baseCollisionShapeIndex = p.createCollisionShapeArray(
+          shapeTypes=baseShapeTypeArray,
+          radii=baseRadiusArray,
+          halfExtents=baseHalfExtentsArray,
+          lengths=lengthsArray,
+          fileNames=fileNameArray,
+          meshScales=meshScaleArray,
+          collisionFramePositions=basePositionsArray,
+          collisionFrameOrientations=baseOrientationsArray,
+          physicsClientId=physicsClientId)
+
+    urdfVisuals = base.urdf_visual_shapes
+
+    shapeTypes = [v.geom_type for v in urdfVisuals]
+    halfExtents = [[ext * 0.5 for ext in v.geom_extents] for v in urdfVisuals]
+    radii = [v.geom_radius for v in urdfVisuals]
+    lengths = [v.geom_length for v in urdfVisuals]
+    fileNames = [v.geom_meshfilename for v in urdfVisuals]
+    meshScales = [v.geom_meshscale for v in urdfVisuals]
+    rgbaColors = [v.material_rgba for v in urdfVisuals]
+    visualFramePositions = [v.origin_xyz for v in urdfVisuals]
+    visualFrameOrientations = [p.getQuaternionFromEuler(v.origin_rpy) for v in urdfVisuals]
+    baseVisualShapeIndex = -1
+
+    if (len(shapeTypes)):
+      #print("len(shapeTypes)=",len(shapeTypes))
+      #print("len(halfExtents)=",len(halfExtents))
+      #print("len(radii)=",len(radii))
+      #print("len(lengths)=",len(lengths))
+      #print("len(fileNames)=",len(fileNames))
+      #print("len(meshScales)=",len(meshScales))
+      #print("len(rgbaColors)=",len(rgbaColors))
+      #print("len(visualFramePositions)=",len(visualFramePositions))
+      #print("len(visualFrameOrientations)=",len(visualFrameOrientations))
+
+      baseVisualShapeIndex = p.createVisualShapeArray(
+          shapeTypes=shapeTypes,
+          halfExtents=halfExtents,
+          radii=radii,
+          lengths=lengths,
+          fileNames=fileNames,
+          meshScales=meshScales,
+          rgbaColors=rgbaColors,
+          visualFramePositions=visualFramePositions,
+          visualFrameOrientations=visualFrameOrientations,
+          physicsClientId=physicsClientId)
+
+    linkMasses = []
+    linkCollisionShapeIndices = []
+    linkVisualShapeIndices = []
+    linkPositions = []
+    linkOrientations = []
+
+    linkInertialFramePositions = []
+    linkInertialFrameOrientations = []
+    linkParentIndices = []
+    linkJointTypes = []
+    linkJointAxis = []
+
+    for joint in self.urdfJoints:
+      link = joint.link
+      linkMass = link.urdf_inertial.mass
+      linkCollisionShapeIndex = -1
+      linkVisualShapeIndex = -1
+      linkPosition = [0, 0, 0]
+      linkOrientation = [0, 0, 0]
+      linkInertialFramePosition = [0, 0, 0]
+      linkInertialFrameOrientation = [0, 0, 0]
+      linkParentIndex = self.linkNameToIndex[joint.parent_name]
+      linkJointType = joint.joint_type
+      linkJointAx = joint.joint_axis_xyz
+      linkShapeTypeArray = []
+      linkRadiusArray = []
+      linkHalfExtentsArray = []
+      lengthsArray = []
+      fileNameArray = []
+      linkMeshScaleArray = []
+      linkPositionsArray = []
+      linkOrientationsArray = []
+
+      for v in link.urdf_collision_shapes:
+        shapeType = v.geom_type
+        linkShapeTypeArray.append(shapeType)
+        linkHalfExtentsArray.append(
+            [0.5 * v.geom_extents[0], 0.5 * v.geom_extents[1], 0.5 * v.geom_extents[2]])
+        linkRadiusArray.append(v.geom_radius)
+        lengthsArray.append(v.geom_length)
+        fileNameArray.append(v.geom_meshfilename)
+        linkMeshScaleArray.append(v.geom_meshscale)
+        linkPositionsArray.append(v.origin_xyz)
+        linkOrientationsArray.append(p.getQuaternionFromEuler(v.origin_rpy))
+
+      if (len(linkShapeTypeArray)):
+        linkCollisionShapeIndex = p.createCollisionShapeArray(
+            shapeTypes=linkShapeTypeArray,
+            radii=linkRadiusArray,
+            halfExtents=linkHalfExtentsArray,
+            lengths=lengthsArray,
+            fileNames=fileNameArray,
+            meshScales=linkMeshScaleArray,
+            collisionFramePositions=linkPositionsArray,
+            collisionFrameOrientations=linkOrientationsArray,
+            physicsClientId=physicsClientId)
+
+      urdfVisuals = link.urdf_visual_shapes
+      linkVisualShapeIndex = -1
+      shapeTypes = [v.geom_type for v in urdfVisuals]
+      halfExtents = [[ext * 0.5 for ext in v.geom_extents] for v in urdfVisuals]
+      radii = [v.geom_radius for v in urdfVisuals]
+      lengths = [v.geom_length for v in urdfVisuals]
+      fileNames = [v.geom_meshfilename for v in urdfVisuals]
+      meshScales = [v.geom_meshscale for v in urdfVisuals]
+      rgbaColors = [v.material_rgba for v in urdfVisuals]
+      visualFramePositions = [v.origin_xyz for v in urdfVisuals]
+      visualFrameOrientations = [p.getQuaternionFromEuler(v.origin_rpy) for v in urdfVisuals]
+
+      if (len(shapeTypes)):
+        print("fileNames=", fileNames)
+
+        linkVisualShapeIndex = p.createVisualShapeArray(
+            shapeTypes=shapeTypes,
+            halfExtents=halfExtents,
+            radii=radii,
+            lengths=lengths,
+            fileNames=fileNames,
+            meshScales=meshScales,
+            rgbaColors=rgbaColors,
+            visualFramePositions=visualFramePositions,
+            visualFrameOrientations=visualFrameOrientations,
+            physicsClientId=physicsClientId)
+
+      linkMasses.append(linkMass)
+      linkCollisionShapeIndices.append(linkCollisionShapeIndex)
+      linkVisualShapeIndices.append(linkVisualShapeIndex)
+      linkPositions.append(joint.joint_origin_xyz)
+      linkOrientations.append(p.getQuaternionFromEuler(joint.joint_origin_rpy))
+      linkInertialFramePositions.append(link.urdf_inertial.origin_xyz)
+      linkInertialFrameOrientations.append(p.getQuaternionFromEuler(link.urdf_inertial.origin_rpy))
+      linkParentIndices.append(linkParentIndex)
+      linkJointTypes.append(joint.joint_type)
+      linkJointAxis.append(joint.joint_axis_xyz)
+    obUid = p.createMultiBody(baseMass,\
+       baseCollisionShapeIndex=baseCollisionShapeIndex,
+                                          baseVisualShapeIndex=baseVisualShapeIndex,
+       basePosition=basePosition,
+       baseOrientation=baseOrientation,
+       baseInertialFramePosition=base.urdf_inertial.origin_xyz,
+       baseInertialFrameOrientation=p.getQuaternionFromEuler(base.urdf_inertial.origin_rpy),
+       linkMasses=linkMasses,
+       linkCollisionShapeIndices=linkCollisionShapeIndices,
+       linkVisualShapeIndices=linkVisualShapeIndices,
+       linkPositions=linkPositions,
+       linkOrientations=linkOrientations,
+       linkInertialFramePositions=linkInertialFramePositions,
+       linkInertialFrameOrientations=linkInertialFrameOrientations,
+       linkParentIndices=linkParentIndices,
+       linkJointTypes=linkJointTypes,
+       linkJointAxis=linkJointAxis,
+       physicsClientId=physicsClientId)
+    return obUid
+
+  def __del__(self):
+    pass
diff --git a/examples/pybullet/gym/pybullet_utils/util.py b/examples/pybullet/gym/pybullet_utils/util.py
index c11ccb811..5a014c8ed 100644
--- a/examples/pybullet/gym/pybullet_utils/util.py
+++ b/examples/pybullet/gym/pybullet_utils/util.py
@@ -1,13 +1,14 @@
 import random
 import numpy as np
 
+
 def set_global_seeds(seed):
-    try:
-        import tensorflow as tf
-    except ImportError:
-        pass
-    else:
-        tf.set_random_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    return
\ No newline at end of file
+  try:
+    import tensorflow as tf
+  except ImportError:
+    pass
+  else:
+    tf.set_random_seed(seed)
+  np.random.seed(seed)
+  random.seed(seed)
+  return
diff --git a/examples/pybullet/numpy/humanoid_running.py b/examples/pybullet/numpy/humanoid_running.py
index 6db8e0a57..5f6a1f894 100644
--- a/examples/pybullet/numpy/humanoid_running.py
+++ b/examples/pybullet/numpy/humanoid_running.py
@@ -1,7 +1,7 @@
 import os, inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(os.path.dirname(currentdir))
-os.sys.path.insert(0,parentdir)
+os.sys.path.insert(0, parentdir)
 
 import sys
 import numpy as np
@@ -13,57 +13,54 @@ gui = True
 
 cid = p.connect(p.SHARED_MEMORY)
 #DIRECT is much faster, but GUI shows the running gait
-if (cid<0):
-	if (gui):
-		cid = p.connect(p.GUI)
-	else:
-		cid = p.connect(p.DIRECT)
+if (cid < 0):
+  if (gui):
+    cid = p.connect(p.GUI)
+  else:
+    cid = p.connect(p.DIRECT)
 
 #p.setGravity(1,2,-9.8)
 #p.setDefaultContactERP (0.4)
-p.setGravity(0,0,-9.8)
+p.setGravity(0, 0, -9.8)
 #numSubSteps=4 and fixedTimeStep=1.0/60. is an effective internal fixed step of 1./240
 #recommended to not go below 50 solver iterations
-p.setPhysicsEngineParameter(fixedTimeStep=1.0/60., numSolverIterations=550, numSubSteps=8)
+p.setPhysicsEngineParameter(fixedTimeStep=1.0 / 60., numSolverIterations=550, numSubSteps=8)
 #this mp4 recording requires ffmpeg installed
 #mp4log = p.startStateLogging(p.STATE_LOGGING_VIDEO_MP4,"humanoid.mp4")
 
 #p.loadSDF("stadium.sdf")
 p.loadURDF("plane_implicit.urdf")
 
-objs = p.loadMJCF("mjcf/humanoid_symmetric_no_ground.xml",flags = p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS)
-human  = objs[0]
+objs = p.loadMJCF("mjcf/humanoid_symmetric_no_ground.xml",
+                  flags=p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS)
+human = objs[0]
 for j in range(p.getNumJoints(human)):
-	jointInfo=p.getJointInfo(human,j)
-	print("joint(",j,"qIndex=",jointInfo[3],"uIndex=",jointInfo[4],")=",jointInfo)
+  jointInfo = p.getJointInfo(human, j)
+  print("joint(", j, "qIndex=", jointInfo[3], "uIndex=", jointInfo[4], ")=", jointInfo)
 ordered_joints = []
 ordered_joint_indices = []
 
 parser = argparse.ArgumentParser()
 parser.add_argument('--profile')
 
-
 jdict = {}
-for j in range( p.getNumJoints(human) ):
-	info = p.getJointInfo(human, j)
-	link_name = info[12].decode("ascii")
-	if link_name=="left_foot": left_foot = j
-	if link_name=="right_foot": right_foot = j
-	ordered_joint_indices.append(j)
-		
-	if info[2] != p.JOINT_REVOLUTE: continue
-	jname = info[1].decode("ascii")
-	jdict[jname] = j
-	lower, upper = (info[8], info[9])
-	ordered_joints.append( (j, lower, upper) )
-	
-	
-	p.setJointMotorControl2(human, j, controlMode=p.VELOCITY_CONTROL, force=0)
-
-	
-
-motor_names  = ["abdomen_z", "abdomen_y", "abdomen_x"]
-motor_power  = [100, 100, 100]
+for j in range(p.getNumJoints(human)):
+  info = p.getJointInfo(human, j)
+  link_name = info[12].decode("ascii")
+  if link_name == "left_foot": left_foot = j
+  if link_name == "right_foot": right_foot = j
+  ordered_joint_indices.append(j)
+
+  if info[2] != p.JOINT_REVOLUTE: continue
+  jname = info[1].decode("ascii")
+  jdict[jname] = j
+  lower, upper = (info[8], info[9])
+  ordered_joints.append((j, lower, upper))
+
+  p.setJointMotorControl2(human, j, controlMode=p.VELOCITY_CONTROL, force=0)
+
+motor_names = ["abdomen_z", "abdomen_y", "abdomen_x"]
+motor_power = [100, 100, 100]
 motor_names += ["right_hip_x", "right_hip_z", "right_hip_y", "right_knee"]
 motor_power += [100, 100, 300, 200]
 motor_names += ["left_hip_x", "left_hip_z", "left_hip_y", "left_knee"]
@@ -74,581 +71,7936 @@ motor_names += ["left_shoulder1", "left_shoulder2", "left_elbow"]
 motor_power += [75, 75, 75]
 motors = [jdict[n] for n in motor_names]
 
+
 class Dummy:
-	pass
+  pass
+
+
 dummy = Dummy()
 dummy.initial_z = None
 
+
 def current_relative_position(jointStates, human, j, lower, upper):
-	#print("j")
-	#print(j)
-	#print (len(jointStates))
-	#print(j)
-	temp  = jointStates[j]
-	pos = temp[0]
-	vel = temp[1]
-	#print("pos")
-	#print(pos)
-	#print("vel")
-	#print(vel)
-	pos_mid = 0.5 * (lower + upper);
-	return (
-		2 * (pos - pos_mid) / (upper - lower),
-		0.1 * vel
-		)
+  #print("j")
+  #print(j)
+  #print (len(jointStates))
+  #print(j)
+  temp = jointStates[j]
+  pos = temp[0]
+  vel = temp[1]
+  #print("pos")
+  #print(pos)
+  #print("vel")
+  #print(vel)
+  pos_mid = 0.5 * (lower + upper)
+  return (2 * (pos - pos_mid) / (upper - lower), 0.1 * vel)
+
 
 def collect_observations(human):
-	#print("ordered_joint_indices")
-	#print(ordered_joint_indices)
-	
-	jointStates = p.getJointStates(human,ordered_joint_indices)
-	j = np.array([current_relative_position(jointStates, human, *jtuple) for jtuple in ordered_joints]).flatten()
-	#print("j")
-	#print(j)
-	body_xyz, (qx, qy, qz, qw) = p.getBasePositionAndOrientation(human)
-	#print("body_xyz")
-	#print(body_xyz, qx,qy,qz,qw)
-	z = body_xyz[2]
-	dummy.distance = body_xyz[0]
-	if dummy.initial_z==None:
-		dummy.initial_z = z
-	(vx, vy, vz), _ = p.getBaseVelocity(human)
-	more = np.array([z-dummy.initial_z, 0.1*vx, 0.1*vy, 0.1*vz, qx, qy, qz, qw])
-	rcont = p.getContactPoints(human, -1, right_foot, -1)
-	#print("rcont")
-	#print(rcont)
-	lcont = p.getContactPoints(human, -1, left_foot, -1)
-	#print("lcont")
-	#print(lcont)
-	feet_contact = np.array([len(rcont)>0, len(lcont)>0])
-	return np.clip( np.concatenate([more] + [j] + [feet_contact]), -5, +5)
+  #print("ordered_joint_indices")
+  #print(ordered_joint_indices)
 
+  jointStates = p.getJointStates(human, ordered_joint_indices)
+  j = np.array([
+      current_relative_position(jointStates, human, *jtuple) for jtuple in ordered_joints
+  ]).flatten()
+  #print("j")
+  #print(j)
+  body_xyz, (qx, qy, qz, qw) = p.getBasePositionAndOrientation(human)
+  #print("body_xyz")
+  #print(body_xyz, qx,qy,qz,qw)
+  z = body_xyz[2]
+  dummy.distance = body_xyz[0]
+  if dummy.initial_z == None:
+    dummy.initial_z = z
+  (vx, vy, vz), _ = p.getBaseVelocity(human)
+  more = np.array([z - dummy.initial_z, 0.1 * vx, 0.1 * vy, 0.1 * vz, qx, qy, qz, qw])
+  rcont = p.getContactPoints(human, -1, right_foot, -1)
+  #print("rcont")
+  #print(rcont)
+  lcont = p.getContactPoints(human, -1, left_foot, -1)
+  #print("lcont")
+  #print(lcont)
+  feet_contact = np.array([len(rcont) > 0, len(lcont) > 0])
+  return np.clip(np.concatenate([more] + [j] + [feet_contact]), -5, +5)
 
 
 def relu(x):
-    return np.maximum(x, 0)
-    
+  return np.maximum(x, 0)
+
+
 class SmallReactivePolicy:
-    "Simple multi-layer perceptron policy, no internal state"
-    def __init__(self):#, observation_space, action_space):
-        #assert weights_dense1_w.shape == (observation_space.shape[0], 256)
-        #assert weights_dense2_w.shape == (256, 128)
-        #assert weights_final_w.shape  == (128, action_space.shape[0])
-        pass
-
-    def act(self, ob):
-        #ob[0] += -1.4 + 0.8
-        x = ob
-        x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
-        x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
-        x = np.dot(x, weights_final_w) + weights_final_b
-        return x
+  "Simple multi-layer perceptron policy, no internal state"
+
+  def __init__(self):  #, observation_space, action_space):
+    #assert weights_dense1_w.shape == (observation_space.shape[0], 256)
+    #assert weights_dense2_w.shape == (256, 128)
+    #assert weights_final_w.shape  == (128, action_space.shape[0])
+    pass
+
+  def act(self, ob):
+    #ob[0] += -1.4 + 0.8
+    x = ob
+    x = relu(np.dot(x, weights_dense1_w) + weights_dense1_b)
+    x = relu(np.dot(x, weights_dense2_w) + weights_dense2_b)
+    x = np.dot(x, weights_final_w) + weights_final_b
+    return x
 
 
 def demo_run():
-	pi = SmallReactivePolicy()
-	t1 = time.time()
-	timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "humanoidTimings.json")
-
-	frame = 0
-	while 1:
-		obs = collect_observations(human)
-	
-		actions = pi.act(obs)
-	
-		#print(" ".join(["%+0.2f"%x for x in obs]))
-		#print("Motors")
-		#print(motors)
-
-		#for m in range(len(motors)):
-			#print("motor_power")
-			#print(motor_power[m])
-			#print("actions[m]")
-			#print(actions[m])
-		#p.setJointMotorControl2(human, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082)
-		#p.setJointMotorControl2(human1, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082)
-			
-		forces = [0.] * len(motors)
-		batch = True
-		for m in range(len(motors)):
-			forces[m] = motor_power[m]*actions[m]*0.082
-			if (not batch):
-				p.setJointMotorControl2(human, motors[m],controlMode=p.TORQUE_CONTROL, force=forces[m])
-		if (batch):
-			p.setJointMotorControlArray(human, motors,controlMode=p.TORQUE_CONTROL, forces=forces)
-
-		p.stepSimulation()
-		
-		
-
-		humanPos, humanOrn = p.getBasePositionAndOrientation(human)
-		
-		if (gui):
-			time.sleep(1./60.)
-			print("frame=",frame)
-			camInfo = p.getDebugVisualizerCamera()
-			curTargetPos = camInfo[11]
-			distance=camInfo[10]
-			yaw = camInfo[8]
-			pitch=camInfo[9]
-			targetPos = [0.95*curTargetPos[0]+0.05*humanPos[0],0.95*curTargetPos[1]+0.05*humanPos[1],curTargetPos[2]]
-			p.resetDebugVisualizerCamera(distance,yaw,pitch,targetPos);
-		frame += 1
-		
-		#if frame==1000: break
-	t2 = time.time()
-	print("############################### distance = %0.2f meters" % dummy.distance)
-	print("############################### FPS = ", 1000/ (t2-t1))
-	#print("Starting benchmark")
-	#logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS,"pybullet_humanoid_timings.json")
-	#p.stopStateLogging(logId)
-	print("ended benchmark")
-	print(frame)
-	p.stopStateLogging(timinglog)
-
-
-weights_dense1_w = np.array([
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-])
+  pi = SmallReactivePolicy()
+  t1 = time.time()
+  timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "humanoidTimings.json")
 
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-
-weights_dense2_w = np.array([
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+  frame = 0
+  while 1:
+    obs = collect_observations(human)
+
+    actions = pi.act(obs)
+
+    #print(" ".join(["%+0.2f"%x for x in obs]))
+    #print("Motors")
+    #print(motors)
+
+    #for m in range(len(motors)):
+    #print("motor_power")
+    #print(motor_power[m])
+    #print("actions[m]")
+    #print(actions[m])
+    #p.setJointMotorControl2(human, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082)
+    #p.setJointMotorControl2(human1, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082)
+
+    forces = [0.] * len(motors)
+    batch = True
+    for m in range(len(motors)):
+      forces[m] = motor_power[m] * actions[m] * 0.082
+      if (not batch):
+        p.setJointMotorControl2(human, motors[m], controlMode=p.TORQUE_CONTROL, force=forces[m])
+    if (batch):
+      p.setJointMotorControlArray(human, motors, controlMode=p.TORQUE_CONTROL, forces=forces)
+
+    p.stepSimulation()
+
+    humanPos, humanOrn = p.getBasePositionAndOrientation(human)
+
+    if (gui):
+      time.sleep(1. / 60.)
+      print("frame=", frame)
+      camInfo = p.getDebugVisualizerCamera()
+      curTargetPos = camInfo[11]
+      distance = camInfo[10]
+      yaw = camInfo[8]
+      pitch = camInfo[9]
+      targetPos = [
+          0.95 * curTargetPos[0] + 0.05 * humanPos[0], 0.95 * curTargetPos[1] + 0.05 * humanPos[1],
+          curTargetPos[2]
+      ]
+      p.resetDebugVisualizerCamera(distance, yaw, pitch, targetPos)
+    frame += 1
+
+    #if frame==1000: break
+  t2 = time.time()
+  print("############################### distance = %0.2f meters" % dummy.distance)
+  print("############################### FPS = ", 1000 / (t2 - t1))
+  #print("Starting benchmark")
+  #logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS,"pybullet_humanoid_timings.json")
+  #p.stopStateLogging(logId)
+  print("ended benchmark")
+  print(frame)
+  p.stopStateLogging(timinglog)
+
+
+# yapf: disable
+weights_dense1_w = np.array(
+    [[
+        -0.3857, -0.7450, +0.1473, +0.1996, +0.0570, +0.4522, +0.1172, +0.7558,
+        -0.2530, -0.7948, +0.3120, +0.6216, +0.1044, -0.4347, +0.0921, +0.0187,
+        +0.2030, +0.2042, -0.2928, +0.2717, +0.5117, +0.1115, -0.0278, -0.0675,
+        -0.2967, +0.3128, -0.1434, -0.0476, +0.0561, -0.1874, +0.1675, -0.1028,
+        +0.0712, +0.8653, -0.5565, -0.1207, +0.0982, -0.2875, -0.0755, -0.3057,
+        -0.0841, +0.5336, +0.2068, -0.0132, -0.2131, -0.5910, +0.1329, +0.2789,
+        +0.0036, +0.0048, +0.2655, +0.3585, +0.2861, -0.2288, -0.0646, +0.0569,
+        -0.4701, -0.0116, +0.4616, +0.0947, -0.1013, -0.0256, +0.2854, +0.3382,
+        +0.0822, +0.1160, -0.4913, +0.3991, +0.0121, -0.3392, +0.2515, +0.7188,
+        +0.3412, +0.1247, +0.1422, +0.0420, +0.1401, +0.5830, +0.0226, +0.6080,
+        +0.3019, -0.7696, -0.0580, +0.2104, +0.3786, +0.2968, -0.3726, +0.2792,
+        +0.3572, -0.4362, +0.5368, +0.0068, +0.4366, -0.4799, +0.2688, +0.1115,
+        +0.0251, +0.4107, -0.0358, +0.0198, +0.2563, +0.0315, -0.1143, +0.6191,
+        +0.1694, +0.4175, +0.1873, +0.0678, +0.1324, +0.3038, +0.0610, +0.2491,
+        -0.2730, +0.2933, +0.1704, +0.1746, +0.1444, -0.2578, +0.3743, +0.3837,
+        +0.3294, +0.0433, +0.5242, -0.3465, +0.1618, -0.5255, -0.1703, +0.0420,
+        +0.5505, +0.4046, +0.1596, +0.3973, -0.5158, -0.2922, +0.3183, -0.0244,
+        +0.3496, +0.4069, -0.1961, +0.2705, -0.1008, -0.4008, -0.1443, -0.2113,
+        +0.2064, -0.3466, +0.2565, +0.0279, +0.5785, -0.2918, +0.7262, +0.0285,
+        -0.1779, +0.2811, -0.2066, +0.5471, -0.7668, +0.1909, -0.5684, -0.0002,
+        +0.2291, -0.0631, +0.0823, +0.5520, -0.3267, +0.0143, +0.4295, +0.2594,
+        +0.3523, -0.1266, +0.4412, -0.2685, +0.0863, +0.1779, +0.1280, +0.5087,
+        +0.0809, +0.1118, -0.5754, -0.3300, -0.0032, +0.7060, -0.5172, -0.6553,
+        -0.0951, -0.0129, +0.1382, -0.0504, -0.2634, +0.2659, -0.1932, -0.3762,
+        +0.3072, +0.1612, +0.2942, -0.1438, -0.0850, +0.1877, +0.4726, +0.2751,
+        -0.0787, +0.3128, -0.1351, +0.2552, -0.4833, -0.0322, +0.3641, +0.3715,
+        -0.1938, +0.5040, -0.0598, +0.0341, +0.6252, -0.2977, +0.2694, +0.0525,
+        +0.6295, -0.1007, -0.0406, +0.1028, +0.5181, +0.2201, +0.2269, +0.3724,
+        -0.1985, -0.1614, +0.0093, +0.2851, +0.0191, +0.0620, +0.1989, +0.5905,
+        +0.5589, +0.1896, +0.3859, +0.1207, -0.0323, +0.3912, +0.2950, +0.3255,
+        +0.2765, -0.3384, +0.4286, +0.2692, +0.2889, +0.5955, +0.0918, -0.3228,
+        +0.3828, -0.0196, -0.0484, +0.3256, -0.1361, +0.5124, -0.1782, +0.2984
+    ],
+     [
+         -0.3305, -0.5427, +0.1033, -0.8093, -0.6948, +1.0265, +0.2688,
+         -0.4297, -0.3887, -0.0335, -0.4224, +0.0902, -0.0416, -0.3621,
+         +0.4824, -0.5138, -0.6160, +0.0404, -1.0360, +0.2734, +0.3415,
+         -0.7259, -0.3395, -0.2803, +0.6166, +0.3448, +0.3657, +0.0495,
+         +0.8924, +0.1673, +0.2726, +1.2509, -0.0482, +0.2982, -0.0817,
+         -0.6190, -0.7550, -0.6310, +0.1912, +0.2475, -0.4001, +0.2715,
+         -1.3571, -1.2735, +0.4491, +0.0965, +0.3893, -0.1449, +0.5919,
+         -0.7355, -0.2579, -0.6447, -0.8400, -1.0339, -0.7232, +0.2704,
+         -0.2094, -0.9082, +0.0719, -0.9922, +0.7732, -0.1924, -0.7696,
+         -0.3170, -0.3350, -0.3462, +0.3686, +0.7608, -0.3624, +0.0222,
+         +0.0152, +0.0056, -0.6305, +0.3649, +0.4987, -0.2182, -0.6930,
+         -0.0010, -0.2901, -0.1365, -0.4998, +0.2176, +0.4334, -0.2511,
+         +0.5084, -0.4405, +0.8559, -0.4784, +0.0126, -0.1106, +0.8800,
+         -0.7713, +0.2210, +0.1882, +0.0233, -0.0679, +0.0100, -0.6957,
+         +0.4229, +0.4257, +0.1985, +0.9668, -1.0394, +0.1009, -1.1962,
+         -0.6810, -0.8522, -0.9797, -0.4765, +0.6148, -0.1001, +0.1577,
+         -0.5535, -0.6540, -0.0717, -1.4224, -0.4940, +0.3266, -0.3978,
+         +0.2954, +0.2281, -0.5369, +0.2422, -0.2924, +0.2163, +0.7444,
+         +0.4338, -0.5244, +0.2214, +0.5298, -0.1770, -0.3549, +0.0541,
+         -0.1783, -0.1656, +0.9927, -0.9395, +0.6691, -0.2921, -1.1986,
+         -0.2086, +0.1251, +0.3633, -0.1052, -0.4214, +0.1419, +0.7749,
+         -0.0831, -0.8905, +0.5360, +0.0565, -2.0616, -1.4039, -0.7739,
+         +0.2181, +0.3649, +0.2573, -0.1620, -0.2045, -0.4774, +0.4206,
+         +0.4216, +0.0844, -0.3038, +0.2918, -1.4121, -1.0846, -0.5310,
+         +0.2190, +0.3286, -0.1058, -0.8251, -0.0417, -0.3994, -0.0012,
+         +0.4315, +0.1682, -1.1433, +0.2849, -0.5689, -0.3473, +0.6981,
+         -0.1699, -0.1203, -1.3225, -0.2436, -0.2331, +0.2366, +0.0377,
+         -0.4367, -0.0645, -1.3493, -0.3995, -0.4136, +0.4007, -0.4532,
+         +0.1397, -0.4860, +0.7116, -1.0520, -0.7300, +0.9354, +0.2490,
+         -0.0118, -0.6467, +1.0208, -0.0185, -0.1010, +0.0259, -0.4621,
+         -0.5824, -0.5307, -0.3225, +0.1271, -0.0694, -0.0902, +0.4922,
+         +0.2764, -0.1927, -0.2009, -0.8424, +0.0237, -0.0958, +0.3260,
+         -1.3182, -0.7006, +0.2267, -0.9277, +0.1478, +0.1729, +0.3861,
+         -0.6517, -0.3447, +0.4089, +0.1253, -0.3610, +0.7556, -0.5048,
+         -0.8110, +0.1085, -0.9362, -0.8233, +0.4134, -0.0085, -1.5413,
+         -0.4102, +0.7793, -1.2224, -0.7392, -0.3367, -0.0849, +0.0131,
+         +0.8377, +0.4575, +0.8130, -2.0800
+     ],
+     [
+         -0.5030, +0.4311, -1.1143, -0.2571, +0.0500, +0.6580, +0.1807,
+         +0.3972, +1.0069, -1.9235, -0.4153, +1.2305, -0.0986, +1.6550,
+         -2.0094, -0.5650, +0.1596, +2.2019, +0.1256, +1.7433, -1.8320,
+         +1.2385, +0.2217, +1.9671, -0.0651, -1.5623, +0.6551, -1.0514,
+         -0.2327, -0.7264, +1.1247, +0.7084, -3.1100, +2.5875, -0.6701,
+         +0.5488, +0.2897, +0.3663, +0.0783, -0.2380, -0.9809, -1.4906,
+         -0.0467, -0.3069, -1.3131, -1.1489, +0.4462, -0.0183, -0.7812,
+         -1.0135, -0.8092, -0.3989, -0.0717, -2.4097, -0.4086, -1.3601,
+         +0.4654, -1.2662, +0.9914, -1.6910, -0.4650, -1.1016, +0.0476,
+         +0.4011, +0.1599, -0.2772, +0.1293, -0.1329, +2.4128, +0.3290,
+         -0.0782, -1.8737, -2.1851, -0.2125, +1.0481, -2.0785, -0.7290,
+         -0.7160, -0.0253, -0.7524, +0.2129, -0.2267, -0.8073, +0.1843,
+         +0.7838, +1.0147, +0.1327, -0.0788, +0.3903, +2.0292, +2.5541,
+         +1.6453, -0.1778, -2.1681, -0.1496, -0.3376, +0.4545, +0.2194,
+         -1.5197, -1.2230, +3.2199, +1.0545, +0.1571, +0.8699, -4.2024,
+         +0.9999, +1.8670, +0.3410, +2.0595, -0.1076, +1.5986, -0.3768,
+         -0.7450, -1.0840, -0.0150, -3.7617, +1.4254, +0.8361, +0.2078,
+         -0.8808, -0.2463, -3.4380, +1.4160, -1.9439, -1.0356, +2.8675,
+         +0.9860, -3.6350, -0.8313, +0.2089, +1.3341, -2.8791, +0.4524,
+         +0.4555, -0.9851, +0.6086, +0.7343, +1.3275, +1.0979, -2.4916,
+         +0.0722, -0.7323, +0.9880, +1.5356, +0.5431, -0.3248, -0.9631,
+         +0.7564, +0.7160, -0.0709, -1.5644, +1.2694, +2.5064, -2.5448,
+         -1.7748, -1.1803, -1.1250, -0.2383, -1.1826, -1.1312, +1.7279,
+         -0.3852, +0.0540, -0.9594, -0.7022, -2.7155, +1.2207, -0.4283,
+         +0.2370, -1.1960, +1.0917, -0.3953, -0.5567, -2.5472, -2.0418,
+         -0.6104, +3.4862, -0.4001, +0.9669, -0.7622, +0.1008, -1.7957,
+         +1.0803, -0.3787, -0.5001, -0.8955, +0.8044, +2.5939, +1.5200,
+         +0.9680, +0.4177, -0.2482, +2.2624, -1.1416, -1.5353, +2.2794,
+         -1.8803, +0.2573, -0.3707, +2.0442, +2.7544, +1.6080, -0.6924,
+         -0.8825, +2.8865, -0.7160, -1.4308, -0.0376, +0.3018, +1.8101,
+         +0.3378, -0.3874, -0.3636, -0.5764, -1.0234, +2.6209, +1.1319,
+         +1.4093, +0.6829, -1.3812, +1.8284, +0.1921, -0.0356, -2.3225,
+         -0.8761, +1.8518, -1.0584, -0.8935, -0.6575, +0.3690, -0.9779,
+         +0.2513, +0.4220, +1.2618, +2.0158, +0.1686, +1.9862, +0.6183,
+         -1.2881, -1.7737, +0.0968, -0.8762, -0.9781, -1.3425, -2.6438,
+         +0.8928, +0.3356, +0.1711, +1.9928, +0.5683, +2.1339, -0.5671,
+         -0.2999, +0.2771, +1.8581, -0.7244
+     ],
+     [
+         -0.8237, -2.4723, -0.0093, -1.0811, +0.5365, +0.6595, -0.5366,
+         +1.1555, -0.8875, -1.3330, +1.4121, -0.0944, -0.0463, -0.9496,
+         -2.8476, -0.7136, +0.6020, -0.0054, -1.4527, +0.9208, +0.3012,
+         -0.1480, -0.8642, +0.1982, +0.1088, +1.2782, -0.7724, -1.3168,
+         -0.9594, -1.1935, -0.3237, -0.6884, -0.1389, +0.7907, -2.4444,
+         -0.4292, -0.1315, -1.2616, -0.0916, -1.7440, -1.5036, +0.1978,
+         +1.2539, +0.7862, +0.0931, -1.4788, +0.1313, -0.0527, -1.4072,
+         +0.4968, -0.2224, +0.3658, +1.6503, -0.2057, -0.6812, -0.2703,
+         -1.6274, +1.0684, +1.2275, +0.1127, -2.2420, -0.1408, -0.2346,
+         -0.1119, +0.1498, -0.0589, -1.4065, -0.1043, +1.4940, -0.2420,
+         -0.2223, -0.0973, +0.5512, -1.6769, +0.3286, +0.5443, -0.1542,
+         +0.9522, -0.8605, +1.4236, +1.6691, -1.5443, -1.4476, -0.2985,
+         +0.5213, -0.6793, -1.4328, -0.4417, -0.0097, -0.3373, -0.0559,
+         -0.3530, +0.5015, -2.2177, -0.8512, -1.4107, -0.2278, +1.0842,
+         -0.6454, -0.6701, +0.2807, -2.3556, +0.6937, -0.1830, +0.2528,
+         +1.4582, +0.9585, +0.7474, -0.2290, -0.3547, -0.0505, +0.8672,
+         -1.3728, +0.3593, -1.5079, +1.2051, +0.1671, +0.1287, -0.1089,
+         +0.9447, +0.2902, -0.0274, +2.6386, -0.4357, -1.0866, -0.7725,
+         -0.7474, -0.2920, -0.7512, +0.2729, -0.2291, +0.4386, -0.2122,
+         -1.0203, +0.5233, -0.3802, -0.1169, +0.1006, -0.5432, -0.2080,
+         +0.6461, -1.4341, +0.0192, -1.2795, +0.8708, -0.3168, -0.4657,
+         +0.5388, +0.8268, -0.9466, -0.4054, +1.5877, +0.1454, +0.5291,
+         -0.8998, +1.3563, -1.8375, -1.0953, -1.6413, +0.2750, +1.0708,
+         -0.9597, -0.3052, -0.0953, -2.2925, -0.0300, +1.2808, +1.3430,
+         -0.0976, -0.7264, +1.1371, -0.7336, +0.1982, -0.6328, -0.0308,
+         +0.8406, -0.1227, -0.1331, -2.1831, -0.5677, +0.4565, -0.1594,
+         -1.8026, -0.1020, +1.3437, -0.2881, +1.0612, -0.9352, -0.2676,
+         +0.2737, +0.6647, -0.4582, -0.3532, -0.2741, -0.5150, -0.2847,
+         +0.3100, +1.0055, +0.6360, +1.2014, -1.0370, +0.3126, +0.2799,
+         +1.4690, +0.2299, -0.9563, +0.1604, +1.0580, -0.0236, +0.5362,
+         -1.1913, -1.6834, -0.5451, -0.2479, +0.5986, +0.9416, +0.8835,
+         -0.8778, -1.5741, +0.2702, +0.9200, -1.2569, -0.0010, +0.7474,
+         +1.0404, +0.0567, +0.7431, -0.5104, +0.2774, -0.3255, +0.0748,
+         +0.5445, +2.7912, +0.1370, +0.0845, -0.2513, -0.2607, +0.5337,
+         -0.9225, +1.1601, +0.3889, -1.9067, +0.0405, +0.8032, +0.3716,
+         +1.0054, -0.1264, -1.2699, +1.0490, -0.4518, -0.4633, +0.2149,
+         -0.0493, +0.4784, -0.4567, -1.1741
+     ],
+     [
+         -0.0944, +0.0480, +1.3354, +0.4432, +0.4545, +0.3836, +0.7112,
+         -1.3257, -0.6328, -0.0922, +0.1074, +0.9008, +0.8560, -0.7031,
+         +0.6321, -0.5160, -0.0619, -2.3403, +0.1938, -1.3679, -0.0181,
+         -0.1057, -0.2195, -1.2982, +0.9032, +0.4164, -0.7188, -0.5110,
+         +0.6287, +1.9699, -1.0438, -0.3658, +0.3573, -1.1268, +0.8508,
+         -0.2637, +0.4056, +0.0488, -0.1575, -0.0724, +1.3003, +0.7745,
+         -1.0815, -0.1154, +0.3653, +0.4608, -0.6923, +0.1087, +0.1252,
+         +1.0462, -0.8961, +0.3603, -0.5450, +1.1358, -0.2845, +0.3265,
+         -0.8838, +1.1137, -0.6003, -0.6661, +0.6010, +0.3586, +0.8275,
+         +0.7424, +0.8698, +0.4970, +0.5513, +1.1435, -0.3027, -0.6154,
+         -0.1741, +1.3238, +0.2132, -0.2333, -0.1694, +0.7055, -0.8932,
+         -0.0664, +0.3277, -0.5222, -0.3499, -0.3942, -0.4391, +0.0751,
+         -0.7457, -1.1832, -0.4438, -0.3663, -1.0466, +0.1051, -1.1153,
+         +0.4985, -0.2054, -0.0507, +0.3941, -0.1353, -0.4284, +0.2639,
+         +0.4999, +0.8301, -0.7917, -0.3978, -0.0342, +0.1796, +0.3845,
+         -1.4847, -0.4932, -0.1793, -0.9531, +0.5409, -0.7341, +0.7626,
+         +0.3836, +0.5465, +0.1335, -0.2535, -0.4195, -0.7734, -0.0282,
+         +0.0965, +0.0656, +0.9656, -0.8877, +0.8172, -0.1439, -1.4527,
+         -0.0562, +1.5419, +0.0539, -0.5125, +0.5689, +0.1007, -1.2620,
+         +0.3370, -0.1986, -0.2692, -1.2472, -0.7832, -0.6892, +0.6072,
+         +0.0229, +0.4541, -0.4522, +0.0858, -0.8327, -0.0613, +0.3183,
+         -0.3064, -1.0261, +0.5405, +1.0349, -0.2815, -0.6725, +0.4688,
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-
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+         -0.4070, +0.0242
+     ]])
+
+weights_dense2_b = np.array([
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+    -0.1559, -0.1759, +0.0344, +0.3008, +0.3244, +0.3597, +0.1691, +0.0277,
+    +0.0064, +0.2372, +0.0961, +0.3293, +0.2077, +0.1230, +0.1195, +0.4294,
+    +0.2816, +0.1169, +0.4083, +0.0309, +0.0433, +0.4852, +0.1903, +0.1055,
+    +0.2894, +0.0468, +0.0669, +0.1655, -0.0712, +0.2791, +0.3653, +0.1661,
+    +0.0730, +0.4481, +0.2336, +0.4547, +0.3048, +0.3674, +0.1072, +0.2519,
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+    +0.2301, +0.3551, +0.1974, +0.5505, +0.1776, +0.0702, -0.0460, +0.1697,
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+    +0.0219, -0.0728, +0.2957, +0.0854, +0.0055, -0.0289, +0.0866, +0.0203,
+    +0.0353, +0.2069, +0.3297, +0.1171, +0.0607, +0.1667, +0.5721, -0.0218
 ])
 
-weights_final_b = np.array([ -0.0274, +0.1314, -0.0578, +0.2965, +0.1318, -0.0622, +0.1158, +0.0643, +0.2138, -0.1422, +0.1579, +0.0836, -0.0388, -0.0933, +0.2233, -0.2276, +0.0375])
+weights_final_w = np.array(
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+        -0.0525, -0.1167, +0.0605, +0.1389, -0.0801, -0.2973, +0.1066, +0.0129,
+        +0.0549
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+         +0.0614, -0.1857, +0.0101
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+         +0.5889, +0.1608, +0.3169
+     ],
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+         +0.0046, -0.1664, -0.1933
+     ],
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+         +0.2743, +0.0971, -0.1144
+     ],
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+         -0.4098, +0.2590, -0.0564
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+         +0.3433, +0.1957, +0.2411
+     ],
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+         +0.0053, -0.0098, -0.2020
+     ],
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+         +0.2374, +0.2102, +0.0998
+     ],
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+     ],
+     [
+         +0.2535, +0.5913, +0.2155, -0.0587, -0.0918, -0.0362, +0.7135,
+         +0.3591, +0.4240, +0.3692, -0.0313, -0.2431, +0.9143, -0.0241,
+         -0.6210, +0.4646, -0.1512
+     ],
+     [
+         +0.3188, +0.2507, -0.2129, -0.4617, +0.1874, -0.1286, +0.0632,
+         -0.2470, -0.0572, +0.6183, +0.3531, +0.1321, -0.1687, +0.1307,
+         -0.3712, +0.0199, -0.0776
+     ],
+     [
+         -0.1779, +0.3660, +0.2386, +0.2297, +0.1792, -0.1369, +0.2320,
+         -0.5867, -0.1306, +0.3471, -0.1127, +0.3352, -0.0214, +0.0801,
+         -0.1368, -0.7262, +0.1757
+     ],
+     [
+         +0.0183, -0.0485, +0.1199, +0.2513, +0.2022, -0.0472, +0.3076,
+         +0.0656, -0.3302, -0.0587, -0.0200, -0.5474, -0.0863, +0.2087,
+         -0.2581, -0.1750, +0.3391
+     ],
+     [
+         -0.2339, +0.0639, +0.2085, -0.1872, -0.2686, +0.1490, +0.0436,
+         -0.0841, -0.1054, -0.4899, +0.0193, -0.0250, +0.2212, +0.0972,
+         +0.0329, +0.1328, -0.0545
+     ],
+     [
+         -0.0874, -0.1087, -0.1577, +0.0936, -0.3083, -0.1450, +0.0365,
+         -0.1924, -0.0536, +0.0475, +0.0348, +0.0772, +0.0348, -0.2800,
+         +0.0864, +0.2640, -0.0215
+     ],
+     [
+         +0.0745, +0.4503, -0.4064, +0.6300, -0.1275, -0.0463, -0.4074,
+         +0.0031, +0.1408, +0.0531, +0.1400, +0.0308, -0.0220, -0.0014,
+         -0.3056, -0.1551, -0.0096
+     ],
+     [
+         +0.1479, +0.1186, +0.1323, -0.3466, -0.0654, -0.1084, -0.2509,
+         +0.0944, -0.2135, +0.2020, +0.0602, -0.1239, +0.0741, +0.2037,
+         -0.4462, +0.1065, +0.1710
+     ]])
+
+weights_final_b = np.array([
+    -0.0274, +0.1314, -0.0578, +0.2965, +0.1318, -0.0622, +0.1158, +0.0643,
+    +0.2138, -0.1422, +0.1579, +0.0836, -0.0388, -0.0933, +0.2233, -0.2276,
+    +0.0375
+])
+# yapf: enable
 
-if __name__=="__main__":
-    demo_run()
+if __name__ == "__main__":
+  demo_run()
diff --git a/examples/pybullet/pybullet.c b/examples/pybullet/pybullet.c
index 9946da3cf..5b8ca6e5b 100644
--- a/examples/pybullet/pybullet.c
+++ b/examples/pybullet/pybullet.c
@@ -1942,7 +1942,7 @@ static PyObject* pybullet_loadSoftBody(PyObject* self, PyObject* args, PyObject*
 	int physicsClientId = 0;
 	int flags = 0;
 
-	static char* kwlist[] = {"fileName", "scale", "mass", "collisionMargin", "physicsClientId", NULL};
+	static char* kwlist[] = {"fileName", "basePosition", "baseOrientation", "scale", "mass", "collisionMargin", "physicsClientId", NULL};
 
 	int bodyUniqueId = -1;
 	const char* fileName = "";
@@ -1952,10 +1952,36 @@ static PyObject* pybullet_loadSoftBody(PyObject* self, PyObject* args, PyObject*
 
 	b3PhysicsClientHandle sm = 0;
 
-	if (!PyArg_ParseTupleAndKeywords(args, keywds, "s|dddi", kwlist, &fileName, &scale, &mass, &collisionMargin, &physicsClientId))
+	double startPos[3] = {0.0, 0.0, 0.0};
+	double startOrn[4] = {0.0, 0.0, 0.0, 1.0};
+
+
+	PyObject* basePosObj = 0;
+	PyObject* baseOrnObj = 0;
+
+	if (!PyArg_ParseTupleAndKeywords(args, keywds, "s|OOdddi", kwlist, &fileName, &basePosObj, &baseOrnObj, &scale, &mass, &collisionMargin, &physicsClientId))
 	{
 		return NULL;
 	}
+	else
+	{
+		if (basePosObj)
+		{
+			if (!pybullet_internalSetVectord(basePosObj, startPos))
+			{
+				PyErr_SetString(SpamError, "Cannot convert basePosition.");
+				return NULL;
+			}
+		}
+		if (baseOrnObj)
+		{
+			if (!pybullet_internalSetVector4d(baseOrnObj, startOrn))
+			{
+				PyErr_SetString(SpamError, "Cannot convert baseOrientation.");
+				return NULL;
+			}
+		}
+	}
 
 	sm = getPhysicsClient(physicsClientId);
 	if (sm == 0)
@@ -1971,6 +1997,9 @@ static PyObject* pybullet_loadSoftBody(PyObject* self, PyObject* args, PyObject*
 		b3SharedMemoryCommandHandle command =
 			b3LoadSoftBodyCommandInit(sm, fileName);
 
+		b3LoadSoftBodySetStartPosition(command, startPos[0], startPos[1], startPos[2]);
+		b3LoadSoftBodySetStartOrientation(command, startOrn[0], startOrn[1], startOrn[2], startOrn[3]);
+
 		if (scale > 0)
 		{
 			b3LoadSoftBodySetScale(command, scale);
diff --git a/examples/pybullet/tensorflow/humanoid_running.py b/examples/pybullet/tensorflow/humanoid_running.py
index 66a130cf2..8a7f1159f 100644
--- a/examples/pybullet/tensorflow/humanoid_running.py
+++ b/examples/pybullet/tensorflow/humanoid_running.py
@@ -6,47 +6,47 @@ import pybullet as p
 import time
 
 cid = p.connect(p.SHARED_MEMORY)
-if (cid<0):
-	cid = p.connect(p.GUI) #DIRECT is much faster, but GUI shows the running gait
+if (cid < 0):
+  cid = p.connect(p.GUI)  #DIRECT is much faster, but GUI shows the running gait
 
-p.setGravity(0,0,-9.8)
-p.setPhysicsEngineParameter(fixedTimeStep=1.0/60.,solverResidualThreshold=1-10, numSolverIterations=50, numSubSteps=4)
+p.setGravity(0, 0, -9.8)
+p.setPhysicsEngineParameter(fixedTimeStep=1.0 / 60.,
+                            solverResidualThreshold=1 - 10,
+                            numSolverIterations=50,
+                            numSubSteps=4)
 #this mp4 recording requires ffmpeg installed
 #mp4log = p.startStateLogging(p.STATE_LOGGING_VIDEO_MP4,"humanoid.mp4")
 
 p.loadSDF("stadium.sdf")
 #p.loadURDF("plane.urdf")
 
-objs = p.loadMJCF("mjcf/humanoid_symmetric_no_ground.xml",flags = p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS)
-human  = objs[0]
+objs = p.loadMJCF("mjcf/humanoid_symmetric_no_ground.xml",
+                  flags=p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS)
+human = objs[0]
 ordered_joints = []
 ordered_joint_indices = []
 
 parser = argparse.ArgumentParser()
 parser.add_argument('--profile')
 
-
 jdict = {}
-for j in range( p.getNumJoints(human) ):
-	info = p.getJointInfo(human, j)
-	link_name = info[12].decode("ascii")
-	if link_name=="left_foot": left_foot = j
-	if link_name=="right_foot": right_foot = j
-	ordered_joint_indices.append(j)
-		
-	if info[2] != p.JOINT_REVOLUTE: continue
-	jname = info[1].decode("ascii")
-	jdict[jname] = j
-	lower, upper = (info[8], info[9])
-	ordered_joints.append( (j, lower, upper) )
-	
-	
-	p.setJointMotorControl2(human, j, controlMode=p.VELOCITY_CONTROL, force=0)
-
-	
-
-motor_names  = ["abdomen_z", "abdomen_y", "abdomen_x"]
-motor_power  = [100, 100, 100]
+for j in range(p.getNumJoints(human)):
+  info = p.getJointInfo(human, j)
+  link_name = info[12].decode("ascii")
+  if link_name == "left_foot": left_foot = j
+  if link_name == "right_foot": right_foot = j
+  ordered_joint_indices.append(j)
+
+  if info[2] != p.JOINT_REVOLUTE: continue
+  jname = info[1].decode("ascii")
+  jdict[jname] = j
+  lower, upper = (info[8], info[9])
+  ordered_joints.append((j, lower, upper))
+
+  p.setJointMotorControl2(human, j, controlMode=p.VELOCITY_CONTROL, force=0)
+
+motor_names = ["abdomen_z", "abdomen_y", "abdomen_x"]
+motor_power = [100, 100, 100]
 motor_names += ["right_hip_x", "right_hip_z", "right_hip_y", "right_knee"]
 motor_power += [100, 100, 300, 200]
 motor_names += ["left_hip_x", "left_hip_z", "left_hip_y", "left_knee"]
@@ -57,597 +57,7951 @@ motor_names += ["left_shoulder1", "left_shoulder2", "left_elbow"]
 motor_power += [75, 75, 75]
 motors = [jdict[n] for n in motor_names]
 
+
 class Dummy:
-	pass
+  pass
+
+
 dummy = Dummy()
 dummy.initial_z = None
 
+
 def current_relative_position(jointStates, human, j, lower, upper):
-	#print("j")
-	#print(j)
-	#print (len(jointStates))
-	#print(j)
-	temp  = jointStates[j]
-	pos = temp[0]
-	vel = temp[1]
-	#print("pos")
-	#print(pos)
-	#print("vel")
-	#print(vel)
-	pos_mid = 0.5 * (lower + upper);
-	return (
-		2 * (pos - pos_mid) / (upper - lower),
-		0.1 * vel
-		)
+  #print("j")
+  #print(j)
+  #print (len(jointStates))
+  #print(j)
+  temp = jointStates[j]
+  pos = temp[0]
+  vel = temp[1]
+  #print("pos")
+  #print(pos)
+  #print("vel")
+  #print(vel)
+  pos_mid = 0.5 * (lower + upper)
+  return (2 * (pos - pos_mid) / (upper - lower), 0.1 * vel)
+
 
 def collect_observations(human):
-	#print("ordered_joint_indices")
-	#print(ordered_joint_indices)
-	
-	jointStates = p.getJointStates(human,ordered_joint_indices)
-	j = np.array([current_relative_position(jointStates, human, *jtuple) for jtuple in ordered_joints]).flatten()
-	#print("j")
-	#print(j)
-	body_xyz, (qx, qy, qz, qw) = p.getBasePositionAndOrientation(human)
-	#print("body_xyz")
-	#print(body_xyz, qx,qy,qz,qw)
-	z = body_xyz[2]
-	dummy.distance = body_xyz[0]
-	if dummy.initial_z==None:
-		dummy.initial_z = z
-	(vx, vy, vz), _ = p.getBaseVelocity(human)
-	more = np.array([z-dummy.initial_z, 0.1*vx, 0.1*vy, 0.1*vz, qx, qy, qz, qw])
-	rcont = p.getContactPoints(human, -1, right_foot, -1)
-	#print("rcont")
-	#print(rcont)
-	lcont = p.getContactPoints(human, -1, left_foot, -1)
-	#print("lcont")
-	#print(lcont)
-	feet_contact = np.array([len(rcont)>0, len(lcont)>0])
-	return np.clip( np.concatenate([more] + [j] + [feet_contact]), -5, +5)
+  #print("ordered_joint_indices")
+  #print(ordered_joint_indices)
+
+  jointStates = p.getJointStates(human, ordered_joint_indices)
+  j = np.array([
+      current_relative_position(jointStates, human, *jtuple) for jtuple in ordered_joints
+  ]).flatten()
+  #print("j")
+  #print(j)
+  body_xyz, (qx, qy, qz, qw) = p.getBasePositionAndOrientation(human)
+  #print("body_xyz")
+  #print(body_xyz, qx,qy,qz,qw)
+  z = body_xyz[2]
+  dummy.distance = body_xyz[0]
+  if dummy.initial_z == None:
+    dummy.initial_z = z
+  (vx, vy, vz), _ = p.getBaseVelocity(human)
+  more = np.array([z - dummy.initial_z, 0.1 * vx, 0.1 * vy, 0.1 * vz, qx, qy, qz, qw])
+  rcont = p.getContactPoints(human, -1, right_foot, -1)
+  #print("rcont")
+  #print(rcont)
+  lcont = p.getContactPoints(human, -1, left_foot, -1)
+  #print("lcont")
+  #print(lcont)
+  feet_contact = np.array([len(rcont) > 0, len(lcont) > 0])
+  return np.clip(np.concatenate([more] + [j] + [feet_contact]), -5, +5)
+
 
 class SmallReactivePolicy(object):
-    def __init__(self, name, ob_space, ac_space):
-        self.name = name
-        with tf.variable_scope(name):
-            batch_of_observations = [None] + list(ob_space.shape)
-            self.ob = tf.placeholder(dtype=tf.float32, shape=batch_of_observations, name="ob")
-            x = self.ob
-
-            width = 256
-            self.dense1_w = tf.get_variable("dense1_w", [x.get_shape()[1], width])
-            self.dense1_b = tf.get_variable("dense1_b", [width])
-            x = tf.nn.relu(tf.matmul(x, self.dense1_w) + self.dense1_b)
-
-            width = 128
-            self.dense2_w = tf.get_variable("dense2_w", [x.get_shape()[1], width])
-            self.dense2_b = tf.get_variable("dense2_b", [width])
-            x = tf.nn.relu(tf.matmul(x, self.dense2_w) + self.dense2_b)
-
-            assert(len(ac_space.shape)==1)
-            width = ac_space.shape[0]
-            self.final_w = tf.get_variable("final_w", [x.get_shape()[1], width])
-            self.final_b = tf.get_variable("final_b", [width])
-            x = tf.matmul(x, self.final_w) + self.final_b
-            self.action_node = x
-
-            self.all_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, tf.get_variable_scope().name)
-
-    def act(self, ob):
-        a = tf.get_default_session().run(self.action_node, feed_dict={ self.ob: ob[None] })
-        return a[0]
-
-    def load_weights(self):
-        assigns = [
-            (self.dense1_w, weights_dense1_w),
-            (self.dense1_b, weights_dense1_b),
-            (self.dense2_w, weights_dense2_w),
-            (self.dense2_b, weights_dense2_b),
-            (self.final_w, weights_final_w),
-            (self.final_b, weights_final_b)]
-        to_run = []
-        feed_dict = {}
-        for var, w in assigns:
-            ph = tf.placeholder(tf.float32, w.shape)
-            to_run.append( tf.assign(var, ph) )
-            feed_dict[ph] = w
-        tf.get_default_session().run(to_run, feed_dict=feed_dict)
+
+  def __init__(self, name, ob_space, ac_space):
+    self.name = name
+    with tf.variable_scope(name):
+      batch_of_observations = [None] + list(ob_space.shape)
+      self.ob = tf.placeholder(dtype=tf.float32, shape=batch_of_observations, name="ob")
+      x = self.ob
+
+      width = 256
+      self.dense1_w = tf.get_variable("dense1_w", [x.get_shape()[1], width])
+      self.dense1_b = tf.get_variable("dense1_b", [width])
+      x = tf.nn.relu(tf.matmul(x, self.dense1_w) + self.dense1_b)
+
+      width = 128
+      self.dense2_w = tf.get_variable("dense2_w", [x.get_shape()[1], width])
+      self.dense2_b = tf.get_variable("dense2_b", [width])
+      x = tf.nn.relu(tf.matmul(x, self.dense2_w) + self.dense2_b)
+
+      assert (len(ac_space.shape) == 1)
+      width = ac_space.shape[0]
+      self.final_w = tf.get_variable("final_w", [x.get_shape()[1], width])
+      self.final_b = tf.get_variable("final_b", [width])
+      x = tf.matmul(x, self.final_w) + self.final_b
+      self.action_node = x
+
+      self.all_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
+                                             tf.get_variable_scope().name)
+
+  def act(self, ob):
+    a = tf.get_default_session().run(self.action_node, feed_dict={self.ob: ob[None]})
+    return a[0]
+
+  def load_weights(self):
+    assigns = [(self.dense1_w, weights_dense1_w), (self.dense1_b, weights_dense1_b),
+               (self.dense2_w, weights_dense2_w), (self.dense2_b, weights_dense2_b),
+               (self.final_w, weights_final_w), (self.final_b, weights_final_b)]
+    to_run = []
+    feed_dict = {}
+    for var, w in assigns:
+      ph = tf.placeholder(tf.float32, w.shape)
+      to_run.append(tf.assign(var, ph))
+      feed_dict[ph] = w
+    tf.get_default_session().run(to_run, feed_dict=feed_dict)
+
 
 def demo_run():
-	sess = tf.InteractiveSession(config=tf.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1, device_count={ "GPU": 0 }))
-	pi = SmallReactivePolicy("pi", np.zeros((44,)), np.zeros((17,)))
-	pi.load_weights()
-	t1 = time.time()
-	timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "humanoidTimings.json")
-
-	frame = 0
-	while 1:
-		obs = collect_observations(human)
-	
-		actions = pi.act(obs)
-	
-		#print(" ".join(["%+0.2f"%x for x in obs]))
-		#print("Motors")
-		#print(motors)
-
-		#for m in range(len(motors)):
-			#print("motor_power")
-			#print(motor_power[m])
-			#print("actions[m]")
-			#print(actions[m])
-		#p.setJointMotorControl2(human, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082)
-		#p.setJointMotorControl2(human1, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082)
-			
-		forces = [0.] * len(motors)
-		for m in range(len(motors)):
-			limit=15
-			ac = np.clip(actions[m],-limit,limit)
-			#print (ac)
-			forces[m] = motor_power[m]*ac*0.082
-		p.setJointMotorControlArray(human, motors,controlMode=p.TORQUE_CONTROL, forces=forces)
-
-		p.stepSimulation()
-		#time.sleep(0.01)
-		distance=5
-		yaw = 0
-		humanPos, humanOrn = p.getBasePositionAndOrientation(human)
-		p.resetDebugVisualizerCamera(distance,yaw,-20,humanPos);
-		frame += 1
-		
-		if frame==1000: break
-	t2 = time.time()
-	print("############################### distance = %0.2f meters" % dummy.distance)
-	print("############################### FPS = ", 1000/ (t2-t1))
-	#print("Starting benchmark")
-	#logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS,"pybullet_humanoid_timings.json")
-	#p.stopStateLogging(logId)
-	print("ended benchmark")
-	print(frame)
-	p.stopStateLogging(timinglog)
-
-
-weights_dense1_w = np.array([
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-])
+  sess = tf.InteractiveSession(config=tf.ConfigProto(
+      inter_op_parallelism_threads=1, intra_op_parallelism_threads=1, device_count={"GPU": 0}))
+  pi = SmallReactivePolicy("pi", np.zeros((44,)), np.zeros((17,)))
+  pi.load_weights()
+  t1 = time.time()
+  timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "humanoidTimings.json")
+
+  frame = 0
+  while 1:
+    obs = collect_observations(human)
+
+    actions = pi.act(obs)
+
+    #print(" ".join(["%+0.2f"%x for x in obs]))
+    #print("Motors")
+    #print(motors)
+
+    #for m in range(len(motors)):
+    #print("motor_power")
+    #print(motor_power[m])
+    #print("actions[m]")
+    #print(actions[m])
+    #p.setJointMotorControl2(human, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082)
+    #p.setJointMotorControl2(human1, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082)
 
-weights_dense1_b = np.array([ -0.0562, -0.1282, -0.3753, -0.1119, -0.0136, -0.2313, -0.0345, +0.0894, -0.0010, -0.0601, -0.0762, -0.1205, -0.1190, +0.0179, -0.0906, -0.0572, +0.0797, -0.2050, -0.0318, -0.2306, -0.0182, -0.0847, +0.0607, -0.1987, -0.1262, -0.1896, -0.0226, -0.0034, -0.0809, +0.0404, +0.0756, -0.1168, -0.0733, -0.0739, +0.0161, -0.0528, +0.0067, +0.0332, -0.2382, -0.0745, -0.0184, -0.0287, +0.1160, -0.0248, -0.1523, -0.0589, -0.3165, -0.0986, -0.0604, -0.0633, +0.0549, -0.0209, -0.0474, -0.2358, -0.0890, -0.1201, -0.0595, +0.1562, -0.1967, -0.0995, -0.1231, +0.0500, +0.0962, -0.1140, +0.0393, -0.1390, -0.1273, -0.0866, +0.0868, -0.0849, -0.1148, -0.2904, +0.0706, -0.1821, -0.1143, -0.2196, -0.0112, -0.0375, -0.1567, -0.0589, +0.1257, +0.1125, -0.1020, +0.0818, -0.2838, -0.1653, -0.1676, -0.0244, -0.0784, -0.0181, -0.0429, -0.0966, +0.0494, +0.0093, -0.0858, -0.0919, -0.1196, -0.2181, +0.0292, -0.2283, -0.2595, -0.0925, -0.0134, -0.0093, +0.0310, +0.0078, -0.0172, -0.0803, -0.0386, +0.1367, -0.1948, -0.0290, +0.0785, -0.0464, +0.0371, -0.0128, -0.0377, -0.0724, +0.1637, -0.1972, -0.1093, -0.1634, -0.0691, -0.0834, -0.2310, -0.2676, -0.0683, -0.0024, -0.1401, -0.0378, -0.1633, -0.1665, -0.1098, -0.0746, -0.0684, -0.1914, -0.1284, -0.0998, +0.0577, -0.0638, -0.0885, -0.0445, -0.0384, -0.1012, +0.0067, -0.2128, -0.1312, +0.0526, +0.1238, +0.0742, +0.0261, +0.0444, +0.0614, +0.1223, +0.1232, -0.1827, -0.1289, -0.0469, -0.1420, +0.0705, -0.2220, -0.2401, -0.1121, -0.1697, -0.1621, +0.0688, +0.0688, -0.0040, -0.0048, -0.0416, -0.1265, +0.0311, +0.0280, +0.0287, -0.1893, -0.0966, -0.0876, -0.1778, +0.0000, +0.0074, -0.0978, -0.1932, -0.0790, +0.0037, -0.1398, -0.1006, -0.0532, -0.1227, +0.1148, -0.1254, -0.1134, +0.1205, -0.1588, +0.1093, -0.2470, -0.0915, -0.1715, -0.0036, -0.2943, +0.1375, +0.1075, -0.0339, -0.0491, +0.0060, +0.0145, -0.1867, +0.0646, -0.1327, -0.0529, -0.0830, +0.1433, -0.1924, -0.0285, -0.2420, -0.2244, -0.1451, -0.1009, -0.1149, +0.0846, +0.0473, +0.1568, -0.0062, -0.1289, -0.2085, +0.0600, -0.1314, -0.1569, +0.2043, -0.0302, -0.1666, +0.0473, -0.0075, -0.2825, -0.0696, -0.2223, -0.2911, -0.0567, -0.0510, +0.0184, -0.2132, -0.0371, +0.1715, -0.1301, -0.0310, +0.1523, -0.0142, +0.0185, -0.0030, -0.0725, -0.1011, -0.0923, -0.1991, -0.1686, -0.1198, -0.2226, -0.1094])
-
-weights_dense2_w = np.array([
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+    forces = [0.] * len(motors)
+    for m in range(len(motors)):
+      limit = 15
+      ac = np.clip(actions[m], -limit, limit)
+      #print (ac)
+      forces[m] = motor_power[m] * ac * 0.082
+    p.setJointMotorControlArray(human, motors, controlMode=p.TORQUE_CONTROL, forces=forces)
+
+    p.stepSimulation()
+    #time.sleep(0.01)
+    distance = 5
+    yaw = 0
+    humanPos, humanOrn = p.getBasePositionAndOrientation(human)
+    p.resetDebugVisualizerCamera(distance, yaw, -20, humanPos)
+    frame += 1
+
+    if frame == 1000: break
+  t2 = time.time()
+  print("############################### distance = %0.2f meters" % dummy.distance)
+  print("############################### FPS = ", 1000 / (t2 - t1))
+  #print("Starting benchmark")
+  #logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS,"pybullet_humanoid_timings.json")
+  #p.stopStateLogging(logId)
+  print("ended benchmark")
+  print(frame)
+  p.stopStateLogging(timinglog)
+
+
+# yapf: disable
+weights_dense1_w = np.array(
+    [[
+        -0.3857, -0.7450, +0.1473, +0.1996, +0.0570, +0.4522, +0.1172, +0.7558,
+        -0.2530, -0.7948, +0.3120, +0.6216, +0.1044, -0.4347, +0.0921, +0.0187,
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-
-weights_final_w = np.array([
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+         -0.4070, +0.0242
+     ]])
+
+weights_dense2_b = np.array([
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+    -0.1559, -0.1759, +0.0344, +0.3008, +0.3244, +0.3597, +0.1691, +0.0277,
+    +0.0064, +0.2372, +0.0961, +0.3293, +0.2077, +0.1230, +0.1195, +0.4294,
+    +0.2816, +0.1169, +0.4083, +0.0309, +0.0433, +0.4852, +0.1903, +0.1055,
+    +0.2894, +0.0468, +0.0669, +0.1655, -0.0712, +0.2791, +0.3653, +0.1661,
+    +0.0730, +0.4481, +0.2336, +0.4547, +0.3048, +0.3674, +0.1072, +0.2519,
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+    +0.2301, +0.3551, +0.1974, +0.5505, +0.1776, +0.0702, -0.0460, +0.1697,
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+    +0.2252, +0.1527, +0.3315, +0.1419, +0.3483, +0.3999, +0.2818, +0.1021,
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+    +0.0219, -0.0728, +0.2957, +0.0854, +0.0055, -0.0289, +0.0866, +0.0203,
+    +0.0353, +0.2069, +0.3297, +0.1171, +0.0607, +0.1667, +0.5721, -0.0218
 ])
 
-weights_final_b = np.array([ -0.0274, +0.1314, -0.0578, +0.2965, +0.1318, -0.0622, +0.1158, +0.0643, +0.2138, -0.1422, +0.1579, +0.0836, -0.0388, -0.0933, +0.2233, -0.2276, +0.0375])
+weights_final_w = np.array(
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+        -0.0525, -0.1167, +0.0605, +0.1389, -0.0801, -0.2973, +0.1066, +0.0129,
+        +0.0549
+    ],
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+         +0.0614, -0.1857, +0.0101
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+         +0.5889, +0.1608, +0.3169
+     ],
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+         +0.0046, -0.1664, -0.1933
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+         +0.2743, +0.0971, -0.1144
+     ],
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+         -0.4098, +0.2590, -0.0564
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+         +0.3433, +0.1957, +0.2411
+     ],
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+         +0.0111, +0.3729, -0.3116
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+         +0.1208, -0.3628, +0.2056
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+         +0.0053, -0.0098, -0.2020
+     ],
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+         +0.2374, +0.2102, +0.0998
+     ],
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+     ],
+     [
+         -0.1779, +0.3660, +0.2386, +0.2297, +0.1792, -0.1369, +0.2320,
+         -0.5867, -0.1306, +0.3471, -0.1127, +0.3352, -0.0214, +0.0801,
+         -0.1368, -0.7262, +0.1757
+     ],
+     [
+         +0.0183, -0.0485, +0.1199, +0.2513, +0.2022, -0.0472, +0.3076,
+         +0.0656, -0.3302, -0.0587, -0.0200, -0.5474, -0.0863, +0.2087,
+         -0.2581, -0.1750, +0.3391
+     ],
+     [
+         -0.2339, +0.0639, +0.2085, -0.1872, -0.2686, +0.1490, +0.0436,
+         -0.0841, -0.1054, -0.4899, +0.0193, -0.0250, +0.2212, +0.0972,
+         +0.0329, +0.1328, -0.0545
+     ],
+     [
+         -0.0874, -0.1087, -0.1577, +0.0936, -0.3083, -0.1450, +0.0365,
+         -0.1924, -0.0536, +0.0475, +0.0348, +0.0772, +0.0348, -0.2800,
+         +0.0864, +0.2640, -0.0215
+     ],
+     [
+         +0.0745, +0.4503, -0.4064, +0.6300, -0.1275, -0.0463, -0.4074,
+         +0.0031, +0.1408, +0.0531, +0.1400, +0.0308, -0.0220, -0.0014,
+         -0.3056, -0.1551, -0.0096
+     ],
+     [
+         +0.1479, +0.1186, +0.1323, -0.3466, -0.0654, -0.1084, -0.2509,
+         +0.0944, -0.2135, +0.2020, +0.0602, -0.1239, +0.0741, +0.2037,
+         -0.4462, +0.1065, +0.1710
+     ]])
+
+weights_final_b = np.array([
+    -0.0274, +0.1314, -0.0578, +0.2965, +0.1318, -0.0622, +0.1158, +0.0643,
+    +0.2138, -0.1422, +0.1579, +0.0836, -0.0388, -0.0933, +0.2233, -0.2276,
+    +0.0375
+])
+# yapf: enable
 
-if __name__=="__main__":
-    demo_run()
+if __name__ == "__main__":
+  demo_run()
diff --git a/examples/pybullet/tensorflow/humanoid_running_vr_follow.py b/examples/pybullet/tensorflow/humanoid_running_vr_follow.py
index 542c1397f..27148a48b 100644
--- a/examples/pybullet/tensorflow/humanoid_running_vr_follow.py
+++ b/examples/pybullet/tensorflow/humanoid_running_vr_follow.py
@@ -4,46 +4,42 @@ import numpy as np
 
 import pybullet as p
 import time
-p.connect(p.SHARED_MEMORY) #DIRECT is much faster, but GUI shows the running gait
+p.connect(p.SHARED_MEMORY)  #DIRECT is much faster, but GUI shows the running gait
 p.resetSimulation()
-p.setGravity(0,0,-9.8)
+p.setGravity(0, 0, -9.8)
 p.setRealTimeSimulation(0)
-p.setPhysicsEngineParameter(fixedTimeStep=1.0/60., numSolverIterations=5, numSubSteps=2)
+p.setPhysicsEngineParameter(fixedTimeStep=1.0 / 60., numSolverIterations=5, numSubSteps=2)
 #this mp4 recording requires ffmpeg installed
 #mp4log = p.startStateLogging(p.STATE_LOGGING_VIDEO_MP4,"humanoid.mp4")
 
-
-
-plane, human  = p.loadMJCF("mjcf/humanoid_symmetric.xml",flags = p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS)
+plane, human = p.loadMJCF("mjcf/humanoid_symmetric.xml",
+                          flags=p.URDF_USE_SELF_COLLISION_EXCLUDE_ALL_PARENTS)
 print("human")
 print(human)
-p.setVRCameraState([0,0,0],[0,0,0,0],human, trackObjectFlag=human)#p.VR_CAMERA_TRACK_OBJECT_ORIENTATION)
+p.setVRCameraState([0, 0, 0], [0, 0, 0, 0], human,
+                   trackObjectFlag=human)  #p.VR_CAMERA_TRACK_OBJECT_ORIENTATION)
 
 ordered_joints = []
 ordered_joint_indices = []
 
-
 jdict = {}
-for j in range( p.getNumJoints(human) ):
-	info = p.getJointInfo(human, j)
-	link_name = info[12].decode("ascii")
-	if link_name=="left_foot": left_foot = j
-	if link_name=="right_foot": right_foot = j
-	ordered_joint_indices.append(j)
-		
-	if info[2] != p.JOINT_REVOLUTE: continue
-	jname = info[1].decode("ascii")
-	jdict[jname] = j
-	lower, upper = (info[8], info[9])
-	ordered_joints.append( (j, lower, upper) )
-	
-	
-	p.setJointMotorControl2(human, j, controlMode=p.VELOCITY_CONTROL, force=0)
-
-	
-
-motor_names  = ["abdomen_z", "abdomen_y", "abdomen_x"]
-motor_power  = [100, 100, 100]
+for j in range(p.getNumJoints(human)):
+  info = p.getJointInfo(human, j)
+  link_name = info[12].decode("ascii")
+  if link_name == "left_foot": left_foot = j
+  if link_name == "right_foot": right_foot = j
+  ordered_joint_indices.append(j)
+
+  if info[2] != p.JOINT_REVOLUTE: continue
+  jname = info[1].decode("ascii")
+  jdict[jname] = j
+  lower, upper = (info[8], info[9])
+  ordered_joints.append((j, lower, upper))
+
+  p.setJointMotorControl2(human, j, controlMode=p.VELOCITY_CONTROL, force=0)
+
+motor_names = ["abdomen_z", "abdomen_y", "abdomen_x"]
+motor_power = [100, 100, 100]
 motor_names += ["right_hip_x", "right_hip_z", "right_hip_y", "right_knee"]
 motor_power += [100, 100, 300, 200]
 motor_names += ["left_hip_x", "left_hip_z", "left_hip_y", "left_knee"]
@@ -54,594 +50,7948 @@ motor_names += ["left_shoulder1", "left_shoulder2", "left_elbow"]
 motor_power += [75, 75, 75]
 motors = [jdict[n] for n in motor_names]
 
+
 class Dummy:
-	pass
+  pass
+
+
 dummy = Dummy()
 dummy.initial_z = None
 
+
 def current_relative_position(jointStates, human, j, lower, upper):
-	#print("j")
-	#print(j)
-	#print (len(jointStates))
-	#print(j)
-	temp  = jointStates[j]
-	pos = temp[0]
-	vel = temp[1]
-	#print("pos")
-	#print(pos)
-	#print("vel")
-	#print(vel)
-	pos_mid = 0.5 * (lower + upper);
-	return (
-		2 * (pos - pos_mid) / (upper - lower),
-		0.1 * vel
-		)
+  #print("j")
+  #print(j)
+  #print (len(jointStates))
+  #print(j)
+  temp = jointStates[j]
+  pos = temp[0]
+  vel = temp[1]
+  #print("pos")
+  #print(pos)
+  #print("vel")
+  #print(vel)
+  pos_mid = 0.5 * (lower + upper)
+  return (2 * (pos - pos_mid) / (upper - lower), 0.1 * vel)
+
 
 def collect_observations(human):
-	#print("ordered_joint_indices")
-	#print(ordered_joint_indices)
-	
-	jointStates = p.getJointStates(human,ordered_joint_indices)
-	j = np.array([current_relative_position(jointStates, human, *jtuple) for jtuple in ordered_joints]).flatten()
-	#print("j")
-	#print(j)
-	body_xyz, (qx, qy, qz, qw) = p.getBasePositionAndOrientation(human)
-	#print("body_xyz")
-	#print(body_xyz, qx,qy,qz,qw)
-	z = body_xyz[2]
-	dummy.distance = body_xyz[0]
-	if dummy.initial_z==None:
-		dummy.initial_z = z
-	(vx, vy, vz), _ = p.getBaseVelocity(human)
-	more = np.array([z-dummy.initial_z, 0.1*vx, 0.1*vy, 0.1*vz, qx, qy, qz, qw])
-	rcont = p.getContactPoints(human, -1, right_foot, -1)
-	#print("rcont")
-	#print(rcont)
-	lcont = p.getContactPoints(human, -1, left_foot, -1)
-	#print("lcont")
-	#print(lcont)
-	feet_contact = np.array([len(rcont)>0, len(lcont)>0])
-	return np.clip( np.concatenate([more] + [j] + [feet_contact]), -5, +5)
+  #print("ordered_joint_indices")
+  #print(ordered_joint_indices)
+
+  jointStates = p.getJointStates(human, ordered_joint_indices)
+  j = np.array([
+      current_relative_position(jointStates, human, *jtuple) for jtuple in ordered_joints
+  ]).flatten()
+  #print("j")
+  #print(j)
+  body_xyz, (qx, qy, qz, qw) = p.getBasePositionAndOrientation(human)
+  #print("body_xyz")
+  #print(body_xyz, qx,qy,qz,qw)
+  z = body_xyz[2]
+  dummy.distance = body_xyz[0]
+  if dummy.initial_z == None:
+    dummy.initial_z = z
+  (vx, vy, vz), _ = p.getBaseVelocity(human)
+  more = np.array([z - dummy.initial_z, 0.1 * vx, 0.1 * vy, 0.1 * vz, qx, qy, qz, qw])
+  rcont = p.getContactPoints(human, -1, right_foot, -1)
+  #print("rcont")
+  #print(rcont)
+  lcont = p.getContactPoints(human, -1, left_foot, -1)
+  #print("lcont")
+  #print(lcont)
+  feet_contact = np.array([len(rcont) > 0, len(lcont) > 0])
+  return np.clip(np.concatenate([more] + [j] + [feet_contact]), -5, +5)
+
 
 class SmallReactivePolicy(object):
-    def __init__(self, name, ob_space, ac_space):
-        self.name = name
-        with tf.variable_scope(name):
-            batch_of_observations = [None] + list(ob_space.shape)
-            self.ob = tf.placeholder(dtype=tf.float32, shape=batch_of_observations, name="ob")
-            x = self.ob
-
-            width = 256
-            self.dense1_w = tf.get_variable("dense1_w", [x.get_shape()[1], width])
-            self.dense1_b = tf.get_variable("dense1_b", [width])
-            x = tf.nn.relu(tf.matmul(x, self.dense1_w) + self.dense1_b)
-
-            width = 128
-            self.dense2_w = tf.get_variable("dense2_w", [x.get_shape()[1], width])
-            self.dense2_b = tf.get_variable("dense2_b", [width])
-            x = tf.nn.relu(tf.matmul(x, self.dense2_w) + self.dense2_b)
-
-            assert(len(ac_space.shape)==1)
-            width = ac_space.shape[0]
-            self.final_w = tf.get_variable("final_w", [x.get_shape()[1], width])
-            self.final_b = tf.get_variable("final_b", [width])
-            x = tf.matmul(x, self.final_w) + self.final_b
-            self.action_node = x
-
-            self.all_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, tf.get_variable_scope().name)
-
-    def act(self, ob):
-        a = tf.get_default_session().run(self.action_node, feed_dict={ self.ob: ob[None] })
-        return a[0]
-
-    def load_weights(self):
-        assigns = [
-            (self.dense1_w, weights_dense1_w),
-            (self.dense1_b, weights_dense1_b),
-            (self.dense2_w, weights_dense2_w),
-            (self.dense2_b, weights_dense2_b),
-            (self.final_w, weights_final_w),
-            (self.final_b, weights_final_b)]
-        to_run = []
-        feed_dict = {}
-        for var, w in assigns:
-            ph = tf.placeholder(tf.float32, w.shape)
-            to_run.append( tf.assign(var, ph) )
-            feed_dict[ph] = w
-        tf.get_default_session().run(to_run, feed_dict=feed_dict)
+
+  def __init__(self, name, ob_space, ac_space):
+    self.name = name
+    with tf.variable_scope(name):
+      batch_of_observations = [None] + list(ob_space.shape)
+      self.ob = tf.placeholder(dtype=tf.float32, shape=batch_of_observations, name="ob")
+      x = self.ob
+
+      width = 256
+      self.dense1_w = tf.get_variable("dense1_w", [x.get_shape()[1], width])
+      self.dense1_b = tf.get_variable("dense1_b", [width])
+      x = tf.nn.relu(tf.matmul(x, self.dense1_w) + self.dense1_b)
+
+      width = 128
+      self.dense2_w = tf.get_variable("dense2_w", [x.get_shape()[1], width])
+      self.dense2_b = tf.get_variable("dense2_b", [width])
+      x = tf.nn.relu(tf.matmul(x, self.dense2_w) + self.dense2_b)
+
+      assert (len(ac_space.shape) == 1)
+      width = ac_space.shape[0]
+      self.final_w = tf.get_variable("final_w", [x.get_shape()[1], width])
+      self.final_b = tf.get_variable("final_b", [width])
+      x = tf.matmul(x, self.final_w) + self.final_b
+      self.action_node = x
+
+      self.all_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
+                                             tf.get_variable_scope().name)
+
+  def act(self, ob):
+    a = tf.get_default_session().run(self.action_node, feed_dict={self.ob: ob[None]})
+    return a[0]
+
+  def load_weights(self):
+    assigns = [(self.dense1_w, weights_dense1_w), (self.dense1_b, weights_dense1_b),
+               (self.dense2_w, weights_dense2_w), (self.dense2_b, weights_dense2_b),
+               (self.final_w, weights_final_w), (self.final_b, weights_final_b)]
+    to_run = []
+    feed_dict = {}
+    for var, w in assigns:
+      ph = tf.placeholder(tf.float32, w.shape)
+      to_run.append(tf.assign(var, ph))
+      feed_dict[ph] = w
+    tf.get_default_session().run(to_run, feed_dict=feed_dict)
+
 
 def demo_run():
-	sess = tf.InteractiveSession(config=tf.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1, device_count={ "GPU": 0 }))
-	pi = SmallReactivePolicy("pi", np.zeros((44,)), np.zeros((17,)))
-	pi.load_weights()
-	t1 = time.time()
-	timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "humanoidTimings.json")
-
-	frame = 0
-	while 1:
-		obs = collect_observations(human)
-	
-		actions = pi.act(obs)
-	
-		#print(" ".join(["%+0.2f"%x for x in obs]))
-		#print("Motors")
-		#print(motors)
-
-		#for m in range(len(motors)):
-			#print("motor_power")
-			#print(motor_power[m])
-			#print("actions[m]")
-			#print(actions[m])
-		#p.setJointMotorControl2(human, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082)
-		#p.setJointMotorControl2(human1, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082)
-			
-		forces = [0.] * len(motors)
-		for m in range(len(motors)):
-			forces[m] = motor_power[m]*actions[m]*0.082
-		p.setJointMotorControlArray(human, motors,controlMode=p.TORQUE_CONTROL, forces=forces)
-
-		p.stepSimulation()
-		time.sleep(0.01)
-		distance=5
-		yaw = 0
-		humanPos, humanOrn = p.getBasePositionAndOrientation(human)
-		#p.resetDebugVisualizerCamera(distance,yaw,20,humanPos);
-		frame += 1
-		
-		if frame==1000: break
-	t2 = time.time()
-	print("############################### distance = %0.2f meters" % dummy.distance)
-	print("############################### FPS = ", 1000/ (t2-t1))
-	#print("Starting benchmark")
-	#logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS,"pybullet_humanoid_timings.json")
-	#p.stopStateLogging(logId)
-	print("ended benchmark")
-	print(frame)
-	p.stopStateLogging(timinglog)
-
-
-weights_dense1_w = np.array([
-[ -0.3857, -0.7450, +0.1473, +0.1996, +0.0570, +0.4522, +0.1172, +0.7558, -0.2530, -0.7948, +0.3120, +0.6216, +0.1044, -0.4347, +0.0921, +0.0187, +0.2030, +0.2042, -0.2928, +0.2717, +0.5117, +0.1115, -0.0278, -0.0675, -0.2967, +0.3128, -0.1434, -0.0476, +0.0561, -0.1874, +0.1675, -0.1028, +0.0712, +0.8653, -0.5565, -0.1207, +0.0982, -0.2875, -0.0755, -0.3057, -0.0841, +0.5336, +0.2068, -0.0132, -0.2131, -0.5910, +0.1329, +0.2789, +0.0036, +0.0048, +0.2655, +0.3585, +0.2861, -0.2288, -0.0646, +0.0569, -0.4701, -0.0116, +0.4616, +0.0947, -0.1013, -0.0256, +0.2854, +0.3382, +0.0822, +0.1160, -0.4913, +0.3991, +0.0121, -0.3392, +0.2515, +0.7188, +0.3412, +0.1247, +0.1422, +0.0420, +0.1401, +0.5830, +0.0226, +0.6080, +0.3019, -0.7696, -0.0580, +0.2104, +0.3786, +0.2968, -0.3726, +0.2792, +0.3572, -0.4362, +0.5368, +0.0068, +0.4366, -0.4799, +0.2688, +0.1115, +0.0251, +0.4107, -0.0358, +0.0198, +0.2563, +0.0315, -0.1143, +0.6191, +0.1694, +0.4175, +0.1873, +0.0678, +0.1324, +0.3038, +0.0610, +0.2491, -0.2730, +0.2933, +0.1704, +0.1746, +0.1444, -0.2578, +0.3743, +0.3837, +0.3294, +0.0433, +0.5242, -0.3465, +0.1618, -0.5255, -0.1703, +0.0420, +0.5505, +0.4046, +0.1596, +0.3973, -0.5158, -0.2922, +0.3183, -0.0244, +0.3496, +0.4069, -0.1961, +0.2705, -0.1008, -0.4008, -0.1443, -0.2113, +0.2064, -0.3466, +0.2565, +0.0279, +0.5785, -0.2918, +0.7262, +0.0285, -0.1779, +0.2811, -0.2066, +0.5471, -0.7668, +0.1909, -0.5684, -0.0002, +0.2291, -0.0631, +0.0823, +0.5520, -0.3267, +0.0143, +0.4295, +0.2594, +0.3523, -0.1266, +0.4412, -0.2685, +0.0863, +0.1779, +0.1280, +0.5087, +0.0809, +0.1118, -0.5754, -0.3300, -0.0032, +0.7060, -0.5172, -0.6553, -0.0951, -0.0129, +0.1382, -0.0504, -0.2634, +0.2659, -0.1932, -0.3762, +0.3072, +0.1612, +0.2942, -0.1438, -0.0850, +0.1877, +0.4726, +0.2751, -0.0787, +0.3128, -0.1351, +0.2552, -0.4833, -0.0322, +0.3641, +0.3715, -0.1938, +0.5040, -0.0598, +0.0341, +0.6252, -0.2977, +0.2694, +0.0525, +0.6295, -0.1007, -0.0406, +0.1028, +0.5181, +0.2201, +0.2269, +0.3724, -0.1985, -0.1614, +0.0093, +0.2851, +0.0191, +0.0620, +0.1989, +0.5905, +0.5589, +0.1896, +0.3859, +0.1207, -0.0323, +0.3912, +0.2950, +0.3255, +0.2765, -0.3384, +0.4286, +0.2692, +0.2889, +0.5955, +0.0918, -0.3228, +0.3828, -0.0196, -0.0484, +0.3256, -0.1361, +0.5124, -0.1782, +0.2984],
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-])
+  sess = tf.InteractiveSession(config=tf.ConfigProto(
+      inter_op_parallelism_threads=1, intra_op_parallelism_threads=1, device_count={"GPU": 0}))
+  pi = SmallReactivePolicy("pi", np.zeros((44,)), np.zeros((17,)))
+  pi.load_weights()
+  t1 = time.time()
+  timinglog = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS, "humanoidTimings.json")
+
+  frame = 0
+  while 1:
+    obs = collect_observations(human)
+
+    actions = pi.act(obs)
+
+    #print(" ".join(["%+0.2f"%x for x in obs]))
+    #print("Motors")
+    #print(motors)
+
+    #for m in range(len(motors)):
+    #print("motor_power")
+    #print(motor_power[m])
+    #print("actions[m]")
+    #print(actions[m])
+    #p.setJointMotorControl2(human, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082)
+    #p.setJointMotorControl2(human1, motors[m], controlMode=p.TORQUE_CONTROL, force=motor_power[m]*actions[m]*0.082)
 
-weights_dense1_b = np.array([ -0.0562, -0.1282, -0.3753, -0.1119, -0.0136, -0.2313, -0.0345, +0.0894, -0.0010, -0.0601, -0.0762, -0.1205, -0.1190, +0.0179, -0.0906, -0.0572, +0.0797, -0.2050, -0.0318, -0.2306, -0.0182, -0.0847, +0.0607, -0.1987, -0.1262, -0.1896, -0.0226, -0.0034, -0.0809, +0.0404, +0.0756, -0.1168, -0.0733, -0.0739, +0.0161, -0.0528, +0.0067, +0.0332, -0.2382, -0.0745, -0.0184, -0.0287, +0.1160, -0.0248, -0.1523, -0.0589, -0.3165, -0.0986, -0.0604, -0.0633, +0.0549, -0.0209, -0.0474, -0.2358, -0.0890, -0.1201, -0.0595, +0.1562, -0.1967, -0.0995, -0.1231, +0.0500, +0.0962, -0.1140, +0.0393, -0.1390, -0.1273, -0.0866, +0.0868, -0.0849, -0.1148, -0.2904, +0.0706, -0.1821, -0.1143, -0.2196, -0.0112, -0.0375, -0.1567, -0.0589, +0.1257, +0.1125, -0.1020, +0.0818, -0.2838, -0.1653, -0.1676, -0.0244, -0.0784, -0.0181, -0.0429, -0.0966, +0.0494, +0.0093, -0.0858, -0.0919, -0.1196, -0.2181, +0.0292, -0.2283, -0.2595, -0.0925, -0.0134, -0.0093, +0.0310, +0.0078, -0.0172, -0.0803, -0.0386, +0.1367, -0.1948, -0.0290, +0.0785, -0.0464, +0.0371, -0.0128, -0.0377, -0.0724, +0.1637, -0.1972, -0.1093, -0.1634, -0.0691, -0.0834, -0.2310, -0.2676, -0.0683, -0.0024, -0.1401, -0.0378, -0.1633, -0.1665, -0.1098, -0.0746, -0.0684, -0.1914, -0.1284, -0.0998, +0.0577, -0.0638, -0.0885, -0.0445, -0.0384, -0.1012, +0.0067, -0.2128, -0.1312, +0.0526, +0.1238, +0.0742, +0.0261, +0.0444, +0.0614, +0.1223, +0.1232, -0.1827, -0.1289, -0.0469, -0.1420, +0.0705, -0.2220, -0.2401, -0.1121, -0.1697, -0.1621, +0.0688, +0.0688, -0.0040, -0.0048, -0.0416, -0.1265, +0.0311, +0.0280, +0.0287, -0.1893, -0.0966, -0.0876, -0.1778, +0.0000, +0.0074, -0.0978, -0.1932, -0.0790, +0.0037, -0.1398, -0.1006, -0.0532, -0.1227, +0.1148, -0.1254, -0.1134, +0.1205, -0.1588, +0.1093, -0.2470, -0.0915, -0.1715, -0.0036, -0.2943, +0.1375, +0.1075, -0.0339, -0.0491, +0.0060, +0.0145, -0.1867, +0.0646, -0.1327, -0.0529, -0.0830, +0.1433, -0.1924, -0.0285, -0.2420, -0.2244, -0.1451, -0.1009, -0.1149, +0.0846, +0.0473, +0.1568, -0.0062, -0.1289, -0.2085, +0.0600, -0.1314, -0.1569, +0.2043, -0.0302, -0.1666, +0.0473, -0.0075, -0.2825, -0.0696, -0.2223, -0.2911, -0.0567, -0.0510, +0.0184, -0.2132, -0.0371, +0.1715, -0.1301, -0.0310, +0.1523, -0.0142, +0.0185, -0.0030, -0.0725, -0.1011, -0.0923, -0.1991, -0.1686, -0.1198, -0.2226, -0.1094])
-
-weights_dense2_w = np.array([
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+    forces = [0.] * len(motors)
+    for m in range(len(motors)):
+      forces[m] = motor_power[m] * actions[m] * 0.082
+    p.setJointMotorControlArray(human, motors, controlMode=p.TORQUE_CONTROL, forces=forces)
+
+    p.stepSimulation()
+    time.sleep(0.01)
+    distance = 5
+    yaw = 0
+    humanPos, humanOrn = p.getBasePositionAndOrientation(human)
+    #p.resetDebugVisualizerCamera(distance,yaw,20,humanPos);
+    frame += 1
+
+    if frame == 1000: break
+  t2 = time.time()
+  print("############################### distance = %0.2f meters" % dummy.distance)
+  print("############################### FPS = ", 1000 / (t2 - t1))
+  #print("Starting benchmark")
+  #logId = p.startStateLogging(p.STATE_LOGGING_PROFILE_TIMINGS,"pybullet_humanoid_timings.json")
+  #p.stopStateLogging(logId)
+  print("ended benchmark")
+  print(frame)
+  p.stopStateLogging(timinglog)
+
+
+# yapf: disable
+weights_dense1_w = np.array(
+    [[
+        -0.3857, -0.7450, +0.1473, +0.1996, +0.0570, +0.4522, +0.1172, +0.7558,
+        -0.2530, -0.7948, +0.3120, +0.6216, +0.1044, -0.4347, +0.0921, +0.0187,
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-
-weights_final_w = np.array([
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+         -0.4070, +0.0242
+     ]])
+
+weights_dense2_b = np.array([
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+    -0.1559, -0.1759, +0.0344, +0.3008, +0.3244, +0.3597, +0.1691, +0.0277,
+    +0.0064, +0.2372, +0.0961, +0.3293, +0.2077, +0.1230, +0.1195, +0.4294,
+    +0.2816, +0.1169, +0.4083, +0.0309, +0.0433, +0.4852, +0.1903, +0.1055,
+    +0.2894, +0.0468, +0.0669, +0.1655, -0.0712, +0.2791, +0.3653, +0.1661,
+    +0.0730, +0.4481, +0.2336, +0.4547, +0.3048, +0.3674, +0.1072, +0.2519,
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+    +0.2301, +0.3551, +0.1974, +0.5505, +0.1776, +0.0702, -0.0460, +0.1697,
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+    +0.2252, +0.1527, +0.3315, +0.1419, +0.3483, +0.3999, +0.2818, +0.1021,
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+    +0.0219, -0.0728, +0.2957, +0.0854, +0.0055, -0.0289, +0.0866, +0.0203,
+    +0.0353, +0.2069, +0.3297, +0.1171, +0.0607, +0.1667, +0.5721, -0.0218
 ])
 
-weights_final_b = np.array([ -0.0274, +0.1314, -0.0578, +0.2965, +0.1318, -0.0622, +0.1158, +0.0643, +0.2138, -0.1422, +0.1579, +0.0836, -0.0388, -0.0933, +0.2233, -0.2276, +0.0375])
+weights_final_w = np.array(
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+        -0.0525, -0.1167, +0.0605, +0.1389, -0.0801, -0.2973, +0.1066, +0.0129,
+        +0.0549
+    ],
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+         +0.0614, -0.1857, +0.0101
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+         +0.5889, +0.1608, +0.3169
+     ],
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+         +0.0046, -0.1664, -0.1933
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+         +0.2743, +0.0971, -0.1144
+     ],
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+         -0.4098, +0.2590, -0.0564
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+         +0.3433, +0.1957, +0.2411
+     ],
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+         +0.0111, +0.3729, -0.3116
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+         +0.1208, -0.3628, +0.2056
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+         +0.0053, -0.0098, -0.2020
+     ],
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+         +0.2374, +0.2102, +0.0998
+     ],
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+     ],
+     [
+         -0.1779, +0.3660, +0.2386, +0.2297, +0.1792, -0.1369, +0.2320,
+         -0.5867, -0.1306, +0.3471, -0.1127, +0.3352, -0.0214, +0.0801,
+         -0.1368, -0.7262, +0.1757
+     ],
+     [
+         +0.0183, -0.0485, +0.1199, +0.2513, +0.2022, -0.0472, +0.3076,
+         +0.0656, -0.3302, -0.0587, -0.0200, -0.5474, -0.0863, +0.2087,
+         -0.2581, -0.1750, +0.3391
+     ],
+     [
+         -0.2339, +0.0639, +0.2085, -0.1872, -0.2686, +0.1490, +0.0436,
+         -0.0841, -0.1054, -0.4899, +0.0193, -0.0250, +0.2212, +0.0972,
+         +0.0329, +0.1328, -0.0545
+     ],
+     [
+         -0.0874, -0.1087, -0.1577, +0.0936, -0.3083, -0.1450, +0.0365,
+         -0.1924, -0.0536, +0.0475, +0.0348, +0.0772, +0.0348, -0.2800,
+         +0.0864, +0.2640, -0.0215
+     ],
+     [
+         +0.0745, +0.4503, -0.4064, +0.6300, -0.1275, -0.0463, -0.4074,
+         +0.0031, +0.1408, +0.0531, +0.1400, +0.0308, -0.0220, -0.0014,
+         -0.3056, -0.1551, -0.0096
+     ],
+     [
+         +0.1479, +0.1186, +0.1323, -0.3466, -0.0654, -0.1084, -0.2509,
+         +0.0944, -0.2135, +0.2020, +0.0602, -0.1239, +0.0741, +0.2037,
+         -0.4462, +0.1065, +0.1710
+     ]])
+
+weights_final_b = np.array([
+    -0.0274, +0.1314, -0.0578, +0.2965, +0.1318, -0.0622, +0.1158, +0.0643,
+    +0.2138, -0.1422, +0.1579, +0.0836, -0.0388, -0.0933, +0.2233, -0.2276,
+    +0.0375
+])
+# yapf: enable
 
-if __name__=="__main__":
-    demo_run()
+if __name__ == "__main__":
+  demo_run()
diff --git a/examples/pybullet/unittests/saveRestoreStateTest.py b/examples/pybullet/unittests/saveRestoreStateTest.py
index da3480904..bd4b1d9d1 100644
--- a/examples/pybullet/unittests/saveRestoreStateTest.py
+++ b/examples/pybullet/unittests/saveRestoreStateTest.py
@@ -1,144 +1,137 @@
 import unittest
 import pybullet as p
 import math, time
-import difflib,sys
+import difflib, sys
 
 from utils import allclose, dot
 
+
 class TestPybulletSaveRestoreMethods(unittest.TestCase):
 
-	def setupWorld(self):
-		numObjects = 50
-		
-		
-		maximalCoordinates = False
-
-		p.resetSimulation()
-		p.setPhysicsEngineParameter(deterministicOverlappingPairs=1)
-		p.loadURDF("planeMesh.urdf",useMaximalCoordinates=maximalCoordinates)
-		kukaId = p.loadURDF("kuka_iiwa/model_free_base.urdf",[0,0,10], useMaximalCoordinates=maximalCoordinates)
-		for i in range (p.getNumJoints(kukaId)):
-			p.setJointMotorControl2(kukaId,i,p.POSITION_CONTROL,force=0)
-		for i in range (numObjects):
-			cube = p.loadURDF("cube_small.urdf",[0,i*0.02,(i+1)*0.2])
-			#p.changeDynamics(cube,-1,mass=100)
-		p.stepSimulation()
-		p.setGravity(0,0,-10)
-
-
-	def dumpStateToFile(self, file):
-		for i in  range (p.getNumBodies()):
-			pos,orn = p.getBasePositionAndOrientation(i)
-			linVel,angVel = p.getBaseVelocity(i)
-			txtPos = "pos="+str(pos)+"\n"
-			txtOrn = "orn="+str(orn)+"\n"
-			txtLinVel = "linVel"+str(linVel)+"\n"
-			txtAngVel = "angVel"+str(angVel)+"\n"
-			file.write(txtPos)
-			file.write(txtOrn)
-			file.write(txtLinVel)
-			file.write(txtAngVel)
-		
-	def compareFiles(self, file1,file2):
-		diff = difflib.unified_diff(
-	            file1.readlines(),
-	            file2.readlines(),
-	            fromfile='saveFile.txt',
-	            tofile='restoreFile.txt',
-	        )
-		numDifferences = 0
-		for line in diff:
-			numDifferences = numDifferences+1
-			sys.stdout.write(line)
-		self.assertEqual(numDifferences,0)
-		
-
-	def testSaveRestoreState(self):
-		numSteps = 500
-		numSteps2 = 30
-
-		
-		verbose = 0
-		self.setupWorld()
-		
-
-		for i in range (numSteps):
-			p.stepSimulation()	
-		p.saveBullet("state.bullet")
-		if verbose:
-			p.setInternalSimFlags(1)
-		p.stepSimulation()
-		if verbose:
-			p.setInternalSimFlags(0)
-			print("contact points=")
-			for q in p.getContactPoints():
-				print(q)
-
-		for i in range (numSteps2):
-			p.stepSimulation()
-
-
-		file = open("saveFile.txt","w") 
-		self.dumpStateToFile(file)
-		file.close() 
-
-		#################################
-		self.setupWorld()
-
-		#both restore from file or from in-memory state should work
-		p.restoreState(fileName="state.bullet")
-		stateId = p.saveState()
-
-		if verbose:
-			p.setInternalSimFlags(1)
-		p.stepSimulation()
-		if verbose:
-			p.setInternalSimFlags(0)
-			print("contact points restored=")
-			for q in p.getContactPoints():
-				print(q)
-		for i in range (numSteps2):
-			p.stepSimulation()
-
-			
-		file = open("restoreFile.txt","w")
-		self.dumpStateToFile(file)
-		file.close() 
-
-		p.restoreState(stateId)
-		if verbose:
-			p.setInternalSimFlags(1)
-		p.stepSimulation()
-		if verbose:
-			p.setInternalSimFlags(0)
-			print("contact points restored=")
-			for q in p.getContactPoints():
-				print(q)
-		for i in range (numSteps2):
-			p.stepSimulation()
-
-			
-		file = open("restoreFile2.txt","w")
-		self.dumpStateToFile(file)
-		file.close() 
-
-		file1 = open("saveFile.txt","r") 
-		file2 = open("restoreFile.txt","r") 
-		self.compareFiles(file1,file2)
-		file1.close()
-		file2.close()
-
-		file1 = open("saveFile.txt","r") 
-		file2 = open("restoreFile2.txt","r") 
-		self.compareFiles(file1,file2)
-		file1.close()
-		file2.close()        
-
-		
+  def setupWorld(self):
+    numObjects = 50
+
+    maximalCoordinates = False
+
+    p.resetSimulation()
+    p.setPhysicsEngineParameter(deterministicOverlappingPairs=1)
+    p.loadURDF("planeMesh.urdf", useMaximalCoordinates=maximalCoordinates)
+    kukaId = p.loadURDF("kuka_iiwa/model_free_base.urdf", [0, 0, 10],
+                        useMaximalCoordinates=maximalCoordinates)
+    for i in range(p.getNumJoints(kukaId)):
+      p.setJointMotorControl2(kukaId, i, p.POSITION_CONTROL, force=0)
+    for i in range(numObjects):
+      cube = p.loadURDF("cube_small.urdf", [0, i * 0.02, (i + 1) * 0.2])
+      #p.changeDynamics(cube,-1,mass=100)
+    p.stepSimulation()
+    p.setGravity(0, 0, -10)
+
+  def dumpStateToFile(self, file):
+    for i in range(p.getNumBodies()):
+      pos, orn = p.getBasePositionAndOrientation(i)
+      linVel, angVel = p.getBaseVelocity(i)
+      txtPos = "pos=" + str(pos) + "\n"
+      txtOrn = "orn=" + str(orn) + "\n"
+      txtLinVel = "linVel" + str(linVel) + "\n"
+      txtAngVel = "angVel" + str(angVel) + "\n"
+      file.write(txtPos)
+      file.write(txtOrn)
+      file.write(txtLinVel)
+      file.write(txtAngVel)
+
+  def compareFiles(self, file1, file2):
+    diff = difflib.unified_diff(
+        file1.readlines(),
+        file2.readlines(),
+        fromfile='saveFile.txt',
+        tofile='restoreFile.txt',
+    )
+    numDifferences = 0
+    for line in diff:
+      numDifferences = numDifferences + 1
+      sys.stdout.write(line)
+    self.assertEqual(numDifferences, 0)
+
+  def testSaveRestoreState(self):
+    numSteps = 500
+    numSteps2 = 30
+
+    verbose = 0
+    self.setupWorld()
+
+    for i in range(numSteps):
+      p.stepSimulation()
+    p.saveBullet("state.bullet")
+    if verbose:
+      p.setInternalSimFlags(1)
+    p.stepSimulation()
+    if verbose:
+      p.setInternalSimFlags(0)
+      print("contact points=")
+      for q in p.getContactPoints():
+        print(q)
+
+    for i in range(numSteps2):
+      p.stepSimulation()
+
+    file = open("saveFile.txt", "w")
+    self.dumpStateToFile(file)
+    file.close()
+
+    #################################
+    self.setupWorld()
+
+    #both restore from file or from in-memory state should work
+    p.restoreState(fileName="state.bullet")
+    stateId = p.saveState()
+
+    if verbose:
+      p.setInternalSimFlags(1)
+    p.stepSimulation()
+    if verbose:
+      p.setInternalSimFlags(0)
+      print("contact points restored=")
+      for q in p.getContactPoints():
+        print(q)
+    for i in range(numSteps2):
+      p.stepSimulation()
+
+    file = open("restoreFile.txt", "w")
+    self.dumpStateToFile(file)
+    file.close()
+
+    p.restoreState(stateId)
+    if verbose:
+      p.setInternalSimFlags(1)
+    p.stepSimulation()
+    if verbose:
+      p.setInternalSimFlags(0)
+      print("contact points restored=")
+      for q in p.getContactPoints():
+        print(q)
+    for i in range(numSteps2):
+      p.stepSimulation()
+
+    file = open("restoreFile2.txt", "w")
+    self.dumpStateToFile(file)
+    file.close()
+
+    file1 = open("saveFile.txt", "r")
+    file2 = open("restoreFile.txt", "r")
+    self.compareFiles(file1, file2)
+    file1.close()
+    file2.close()
+
+    file1 = open("saveFile.txt", "r")
+    file2 = open("restoreFile2.txt", "r")
+    self.compareFiles(file1, file2)
+    file1.close()
+    file2.close()
+
 
 #while (p.getConnectionInfo()["isConnected"]):
 #	time.sleep(1)
 
 if __name__ == '__main__':
-	p.connect(p.DIRECT)
-	unittest.main()
+  p.connect(p.DIRECT)
+  unittest.main()
diff --git a/examples/pybullet/unittests/unittests.py b/examples/pybullet/unittests/unittests.py
index 469234998..74dd423d4 100644
--- a/examples/pybullet/unittests/unittests.py
+++ b/examples/pybullet/unittests/unittests.py
@@ -4,113 +4,120 @@ import time
 
 from utils import allclose, dot
 
+
 class TestPybulletMethods(unittest.TestCase):
 
-    def test_import(self):
-        import pybullet as p
-        self.assertGreater(p.getAPIVersion(), 201700000)
-
-    def test_connect_direct(self):
-        import pybullet as p
-        cid = p.connect(p.DIRECT)
-        self.assertEqual(cid,0)
-        p.disconnect()
-
-    def test_loadurdf(self):
-        import pybullet as p
-        p.connect(p.DIRECT)
-        ob = p.loadURDF("r2d2.urdf")
-        self.assertEqual(ob,0)
-        p.disconnect()
-
-    def test_rolling_friction(self):
-        import pybullet as p
-        p.connect(p.DIRECT)
-        p.loadURDF("plane.urdf")
-        sphere = p.loadURDF("sphere2.urdf",[0,0,1])
-        p.resetBaseVelocity(sphere,linearVelocity=[1,0,0])
-        p.changeDynamics(sphere,-1,linearDamping=0,angularDamping=0)
-        #p.changeDynamics(sphere,-1,rollingFriction=0)
-        p.setGravity(0,0,-10)
-        for i in range (1000):
-          p.stepSimulation()
-        vel = p.getBaseVelocity(sphere)
-        self.assertLess(vel[0][0],1e-10)
-        self.assertLess(vel[0][1],1e-10)
-        self.assertLess(vel[0][2],1e-10)
-        self.assertLess(vel[1][0],1e-10)
-        self.assertLess(vel[1][1],1e-10)
-        self.assertLess(vel[1][2],1e-10)
-        p.disconnect()
+  def test_import(self):
+    import pybullet as p
+    self.assertGreater(p.getAPIVersion(), 201700000)
+
+  def test_connect_direct(self):
+    import pybullet as p
+    cid = p.connect(p.DIRECT)
+    self.assertEqual(cid, 0)
+    p.disconnect()
+
+  def test_loadurdf(self):
+    import pybullet as p
+    p.connect(p.DIRECT)
+    ob = p.loadURDF("r2d2.urdf")
+    self.assertEqual(ob, 0)
+    p.disconnect()
+
+  def test_rolling_friction(self):
+    import pybullet as p
+    p.connect(p.DIRECT)
+    p.loadURDF("plane.urdf")
+    sphere = p.loadURDF("sphere2.urdf", [0, 0, 1])
+    p.resetBaseVelocity(sphere, linearVelocity=[1, 0, 0])
+    p.changeDynamics(sphere, -1, linearDamping=0, angularDamping=0)
+    #p.changeDynamics(sphere,-1,rollingFriction=0)
+    p.setGravity(0, 0, -10)
+    for i in range(1000):
+      p.stepSimulation()
+    vel = p.getBaseVelocity(sphere)
+    self.assertLess(vel[0][0], 1e-10)
+    self.assertLess(vel[0][1], 1e-10)
+    self.assertLess(vel[0][2], 1e-10)
+    self.assertLess(vel[1][0], 1e-10)
+    self.assertLess(vel[1][1], 1e-10)
+    self.assertLess(vel[1][2], 1e-10)
+    p.disconnect()
+
 
 class TestPybulletJacobian(unittest.TestCase):
 
-    def getMotorJointStates(self, robot):
-        import pybullet as p
-        joint_states = p.getJointStates(robot, range(p.getNumJoints(robot)))
-        joint_infos = [p.getJointInfo(robot, i) for i in range(p.getNumJoints(robot))]
-        joint_states = [j for j, i in zip(joint_states, joint_infos) if i[3] > -1]
-        joint_positions = [state[0] for state in joint_states]
-        joint_velocities = [state[1] for state in joint_states]
-        joint_torques = [state[3] for state in joint_states]
-        return joint_positions, joint_velocities, joint_torques
-
-    def setJointPosition(self, robot, position, kp=1.0, kv=0.3):
-        import pybullet as p
-        num_joints = p.getNumJoints(robot)
-        zero_vec = [0.0] * num_joints
-        if len(position) == num_joints:
-            p.setJointMotorControlArray(robot, range(num_joints), p.POSITION_CONTROL,
-                targetPositions=position, targetVelocities=zero_vec,
-                positionGains=[kp] * num_joints, velocityGains=[kv] * num_joints)
-
-    def testJacobian(self):
-        import pybullet as p
-
-        clid = p.connect(p.SHARED_MEMORY)
-        if (clid<0):
-            p.connect(p.DIRECT)
-
-        time_step = 0.001
-        gravity_constant = -9.81
-
-        urdfs = ["TwoJointRobot_w_fixedJoints.urdf",
-                 "TwoJointRobot_w_fixedJoints.urdf",
-                 "kuka_iiwa/model.urdf",
-                 "kuka_lwr/kuka.urdf"]
-        for urdf in urdfs:
-            p.resetSimulation()
-            p.setTimeStep(time_step)
-            p.setGravity(0.0, 0.0, gravity_constant)
-
-            robotId = p.loadURDF(urdf, useFixedBase=True)
-            p.resetBasePositionAndOrientation(robotId,[0,0,0],[0,0,0,1])
-            numJoints = p.getNumJoints(robotId)
-            endEffectorIndex = numJoints - 1
-
-            # Set a joint target for the position control and step the sim.
-            self.setJointPosition(robotId, [0.1 * (i % 3)
-                                            for i in range(numJoints)])
-            p.stepSimulation()
-
-            # Get the joint and link state directly from Bullet.
-            mpos, mvel, mtorq = self.getMotorJointStates(robotId)
-
-            result = p.getLinkState(robotId, endEffectorIndex,
-                    computeLinkVelocity=1, computeForwardKinematics=1)
-            link_trn, link_rot, com_trn, com_rot, frame_pos, frame_rot, link_vt, link_vr = result
-            # Get the Jacobians for the CoM of the end-effector link.
-            # Note that in this example com_rot = identity, and we would need to use com_rot.T * com_trn.
-            # The localPosition is always defined in terms of the link frame coordinates.
-
-            zero_vec = [0.0] * len(mpos)
-            jac_t, jac_r = p.calculateJacobian(robotId, endEffectorIndex,
-                    com_trn, mpos, zero_vec, zero_vec)
-
-            assert(allclose(dot(jac_t, mvel), link_vt))
-            assert(allclose(dot(jac_r, mvel), link_vr))
-        p.disconnect()
+  def getMotorJointStates(self, robot):
+    import pybullet as p
+    joint_states = p.getJointStates(robot, range(p.getNumJoints(robot)))
+    joint_infos = [p.getJointInfo(robot, i) for i in range(p.getNumJoints(robot))]
+    joint_states = [j for j, i in zip(joint_states, joint_infos) if i[3] > -1]
+    joint_positions = [state[0] for state in joint_states]
+    joint_velocities = [state[1] for state in joint_states]
+    joint_torques = [state[3] for state in joint_states]
+    return joint_positions, joint_velocities, joint_torques
+
+  def setJointPosition(self, robot, position, kp=1.0, kv=0.3):
+    import pybullet as p
+    num_joints = p.getNumJoints(robot)
+    zero_vec = [0.0] * num_joints
+    if len(position) == num_joints:
+      p.setJointMotorControlArray(robot,
+                                  range(num_joints),
+                                  p.POSITION_CONTROL,
+                                  targetPositions=position,
+                                  targetVelocities=zero_vec,
+                                  positionGains=[kp] * num_joints,
+                                  velocityGains=[kv] * num_joints)
+
+  def testJacobian(self):
+    import pybullet as p
+
+    clid = p.connect(p.SHARED_MEMORY)
+    if (clid < 0):
+      p.connect(p.DIRECT)
+
+    time_step = 0.001
+    gravity_constant = -9.81
+
+    urdfs = [
+        "TwoJointRobot_w_fixedJoints.urdf", "TwoJointRobot_w_fixedJoints.urdf",
+        "kuka_iiwa/model.urdf", "kuka_lwr/kuka.urdf"
+    ]
+    for urdf in urdfs:
+      p.resetSimulation()
+      p.setTimeStep(time_step)
+      p.setGravity(0.0, 0.0, gravity_constant)
+
+      robotId = p.loadURDF(urdf, useFixedBase=True)
+      p.resetBasePositionAndOrientation(robotId, [0, 0, 0], [0, 0, 0, 1])
+      numJoints = p.getNumJoints(robotId)
+      endEffectorIndex = numJoints - 1
+
+      # Set a joint target for the position control and step the sim.
+      self.setJointPosition(robotId, [0.1 * (i % 3) for i in range(numJoints)])
+      p.stepSimulation()
+
+      # Get the joint and link state directly from Bullet.
+      mpos, mvel, mtorq = self.getMotorJointStates(robotId)
+
+      result = p.getLinkState(robotId,
+                              endEffectorIndex,
+                              computeLinkVelocity=1,
+                              computeForwardKinematics=1)
+      link_trn, link_rot, com_trn, com_rot, frame_pos, frame_rot, link_vt, link_vr = result
+      # Get the Jacobians for the CoM of the end-effector link.
+      # Note that in this example com_rot = identity, and we would need to use com_rot.T * com_trn.
+      # The localPosition is always defined in terms of the link frame coordinates.
+
+      zero_vec = [0.0] * len(mpos)
+      jac_t, jac_r = p.calculateJacobian(robotId, endEffectorIndex, com_trn, mpos, zero_vec,
+                                         zero_vec)
+
+      assert (allclose(dot(jac_t, mvel), link_vt))
+      assert (allclose(dot(jac_r, mvel), link_vr))
+    p.disconnect()
 
-if __name__ == '__main__':
-    unittest.main()
 
+if __name__ == '__main__':
+  unittest.main()
diff --git a/examples/pybullet/unittests/userDataTest.py b/examples/pybullet/unittests/userDataTest.py
index e16b56958..1f0d06e8e 100644
--- a/examples/pybullet/unittests/userDataTest.py
+++ b/examples/pybullet/unittests/userDataTest.py
@@ -9,256 +9,256 @@ ROBOT_PATH = "r2d2.urdf"
 
 class TestUserDataMethods(unittest.TestCase):
 
-	@classmethod
-	def setUpClass(cls):
-		TestUserDataMethods.server = bullet_client.BulletClient(connection_mode=pybullet.SHARED_MEMORY_SERVER)
-
-	@classmethod
-	def tearDownClass(cls):
-		del TestUserDataMethods.server
-
-	def setUp(self):
-		self.client = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
-
-	def tearDown(self):
-		self.client.resetSimulation()
-		del self.client
-
-	def testAddUserData(self):
-		plane_id = self.client.loadURDF(PLANE_PATH)
-		uid1 = self.client.addUserData(plane_id, "MyKey1", "MyValue1")
-		uid2 = self.client.addUserData(plane_id, "MyKey2", "MyValue2")
-		uid3 = self.client.addUserData(plane_id, "MyKey3", "MyValue3")
-		uid4 = self.client.addUserData(plane_id, "MyKey4", "MyValue4")
-
-		# Retrieve user data and make sure it's correct.
-		self.assertEqual(b"MyValue1", self.client.getUserData(uid1))
-		self.assertEqual(b"MyValue2", self.client.getUserData(uid2))
-		self.assertEqual(b"MyValue3", self.client.getUserData(uid3))
-		self.assertEqual(b"MyValue4", self.client.getUserData(uid4))
-
-		# Disconnect/reconnect and make sure that the user data is synced back.
-		del self.client
-		self.client = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
-
-		self.assertEqual(b"MyValue1", self.client.getUserData(uid1))
-		self.assertEqual(b"MyValue2", self.client.getUserData(uid2))
-		self.assertEqual(b"MyValue3", self.client.getUserData(uid3))
-		self.assertEqual(b"MyValue4", self.client.getUserData(uid4))
-
-		self.client.resetSimulation()
-		self.assertEqual(None, self.client.getUserData(uid1))
-		self.assertEqual(None, self.client.getUserData(uid2))
-		self.assertEqual(None, self.client.getUserData(uid3))
-		self.assertEqual(None, self.client.getUserData(uid4))
-
-
-	def testGetNumUserData(self):
-		plane_id = self.client.loadURDF(PLANE_PATH)
-		uid1 = self.client.addUserData(plane_id, "MyKey1", "MyValue1")
-		uid2 = self.client.addUserData(plane_id, "MyKey2", "MyValue2")
-		uid3 = self.client.addUserData(plane_id, "MyKey3", "MyValue3")
-		uid4 = self.client.addUserData(plane_id, "MyKey4", "MyValue4")
-
-		self.assertEqual(4, self.client.getNumUserData(plane_id))
-		
-		del self.client
-		self.client = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
-
-		self.assertEqual(4, self.client.getNumUserData(plane_id))
-
-
-	def testReplaceUserData(self):
-		plane_id = self.client.loadURDF(PLANE_PATH)
-		uid = self.client.addUserData(plane_id, "MyKey", "MyValue")
-
-		self.assertEqual(b"MyValue", self.client.getUserData(uid))
-		
-		new_uid = self.client.addUserData(plane_id, "MyKey", "MyNewValue")
-		self.assertEqual(uid, new_uid)
-		self.assertEqual(b"MyNewValue", self.client.getUserData(uid))
-
-		del self.client
-		self.client = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
-
-		self.assertEqual(b"MyNewValue", self.client.getUserData(uid))
-
-	def testGetUserDataId(self):
-		plane_id = self.client.loadURDF(PLANE_PATH)
-		uid1 = self.client.addUserData(plane_id, "MyKey1", "MyValue1")
-		uid2 = self.client.addUserData(plane_id, "MyKey2", "MyValue2")
-		uid3 = self.client.addUserData(plane_id, "MyKey3", "MyValue3")
-		uid4 = self.client.addUserData(plane_id, "MyKey4", "MyValue4")
-
-		self.assertEqual(uid1, self.client.getUserDataId(plane_id, "MyKey1"))
-		self.assertEqual(uid2, self.client.getUserDataId(plane_id, "MyKey2"))
-		self.assertEqual(uid3, self.client.getUserDataId(plane_id, "MyKey3"))
-		self.assertEqual(uid4, self.client.getUserDataId(plane_id, "MyKey4"))
-
-		del self.client
-		self.client = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
-
-		self.assertEqual(uid1, self.client.getUserDataId(plane_id, "MyKey1"))
-		self.assertEqual(uid2, self.client.getUserDataId(plane_id, "MyKey2"))
-		self.assertEqual(uid3, self.client.getUserDataId(plane_id, "MyKey3"))
-		self.assertEqual(uid4, self.client.getUserDataId(plane_id, "MyKey4"))
-
-
-	def testRemoveUserData(self):
-		plane_id = self.client.loadURDF(PLANE_PATH)
-		uid1 = self.client.addUserData(plane_id, "MyKey1", "MyValue1")
-		uid2 = self.client.addUserData(plane_id, "MyKey2", "MyValue2")
-		uid3 = self.client.addUserData(plane_id, "MyKey3", "MyValue3")
-		uid4 = self.client.addUserData(plane_id, "MyKey4", "MyValue4")
-
-		self.client.removeUserData(uid2)
-
-		self.assertEqual(3, self.client.getNumUserData(plane_id))
-		self.assertEqual(-1, self.client.getUserDataId(plane_id, "MyKey2"))
-		self.assertEqual(None, self.client.getUserData(uid2))
-		self.assertEqual(b"MyValue1", self.client.getUserData(uid1))
-		self.assertEqual(b"MyValue3", self.client.getUserData(uid3))
-		self.assertEqual(b"MyValue4", self.client.getUserData(uid4))
-
-		del self.client
-		self.client = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
-
-		self.assertEqual(3, self.client.getNumUserData(plane_id))
-		self.assertEqual(-1, self.client.getUserDataId(plane_id, "MyKey2"))
-		self.assertEqual(None, self.client.getUserData(uid2))
-		self.assertEqual(b"MyValue1", self.client.getUserData(uid1))
-		self.assertEqual(b"MyValue3", self.client.getUserData(uid3))
-		self.assertEqual(b"MyValue4", self.client.getUserData(uid4))
-
-
-	def testIterateAllUserData(self):
-		plane_id = self.client.loadURDF(PLANE_PATH)
-		uid1 = self.client.addUserData(plane_id, "MyKey1", "MyValue1")
-		uid2 = self.client.addUserData(plane_id, "MyKey2", "MyValue2")
-		uid3 = self.client.addUserData(plane_id, "MyKey3", "MyValue3")
-		uid4 = self.client.addUserData(plane_id, "MyKey4", "MyValue4")
-
-		entries = set()
-		for i in range(self.client.getNumUserData(plane_id)):
-			userDataId, key, bodyId, linkIndex, visualShapeIndex = self.client.getUserDataInfo(plane_id, i)
-			value = self.client.getUserData(userDataId);
-			entries.add((userDataId, key, value, bodyId, linkIndex, visualShapeIndex))
-
-		self.assertTrue((uid1, b"MyKey1", b"MyValue1", plane_id, -1, -1) in entries)
-		self.assertTrue((uid2, b"MyKey2", b"MyValue2", plane_id, -1, -1) in entries)
-		self.assertTrue((uid3, b"MyKey3", b"MyValue3", plane_id, -1, -1) in entries)
-		self.assertTrue((uid4, b"MyKey4", b"MyValue4", plane_id, -1, -1) in entries)
-		self.assertEqual(4, len(entries))
-
-
-	def testRemoveBody(self):
-		plane_id = self.client.loadURDF(PLANE_PATH)
-		uid1 = self.client.addUserData(plane_id, "MyKey1", "MyValue1")
-		uid2 = self.client.addUserData(plane_id, "MyKey2", "MyValue2")
-		uid3 = self.client.addUserData(plane_id, "MyKey3", "MyValue3")
-		uid4 = self.client.addUserData(plane_id, "MyKey4", "MyValue4")
-
-		self.client.removeBody(plane_id)
-		self.assertEqual(None, self.client.getUserData(uid1))
-		self.assertEqual(None, self.client.getUserData(uid2))
-		self.assertEqual(None, self.client.getUserData(uid3))
-		self.assertEqual(None, self.client.getUserData(uid4))
-
-		del self.client
-		self.client = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
-
-		self.assertEqual(None, self.client.getUserData(uid1))
-		self.assertEqual(None, self.client.getUserData(uid2))
-		self.assertEqual(None, self.client.getUserData(uid3))
-		self.assertEqual(None, self.client.getUserData(uid4))
-
-
-	def testMultipleBodies(self):
-		plane1 = self.client.loadURDF(PLANE_PATH)
-		plane2 = self.client.loadURDF(PLANE_PATH)
-		
-		uid1 = self.client.addUserData(plane1, "MyKey1", "This is plane 1 - 1")
-		uid2 = self.client.addUserData(plane1, "MyKey2", "This is plane 1 - 2")
-
-		uid3 = self.client.addUserData(plane2, "MyKey1", "This is plane 2 - 1")
-		uid4 = self.client.addUserData(plane2, "MyKey2", "This is plane 2 - 2")
-		uid5 = self.client.addUserData(plane2, "MyKey3", "This is plane 2 - 3")
-
-		self.assertEqual(b"This is plane 1 - 1", self.client.getUserData(self.client.getUserDataId(plane1, "MyKey1")))
-		self.assertEqual(b"This is plane 1 - 2", self.client.getUserData(self.client.getUserDataId(plane1, "MyKey2")))
-
-		self.assertEqual(b"This is plane 2 - 1", self.client.getUserData(self.client.getUserDataId(plane2, "MyKey1")))
-		self.assertEqual(b"This is plane 2 - 2", self.client.getUserData(self.client.getUserDataId(plane2, "MyKey2")))
-		self.assertEqual(b"This is plane 2 - 3", self.client.getUserData(self.client.getUserDataId(plane2, "MyKey3")))
-
-
-	def testMultipleLinks(self):
-		body_id = self.client.loadURDF(ROBOT_PATH)
-		num_links = self.client.getNumJoints(body_id)
-
-		self.assertTrue(num_links > 1)
-
-		for link_index in range(num_links):
-			uid1 = self.client.addUserData(body_id, "MyKey1", "Value1 for link %s" % link_index, link_index)
-			uid2 = self.client.addUserData(body_id, "MyKey2", "Value2 for link %s" % link_index, link_index)
-
-		for link_index in range(num_links):
-			uid1 = self.client.getUserDataId(body_id, "MyKey1", link_index)
-			uid2 = self.client.getUserDataId(body_id, "MyKey2", link_index)
-			self.assertEqual(("Value1 for link %s" % link_index).encode(), self.client.getUserData(uid1))
-			self.assertEqual(("Value2 for link %s" % link_index).encode(), self.client.getUserData(uid2))
-
-
-	def testMultipleClients(self):
-		client1 = self.client
-		client2 = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
-
-		plane_id = client1.loadURDF(PLANE_PATH)
-		client2.syncBodyInfo()
-
-		# Add user data on client 1, check on client 1
-		uid = client1.addUserData(plane_id, "MyKey", "MyValue")
-		self.assertEqual(None, client2.getUserData(uid))
-		client2.syncUserData()
-		self.assertEqual(b"MyValue", client2.getUserData(uid))
-
-		# Overwrite the value on client 2, check on client 1
-		client2.addUserData(plane_id, "MyKey", "MyNewValue")
-		self.assertEqual(b"MyValue", client1.getUserData(uid))
-		client1.syncUserData()
-		self.assertEqual(b"MyNewValue", client1.getUserData(uid))
-
-		# Remove user data on client 1, check on client 2
-		client1.removeUserData(uid)
-		self.assertEqual(b"MyNewValue", client2.getUserData(uid))
-		client2.syncUserData()
-		self.assertEqual(None, client2.getUserData(uid))
-
-		del client2
-
-
-	def testUserDataOnVisualShapes(self):
-		body_id = self.client.loadURDF(ROBOT_PATH)
-		num_links = self.client.getNumJoints(body_id)
-		visual_shapes = self.client.getVisualShapeData(body_id)
-
-		self.assertTrue(num_links > 0)
-		self.assertTrue(len(visual_shapes) > 0)
-
-		user_data_entries = set()
-		for link_index in range(-1, num_links):
-			num_shapes = sum([1 for shape in visual_shapes if shape[1] == link_index])
-			for shape_index in range(num_shapes):
-				key = "MyKey"
-				value = "MyValue %s, %s" % (link_index, shape_index)
-				uid = self.client.addUserData(body_id, key, value, link_index, shape_index)
-				user_data_entries.add((uid, key, value.encode(), body_id, link_index, shape_index))
-
-		self.assertEqual(len(visual_shapes), self.client.getNumUserData(body_id))
-		for uid, key, value, body_id, link_index, shape_index in user_data_entries:
-			self.assertEqual(value, self.client.getUserData(uid))
-			self.assertEqual(uid, self.client.getUserDataId(body_id, key, link_index, shape_index))
+  @classmethod
+  def setUpClass(cls):
+    TestUserDataMethods.server = bullet_client.BulletClient(
+        connection_mode=pybullet.SHARED_MEMORY_SERVER)
+
+  @classmethod
+  def tearDownClass(cls):
+    del TestUserDataMethods.server
+
+  def setUp(self):
+    self.client = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
+
+  def tearDown(self):
+    self.client.resetSimulation()
+    del self.client
+
+  def testAddUserData(self):
+    plane_id = self.client.loadURDF(PLANE_PATH)
+    uid1 = self.client.addUserData(plane_id, "MyKey1", "MyValue1")
+    uid2 = self.client.addUserData(plane_id, "MyKey2", "MyValue2")
+    uid3 = self.client.addUserData(plane_id, "MyKey3", "MyValue3")
+    uid4 = self.client.addUserData(plane_id, "MyKey4", "MyValue4")
+
+    # Retrieve user data and make sure it's correct.
+    self.assertEqual(b"MyValue1", self.client.getUserData(uid1))
+    self.assertEqual(b"MyValue2", self.client.getUserData(uid2))
+    self.assertEqual(b"MyValue3", self.client.getUserData(uid3))
+    self.assertEqual(b"MyValue4", self.client.getUserData(uid4))
+
+    # Disconnect/reconnect and make sure that the user data is synced back.
+    del self.client
+    self.client = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
+
+    self.assertEqual(b"MyValue1", self.client.getUserData(uid1))
+    self.assertEqual(b"MyValue2", self.client.getUserData(uid2))
+    self.assertEqual(b"MyValue3", self.client.getUserData(uid3))
+    self.assertEqual(b"MyValue4", self.client.getUserData(uid4))
+
+    self.client.resetSimulation()
+    self.assertEqual(None, self.client.getUserData(uid1))
+    self.assertEqual(None, self.client.getUserData(uid2))
+    self.assertEqual(None, self.client.getUserData(uid3))
+    self.assertEqual(None, self.client.getUserData(uid4))
+
+  def testGetNumUserData(self):
+    plane_id = self.client.loadURDF(PLANE_PATH)
+    uid1 = self.client.addUserData(plane_id, "MyKey1", "MyValue1")
+    uid2 = self.client.addUserData(plane_id, "MyKey2", "MyValue2")
+    uid3 = self.client.addUserData(plane_id, "MyKey3", "MyValue3")
+    uid4 = self.client.addUserData(plane_id, "MyKey4", "MyValue4")
+
+    self.assertEqual(4, self.client.getNumUserData(plane_id))
+
+    del self.client
+    self.client = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
+
+    self.assertEqual(4, self.client.getNumUserData(plane_id))
+
+  def testReplaceUserData(self):
+    plane_id = self.client.loadURDF(PLANE_PATH)
+    uid = self.client.addUserData(plane_id, "MyKey", "MyValue")
+
+    self.assertEqual(b"MyValue", self.client.getUserData(uid))
+
+    new_uid = self.client.addUserData(plane_id, "MyKey", "MyNewValue")
+    self.assertEqual(uid, new_uid)
+    self.assertEqual(b"MyNewValue", self.client.getUserData(uid))
+
+    del self.client
+    self.client = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
+
+    self.assertEqual(b"MyNewValue", self.client.getUserData(uid))
+
+  def testGetUserDataId(self):
+    plane_id = self.client.loadURDF(PLANE_PATH)
+    uid1 = self.client.addUserData(plane_id, "MyKey1", "MyValue1")
+    uid2 = self.client.addUserData(plane_id, "MyKey2", "MyValue2")
+    uid3 = self.client.addUserData(plane_id, "MyKey3", "MyValue3")
+    uid4 = self.client.addUserData(plane_id, "MyKey4", "MyValue4")
+
+    self.assertEqual(uid1, self.client.getUserDataId(plane_id, "MyKey1"))
+    self.assertEqual(uid2, self.client.getUserDataId(plane_id, "MyKey2"))
+    self.assertEqual(uid3, self.client.getUserDataId(plane_id, "MyKey3"))
+    self.assertEqual(uid4, self.client.getUserDataId(plane_id, "MyKey4"))
+
+    del self.client
+    self.client = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
+
+    self.assertEqual(uid1, self.client.getUserDataId(plane_id, "MyKey1"))
+    self.assertEqual(uid2, self.client.getUserDataId(plane_id, "MyKey2"))
+    self.assertEqual(uid3, self.client.getUserDataId(plane_id, "MyKey3"))
+    self.assertEqual(uid4, self.client.getUserDataId(plane_id, "MyKey4"))
+
+  def testRemoveUserData(self):
+    plane_id = self.client.loadURDF(PLANE_PATH)
+    uid1 = self.client.addUserData(plane_id, "MyKey1", "MyValue1")
+    uid2 = self.client.addUserData(plane_id, "MyKey2", "MyValue2")
+    uid3 = self.client.addUserData(plane_id, "MyKey3", "MyValue3")
+    uid4 = self.client.addUserData(plane_id, "MyKey4", "MyValue4")
+
+    self.client.removeUserData(uid2)
+
+    self.assertEqual(3, self.client.getNumUserData(plane_id))
+    self.assertEqual(-1, self.client.getUserDataId(plane_id, "MyKey2"))
+    self.assertEqual(None, self.client.getUserData(uid2))
+    self.assertEqual(b"MyValue1", self.client.getUserData(uid1))
+    self.assertEqual(b"MyValue3", self.client.getUserData(uid3))
+    self.assertEqual(b"MyValue4", self.client.getUserData(uid4))
+
+    del self.client
+    self.client = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
+
+    self.assertEqual(3, self.client.getNumUserData(plane_id))
+    self.assertEqual(-1, self.client.getUserDataId(plane_id, "MyKey2"))
+    self.assertEqual(None, self.client.getUserData(uid2))
+    self.assertEqual(b"MyValue1", self.client.getUserData(uid1))
+    self.assertEqual(b"MyValue3", self.client.getUserData(uid3))
+    self.assertEqual(b"MyValue4", self.client.getUserData(uid4))
+
+  def testIterateAllUserData(self):
+    plane_id = self.client.loadURDF(PLANE_PATH)
+    uid1 = self.client.addUserData(plane_id, "MyKey1", "MyValue1")
+    uid2 = self.client.addUserData(plane_id, "MyKey2", "MyValue2")
+    uid3 = self.client.addUserData(plane_id, "MyKey3", "MyValue3")
+    uid4 = self.client.addUserData(plane_id, "MyKey4", "MyValue4")
+
+    entries = set()
+    for i in range(self.client.getNumUserData(plane_id)):
+      userDataId, key, bodyId, linkIndex, visualShapeIndex = self.client.getUserDataInfo(
+          plane_id, i)
+      value = self.client.getUserData(userDataId)
+      entries.add((userDataId, key, value, bodyId, linkIndex, visualShapeIndex))
+
+    self.assertTrue((uid1, b"MyKey1", b"MyValue1", plane_id, -1, -1) in entries)
+    self.assertTrue((uid2, b"MyKey2", b"MyValue2", plane_id, -1, -1) in entries)
+    self.assertTrue((uid3, b"MyKey3", b"MyValue3", plane_id, -1, -1) in entries)
+    self.assertTrue((uid4, b"MyKey4", b"MyValue4", plane_id, -1, -1) in entries)
+    self.assertEqual(4, len(entries))
+
+  def testRemoveBody(self):
+    plane_id = self.client.loadURDF(PLANE_PATH)
+    uid1 = self.client.addUserData(plane_id, "MyKey1", "MyValue1")
+    uid2 = self.client.addUserData(plane_id, "MyKey2", "MyValue2")
+    uid3 = self.client.addUserData(plane_id, "MyKey3", "MyValue3")
+    uid4 = self.client.addUserData(plane_id, "MyKey4", "MyValue4")
+
+    self.client.removeBody(plane_id)
+    self.assertEqual(None, self.client.getUserData(uid1))
+    self.assertEqual(None, self.client.getUserData(uid2))
+    self.assertEqual(None, self.client.getUserData(uid3))
+    self.assertEqual(None, self.client.getUserData(uid4))
+
+    del self.client
+    self.client = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
+
+    self.assertEqual(None, self.client.getUserData(uid1))
+    self.assertEqual(None, self.client.getUserData(uid2))
+    self.assertEqual(None, self.client.getUserData(uid3))
+    self.assertEqual(None, self.client.getUserData(uid4))
+
+  def testMultipleBodies(self):
+    plane1 = self.client.loadURDF(PLANE_PATH)
+    plane2 = self.client.loadURDF(PLANE_PATH)
+
+    uid1 = self.client.addUserData(plane1, "MyKey1", "This is plane 1 - 1")
+    uid2 = self.client.addUserData(plane1, "MyKey2", "This is plane 1 - 2")
+
+    uid3 = self.client.addUserData(plane2, "MyKey1", "This is plane 2 - 1")
+    uid4 = self.client.addUserData(plane2, "MyKey2", "This is plane 2 - 2")
+    uid5 = self.client.addUserData(plane2, "MyKey3", "This is plane 2 - 3")
+
+    self.assertEqual(b"This is plane 1 - 1",
+                     self.client.getUserData(self.client.getUserDataId(plane1, "MyKey1")))
+    self.assertEqual(b"This is plane 1 - 2",
+                     self.client.getUserData(self.client.getUserDataId(plane1, "MyKey2")))
+
+    self.assertEqual(b"This is plane 2 - 1",
+                     self.client.getUserData(self.client.getUserDataId(plane2, "MyKey1")))
+    self.assertEqual(b"This is plane 2 - 2",
+                     self.client.getUserData(self.client.getUserDataId(plane2, "MyKey2")))
+    self.assertEqual(b"This is plane 2 - 3",
+                     self.client.getUserData(self.client.getUserDataId(plane2, "MyKey3")))
+
+  def testMultipleLinks(self):
+    body_id = self.client.loadURDF(ROBOT_PATH)
+    num_links = self.client.getNumJoints(body_id)
+
+    self.assertTrue(num_links > 1)
+
+    for link_index in range(num_links):
+      uid1 = self.client.addUserData(body_id, "MyKey1", "Value1 for link %s" % link_index,
+                                     link_index)
+      uid2 = self.client.addUserData(body_id, "MyKey2", "Value2 for link %s" % link_index,
+                                     link_index)
+
+    for link_index in range(num_links):
+      uid1 = self.client.getUserDataId(body_id, "MyKey1", link_index)
+      uid2 = self.client.getUserDataId(body_id, "MyKey2", link_index)
+      self.assertEqual(("Value1 for link %s" % link_index).encode(), self.client.getUserData(uid1))
+      self.assertEqual(("Value2 for link %s" % link_index).encode(), self.client.getUserData(uid2))
+
+  def testMultipleClients(self):
+    client1 = self.client
+    client2 = bullet_client.BulletClient(pybullet.SHARED_MEMORY)
+
+    plane_id = client1.loadURDF(PLANE_PATH)
+    client2.syncBodyInfo()
+
+    # Add user data on client 1, check on client 1
+    uid = client1.addUserData(plane_id, "MyKey", "MyValue")
+    self.assertEqual(None, client2.getUserData(uid))
+    client2.syncUserData()
+    self.assertEqual(b"MyValue", client2.getUserData(uid))
+
+    # Overwrite the value on client 2, check on client 1
+    client2.addUserData(plane_id, "MyKey", "MyNewValue")
+    self.assertEqual(b"MyValue", client1.getUserData(uid))
+    client1.syncUserData()
+    self.assertEqual(b"MyNewValue", client1.getUserData(uid))
+
+    # Remove user data on client 1, check on client 2
+    client1.removeUserData(uid)
+    self.assertEqual(b"MyNewValue", client2.getUserData(uid))
+    client2.syncUserData()
+    self.assertEqual(None, client2.getUserData(uid))
+
+    del client2
+
+  def testUserDataOnVisualShapes(self):
+    body_id = self.client.loadURDF(ROBOT_PATH)
+    num_links = self.client.getNumJoints(body_id)
+    visual_shapes = self.client.getVisualShapeData(body_id)
+
+    self.assertTrue(num_links > 0)
+    self.assertTrue(len(visual_shapes) > 0)
+
+    user_data_entries = set()
+    for link_index in range(-1, num_links):
+      num_shapes = sum([1 for shape in visual_shapes if shape[1] == link_index])
+      for shape_index in range(num_shapes):
+        key = "MyKey"
+        value = "MyValue %s, %s" % (link_index, shape_index)
+        uid = self.client.addUserData(body_id, key, value, link_index, shape_index)
+        user_data_entries.add((uid, key, value.encode(), body_id, link_index, shape_index))
+
+    self.assertEqual(len(visual_shapes), self.client.getNumUserData(body_id))
+    for uid, key, value, body_id, link_index, shape_index in user_data_entries:
+      self.assertEqual(value, self.client.getUserData(uid))
+      self.assertEqual(uid, self.client.getUserDataId(body_id, key, link_index, shape_index))
 
 
 if __name__ == "__main__":
-    unittest.main()
+  unittest.main()
diff --git a/examples/pybullet/unittests/utils.py b/examples/pybullet/unittests/utils.py
index b3e5f68fe..b6ef61d41 100644
--- a/examples/pybullet/unittests/utils.py
+++ b/examples/pybullet/unittests/utils.py
@@ -1,7 +1,8 @@
 def dot(A, b):
-    """Dot product between a 2D matrix and a 1D vector"""
-    return [sum([aij*bj for aij, bj in zip(ai, b)]) for ai in A]
+  """Dot product between a 2D matrix and a 1D vector"""
+  return [sum([aij * bj for aij, bj in zip(ai, b)]) for ai in A]
+
 
 def allclose(a, b, tol=1e-7):
-    """Are all elements of a vector close to one another"""
-    return all([abs(ai - bi) < tol for ai, bi in zip(a,b)])
+  """Are all elements of a vector close to one another"""
+  return all([abs(ai - bi) < tol for ai, bi in zip(a, b)])
diff --git a/setup.py b/setup.py
index 0e7429efc..190b7a1e1 100644
--- a/setup.py
+++ b/setup.py
@@ -1,11 +1,9 @@
-
 from setuptools import find_packages
 from sys import platform as _platform
 import sys
 import glob
 import os
 
-
 from distutils.core import setup
 from distutils.extension import Extension
 from distutils.util import get_platform
@@ -16,43 +14,57 @@ import multiprocessing
 import multiprocessing.pool
 
 
-def parallelCCompile(self, sources, output_dir=None, macros=None, include_dirs=None, debug=0, extra_preargs=None, extra_postargs=None, depends=None):
-    # those lines are copied from distutils.ccompiler.CCompiler directly
-    macros, objects, extra_postargs, pp_opts, build = self._setup_compile(output_dir, macros, include_dirs, sources, depends, extra_postargs)
-    cc_args = self._get_cc_args(pp_opts, debug, extra_preargs)
-    # parallel code
-    N = 2*multiprocessing.cpu_count()# number of parallel compilations
+def parallelCCompile(self,
+                     sources,
+                     output_dir=None,
+                     macros=None,
+                     include_dirs=None,
+                     debug=0,
+                     extra_preargs=None,
+                     extra_postargs=None,
+                     depends=None):
+  # those lines are copied from distutils.ccompiler.CCompiler directly
+  macros, objects, extra_postargs, pp_opts, build = self._setup_compile(
+      output_dir, macros, include_dirs, sources, depends, extra_postargs)
+  cc_args = self._get_cc_args(pp_opts, debug, extra_preargs)
+  # parallel code
+  N = 2 * multiprocessing.cpu_count()  # number of parallel compilations
+  try:
+    # On Unix-like platforms attempt to obtain the total memory in the
+    # machine and limit the number of parallel jobs to the number of Gbs
+    # of RAM (to avoid killing smaller platforms like the Pi)
+    mem = os.sysconf('SC_PHYS_PAGES') * os.sysconf('SC_PAGE_SIZE')  # bytes
+  except (AttributeError, ValueError):
+    # Couldn't query RAM; don't limit parallelism (it's probably a well
+    # equipped Windows / Mac OS X box)
+    pass
+  else:
+    mem = max(1, int(round(mem / 1024**3)))  # convert to Gb
+    N = min(mem, N)
+
+  def _single_compile(obj):
     try:
-        # On Unix-like platforms attempt to obtain the total memory in the
-        # machine and limit the number of parallel jobs to the number of Gbs
-        # of RAM (to avoid killing smaller platforms like the Pi)
-        mem = os.sysconf('SC_PHYS_PAGES') * os.sysconf('SC_PAGE_SIZE') # bytes
-    except (AttributeError, ValueError):
-        # Couldn't query RAM; don't limit parallelism (it's probably a well
-        # equipped Windows / Mac OS X box)
-        pass
-    else:
-        mem = max(1, int(round(mem / 1024 ** 3))) # convert to Gb
-        N = min(mem, N)
-    def _single_compile(obj):
-        try: src, ext = build[obj]
-        except KeyError: return
-        newcc_args = cc_args
-        if _platform == "darwin":
-          if src.endswith('.cpp'):
-            newcc_args = cc_args + ["-stdlib=libc++"]
-        self._compile(obj, src, ext, newcc_args, extra_postargs, pp_opts)
-    # convert to list, imap is evaluated on-demand
-    pool = multiprocessing.pool.ThreadPool(N)
-    list(pool.imap(_single_compile,objects))
-    return objects
+      src, ext = build[obj]
+    except KeyError:
+      return
+    newcc_args = cc_args
+    if _platform == "darwin":
+      if src.endswith('.cpp'):
+        newcc_args = cc_args + ["-stdlib=libc++"]
+    self._compile(obj, src, ext, newcc_args, extra_postargs, pp_opts)
+
+  # convert to list, imap is evaluated on-demand
+  pool = multiprocessing.pool.ThreadPool(N)
+  list(pool.imap(_single_compile, objects))
+  return objects
+
+
 import distutils.ccompiler
-distutils.ccompiler.CCompiler.compile=parallelCCompile
+distutils.ccompiler.CCompiler.compile = parallelCCompile
 
 #see http://stackoverflow.com/a/8719066/295157
 import os
 
-
 platform = get_platform()
 print(platform)
 
@@ -68,27 +80,24 @@ CXX_FLAGS += '-DB3_USE_ZIPFILE_FILEIO '
 CXX_FLAGS += '-DBT_THREADSAFE=1 '
 CXX_FLAGS += '-DSTATIC_LINK_SPD_PLUGIN '
 
-
 EGL_CXX_FLAGS = ''
 
-
-
 # libraries += [current_python]
 
 libraries = []
 include_dirs = []
 
 try:
-    import numpy
-    NP_DIRS = [numpy.get_include()]
+  import numpy
+  NP_DIRS = [numpy.get_include()]
 except:
-	  print("numpy is disabled. getCameraImage maybe slower.")
+  print("numpy is disabled. getCameraImage maybe slower.")
 else:
-	  print("numpy is enabled.")
-	  CXX_FLAGS += '-DPYBULLET_USE_NUMPY '
-	  for d in NP_DIRS:
-	    print("numpy_include_dirs = %s" % d)
-	  include_dirs += NP_DIRS
+  print("numpy is enabled.")
+  CXX_FLAGS += '-DPYBULLET_USE_NUMPY '
+  for d in NP_DIRS:
+    print("numpy_include_dirs = %s" % d)
+  include_dirs += NP_DIRS
 
 sources = ["examples/pybullet/pybullet.c"]\
 +["src/btLinearMathAll.cpp"]\
@@ -358,71 +367,70 @@ egl_renderer_sources = \
 +["examples/OpenGLWindow/LoadShader.cpp"]
 
 if 'BT_USE_EGL' in CXX_FLAGS:
-    sources += ['examples/ThirdPartyLibs/glad/egl.c']
-    sources += ['examples/OpenGLWindow/EGLOpenGLWindow.cpp']
+  sources += ['examples/ThirdPartyLibs/glad/egl.c']
+  sources += ['examples/OpenGLWindow/EGLOpenGLWindow.cpp']
 
 if _platform == "linux" or _platform == "linux2":
-    libraries = ['dl','pthread']
-    CXX_FLAGS += '-D_LINUX '
-    CXX_FLAGS += '-DGLEW_STATIC '
-    CXX_FLAGS += '-DGLEW_INIT_OPENGL11_FUNCTIONS=1 '
-    CXX_FLAGS += '-DGLEW_DYNAMIC_LOAD_ALL_GLX_FUNCTIONS=1 '
-    CXX_FLAGS += '-DDYNAMIC_LOAD_X11_FUNCTIONS '
-    CXX_FLAGS += '-DHAS_SOCKLEN_T '
-    CXX_FLAGS += '-fno-inline-functions-called-once '
-    CXX_FLAGS += '-fvisibility=hidden '
-    CXX_FLAGS += '-fvisibility-inlines-hidden '
-    EGL_CXX_FLAGS += '-DBT_USE_EGL '
-    EGL_CXX_FLAGS += '-fPIC ' # for plugins
-
-    sources = sources + ["examples/ThirdPartyLibs/enet/unix.c"]\
+  libraries = ['dl', 'pthread']
+  CXX_FLAGS += '-D_LINUX '
+  CXX_FLAGS += '-DGLEW_STATIC '
+  CXX_FLAGS += '-DGLEW_INIT_OPENGL11_FUNCTIONS=1 '
+  CXX_FLAGS += '-DGLEW_DYNAMIC_LOAD_ALL_GLX_FUNCTIONS=1 '
+  CXX_FLAGS += '-DDYNAMIC_LOAD_X11_FUNCTIONS '
+  CXX_FLAGS += '-DHAS_SOCKLEN_T '
+  CXX_FLAGS += '-fno-inline-functions-called-once '
+  CXX_FLAGS += '-fvisibility=hidden '
+  CXX_FLAGS += '-fvisibility-inlines-hidden '
+  EGL_CXX_FLAGS += '-DBT_USE_EGL '
+  EGL_CXX_FLAGS += '-fPIC '  # for plugins
+
+  sources = sources + ["examples/ThirdPartyLibs/enet/unix.c"]\
+  +["examples/OpenGLWindow/X11OpenGLWindow.cpp"]\
+  +["examples/ThirdPartyLibs/glad/gl.c"]\
+  +["examples/ThirdPartyLibs/glad/glx.c"]
+  include_dirs += ["examples/ThirdPartyLibs/optionalX11"]
+
+  if 'BT_USE_EGL' in EGL_CXX_FLAGS:
+    egl_renderer_sources = egl_renderer_sources\
+    +["examples/OpenGLWindow/EGLOpenGLWindow.cpp"]\
+    +['examples/ThirdPartyLibs/glad/egl.c']
+  else:
+    egl_renderer_sources = egl_renderer_sources\
     +["examples/OpenGLWindow/X11OpenGLWindow.cpp"]\
-    +["examples/ThirdPartyLibs/glad/gl.c"]\
     +["examples/ThirdPartyLibs/glad/glx.c"]
-    include_dirs += ["examples/ThirdPartyLibs/optionalX11"]
-
-    if 'BT_USE_EGL' in EGL_CXX_FLAGS:
-        egl_renderer_sources = egl_renderer_sources\
-        +["examples/OpenGLWindow/EGLOpenGLWindow.cpp"]\
-        +['examples/ThirdPartyLibs/glad/egl.c']
-    else:
-        egl_renderer_sources = egl_renderer_sources\
-        +["examples/OpenGLWindow/X11OpenGLWindow.cpp"]\
-        +["examples/ThirdPartyLibs/glad/glx.c"]
-
 
 elif _platform == "win32":
-    print("win32!")
-    libraries = ['Ws2_32','Winmm','User32','Opengl32','kernel32','glu32','Gdi32','Comdlg32']
-    CXX_FLAGS += '-DWIN32 '
-    CXX_FLAGS += '-DGLEW_STATIC '
-    sources = sources + ["examples/ThirdPartyLibs/enet/win32.c"]\
-    +["examples/OpenGLWindow/Win32Window.cpp"]\
-    +["examples/OpenGLWindow/Win32OpenGLWindow.cpp"]\
-    +["examples/ThirdPartyLibs/glad/gl.c"]
+  print("win32!")
+  libraries = ['Ws2_32', 'Winmm', 'User32', 'Opengl32', 'kernel32', 'glu32', 'Gdi32', 'Comdlg32']
+  CXX_FLAGS += '-DWIN32 '
+  CXX_FLAGS += '-DGLEW_STATIC '
+  sources = sources + ["examples/ThirdPartyLibs/enet/win32.c"]\
+  +["examples/OpenGLWindow/Win32Window.cpp"]\
+  +["examples/OpenGLWindow/Win32OpenGLWindow.cpp"]\
+  +["examples/ThirdPartyLibs/glad/gl.c"]
 elif _platform == "darwin":
-    print("darwin!")
-    os.environ['LDFLAGS'] = '-framework Cocoa -stdlib=libc++ -framework OpenGL'
-    CXX_FLAGS += '-DB3_NO_PYTHON_FRAMEWORK '
-    CXX_FLAGS += '-DHAS_SOCKLEN_T '
-    CXX_FLAGS += '-D_DARWIN '
-#    CXX_FLAGS += '-framework Cocoa '
-    sources = sources + ["examples/ThirdPartyLibs/enet/unix.c"]\
-    +["examples/OpenGLWindow/MacOpenGLWindow.cpp"]\
-    +["examples/ThirdPartyLibs/glad/gl.c"]\
-    +["examples/OpenGLWindow/MacOpenGLWindowObjC.m"]
+  print("darwin!")
+  os.environ['LDFLAGS'] = '-framework Cocoa -stdlib=libc++ -framework OpenGL'
+  CXX_FLAGS += '-DB3_NO_PYTHON_FRAMEWORK '
+  CXX_FLAGS += '-DHAS_SOCKLEN_T '
+  CXX_FLAGS += '-D_DARWIN '
+  #    CXX_FLAGS += '-framework Cocoa '
+  sources = sources + ["examples/ThirdPartyLibs/enet/unix.c"]\
+  +["examples/OpenGLWindow/MacOpenGLWindow.cpp"]\
+  +["examples/ThirdPartyLibs/glad/gl.c"]\
+  +["examples/OpenGLWindow/MacOpenGLWindowObjC.m"]
 else:
-    print("bsd!")
-    libraries = ['GL','GLEW','pthread']
-    os.environ['LDFLAGS'] = '-L/usr/X11R6/lib'
-    CXX_FLAGS += '-D_BSD '
-    CXX_FLAGS += '-I/usr/X11R6/include '
-    CXX_FLAGS += '-DHAS_SOCKLEN_T '
-    CXX_FLAGS += '-fno-inline-functions-called-once'
-    sources = ["examples/ThirdPartyLibs/enet/unix.c"]\
-    +["examples/OpenGLWindow/X11OpenGLWindow.cpp"]\
-    +["examples/ThirdPartyLibs/glad/gl.c"]\
-    + sources
+  print("bsd!")
+  libraries = ['GL', 'GLEW', 'pthread']
+  os.environ['LDFLAGS'] = '-L/usr/X11R6/lib'
+  CXX_FLAGS += '-D_BSD '
+  CXX_FLAGS += '-I/usr/X11R6/include '
+  CXX_FLAGS += '-DHAS_SOCKLEN_T '
+  CXX_FLAGS += '-fno-inline-functions-called-once'
+  sources = ["examples/ThirdPartyLibs/enet/unix.c"]\
+  +["examples/OpenGLWindow/X11OpenGLWindow.cpp"]\
+  +["examples/ThirdPartyLibs/glad/gl.c"]\
+  + sources
 
 setup_py_dir = os.path.dirname(os.path.realpath(__file__))
 
@@ -432,67 +440,75 @@ datadir = "examples/pybullet/gym/pybullet_data"
 hh = setup_py_dir + "/" + datadir
 
 for root, dirs, files in os.walk(hh):
-    for fn in files:
-        ext = os.path.splitext(fn)[1][1:]
-        if ext and ext in 'yaml index meta data-00000-of-00001 png gif jpg urdf sdf obj txt mtl dae off stl STL xml '.split():
-            fn = root + "/" + fn
-            need_files.append(fn[1+len(hh):])
+  for fn in files:
+    ext = os.path.splitext(fn)[1][1:]
+    if ext and ext in 'yaml index meta data-00000-of-00001 png gif jpg urdf sdf obj txt mtl dae off stl STL xml '.split(
+    ):
+      fn = root + "/" + fn
+      need_files.append(fn[1 + len(hh):])
 
 print("found resource files: %i" % len(need_files))
-for n in need_files: print("-- %s" % n)
+for n in need_files:
+  print("-- %s" % n)
 print("packages")
 print(find_packages('examples/pybullet/gym'))
 print("-----")
 
 extensions = []
 
-pybullet_ext = Extension("pybullet",
-        sources =  sources,
-        libraries = libraries,
-        extra_compile_args=CXX_FLAGS.split(),
-        include_dirs = include_dirs + ["src","examples/ThirdPartyLibs","examples/ThirdPartyLibs/glad", "examples/ThirdPartyLibs/enet/include","examples/ThirdPartyLibs/clsocket/src"]
-     )
+pybullet_ext = Extension(
+    "pybullet",
+    sources=sources,
+    libraries=libraries,
+    extra_compile_args=CXX_FLAGS.split(),
+    include_dirs=include_dirs + [
+        "src", "examples/ThirdPartyLibs", "examples/ThirdPartyLibs/glad",
+        "examples/ThirdPartyLibs/enet/include", "examples/ThirdPartyLibs/clsocket/src"
+    ])
 extensions.append(pybullet_ext)
 
-
 if 'BT_USE_EGL' in EGL_CXX_FLAGS:
 
-	eglRender = Extension("eglRenderer",
-        	sources =  egl_renderer_sources,
-        	libraries = libraries,
-        	extra_compile_args=(CXX_FLAGS+EGL_CXX_FLAGS ).split(),
-        	include_dirs = include_dirs + ["src","examples", "examples/ThirdPartyLibs","examples/ThirdPartyLibs/glad", "examples/ThirdPartyLibs/enet/include","examples/ThirdPartyLibs/clsocket/src"])
-
-	extensions.append(eglRender)
+  eglRender = Extension(
+      "eglRenderer",
+      sources=egl_renderer_sources,
+      libraries=libraries,
+      extra_compile_args=(CXX_FLAGS + EGL_CXX_FLAGS).split(),
+      include_dirs=include_dirs + [
+          "src", "examples", "examples/ThirdPartyLibs", "examples/ThirdPartyLibs/glad",
+          "examples/ThirdPartyLibs/enet/include", "examples/ThirdPartyLibs/clsocket/src"
+      ])
 
+  extensions.append(eglRender)
 
 setup(
-	name = 'pybullet',
-	version='2.4.8',
-	description='Official Python Interface for the Bullet Physics SDK specialized for Robotics Simulation and Reinforcement Learning',
-	long_description='pybullet is an easy to use Python module for physics simulation, robotics and deep reinforcement learning based on the Bullet Physics SDK. With pybullet you can load articulated bodies from URDF, SDF and other file formats. pybullet provides forward dynamics simulation, inverse dynamics computation, forward and inverse kinematics and collision detection and ray intersection queries. Aside from physics simulation, pybullet supports to rendering, with a CPU renderer and OpenGL visualization and support for virtual reality headsets.',
-	url='https://github.com/bulletphysics/bullet3',
-	author='Erwin Coumans, Yunfei Bai, Jasmine Hsu',
-	author_email='erwincoumans@google.com',
-	license='zlib',
-	platforms='any',
-	keywords=['game development', 'virtual reality', 'physics simulation', 'robotics', 'collision detection', 'opengl'],
-	ext_modules = extensions,
-	classifiers=['Development Status :: 5 - Production/Stable',
-                   'License :: OSI Approved :: zlib/libpng License',
-                   'Operating System :: Microsoft :: Windows',
-                   'Operating System :: POSIX :: Linux',
-                   'Operating System :: MacOS',
-                   'Intended Audience :: Science/Research',
-                   "Programming Language :: Python",
-                   'Programming Language :: Python :: 2.7',
-                   'Programming Language :: Python :: 3.4',
-                   'Programming Language :: Python :: 3.5',
-                   'Programming Language :: Python :: 3.6',
-                   'Topic :: Games/Entertainment :: Simulation',
-                   'Topic :: Scientific/Engineering :: Artificial Intelligence',
-                   'Framework :: Robot Framework'],
-    package_dir = { '': 'examples/pybullet/gym'},
+    name='pybullet',
+    version='2.5.0',
+    description=
+    'Official Python Interface for the Bullet Physics SDK specialized for Robotics Simulation and Reinforcement Learning',
+    long_description=
+    'pybullet is an easy to use Python module for physics simulation, robotics and deep reinforcement learning based on the Bullet Physics SDK. With pybullet you can load articulated bodies from URDF, SDF and other file formats. pybullet provides forward dynamics simulation, inverse dynamics computation, forward and inverse kinematics and collision detection and ray intersection queries. Aside from physics simulation, pybullet supports to rendering, with a CPU renderer and OpenGL visualization and support for virtual reality headsets.',
+    url='https://github.com/bulletphysics/bullet3',
+    author='Erwin Coumans, Yunfei Bai, Jasmine Hsu',
+    author_email='erwincoumans@google.com',
+    license='zlib',
+    platforms='any',
+    keywords=[
+        'game development', 'virtual reality', 'physics simulation', 'robotics',
+        'collision detection', 'opengl'
+    ],
+    ext_modules=extensions,
+    classifiers=[
+        'Development Status :: 5 - Production/Stable',
+        'License :: OSI Approved :: zlib/libpng License',
+        'Operating System :: Microsoft :: Windows', 'Operating System :: POSIX :: Linux',
+        'Operating System :: MacOS', 'Intended Audience :: Science/Research',
+        "Programming Language :: Python", 'Programming Language :: Python :: 2.7',
+        'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5',
+        'Programming Language :: Python :: 3.6', 'Topic :: Games/Entertainment :: Simulation',
+        'Topic :: Scientific/Engineering :: Artificial Intelligence',
+        'Framework :: Robot Framework'
+    ],
+    package_dir={'': 'examples/pybullet/gym'},
     packages=[x for x in find_packages('examples/pybullet/gym')],
-    package_data = { 'pybullet_data': need_files }
-)
+    package_data={'pybullet_data': need_files})
diff --git a/src/Bullet3Common/b3Vector3.h b/src/Bullet3Common/b3Vector3.h
index 56e6c1331..a70d68d6e 100644
--- a/src/Bullet3Common/b3Vector3.h
+++ b/src/Bullet3Common/b3Vector3.h
@@ -36,7 +36,7 @@ subject to the following restrictions:
 #pragma warning(disable : 4556)  // value of intrinsic immediate argument '4294967239' is out of range '0 - 255'
 #endif
 
-#define B3_SHUFFLE(x, y, z, w) ((w) << 6 | (z) << 4 | (y) << 2 | (x))
+#define B3_SHUFFLE(x, y, z, w) (((w) << 6 | (z) << 4 | (y) << 2 | (x)) & 0xff)
 //#define b3_pshufd_ps( _a, _mask ) (__m128) _mm_shuffle_epi32((__m128i)(_a), (_mask) )
 #define b3_pshufd_ps(_a, _mask) _mm_shuffle_ps((_a), (_a), (_mask))
 #define b3_splat3_ps(_a, _i) b3_pshufd_ps((_a), B3_SHUFFLE(_i, _i, _i, 3))
diff --git a/src/BulletDynamics/Featherstone/btMultiBody.cpp b/src/BulletDynamics/Featherstone/btMultiBody.cpp
index 44a7ecaf9..8df15b58c 100644
--- a/src/BulletDynamics/Featherstone/btMultiBody.cpp
+++ b/src/BulletDynamics/Featherstone/btMultiBody.cpp
@@ -344,6 +344,7 @@ void btMultiBody::finalizeMultiDof()
 	m_deltaV.resize(6 + m_dofCount);

 	m_realBuf.resize(6 + m_dofCount + m_dofCount * m_dofCount + 6 + m_dofCount);  //m_dofCount for joint-space vels + m_dofCount^2 for "D" matrices + delta-pos vector (6 base "vels" + joint "vels")

 	m_vectorBuf.resize(2 * m_dofCount);                                           //two 3-vectors (i.e. one six-vector) for each system dof	("h" matrices)

+	m_matrixBuf.resize(m_links.size() + 1);

 	for (int i = 0; i < m_vectorBuf.size(); i++)

 	{

 		m_vectorBuf[i].setValue(0, 0, 0);

diff --git a/src/BulletDynamics/Featherstone/btMultiBodyDynamicsWorld.cpp b/src/BulletDynamics/Featherstone/btMultiBodyDynamicsWorld.cpp
index 2050baa75..1131e5378 100644
--- a/src/BulletDynamics/Featherstone/btMultiBodyDynamicsWorld.cpp
+++ b/src/BulletDynamics/Featherstone/btMultiBodyDynamicsWorld.cpp
@@ -238,7 +238,7 @@ struct MultiBodyInplaceSolverIslandCallback : public btSimulationIslandManager::
 	{
 	}
 
-	MultiBodyInplaceSolverIslandCallback& operator=(MultiBodyInplaceSolverIslandCallback& other)
+	MultiBodyInplaceSolverIslandCallback& operator=(const MultiBodyInplaceSolverIslandCallback& other)
 	{
 		btAssert(0);
 		(void)other;
diff --git a/src/LinearMath/btVector3.h b/src/LinearMath/btVector3.h
index 61fd8d1e4..d65ed9808 100644
--- a/src/LinearMath/btVector3.h
+++ b/src/LinearMath/btVector3.h
@@ -36,7 +36,7 @@ subject to the following restrictions:
 #pragma warning(disable : 4556)  // value of intrinsic immediate argument '4294967239' is out of range '0 - 255'
 #endif
 
-#define BT_SHUFFLE(x, y, z, w) ((w) << 6 | (z) << 4 | (y) << 2 | (x))
+#define BT_SHUFFLE(x, y, z, w) (((w) << 6 | (z) << 4 | (y) << 2 | (x)) & 0xff)
 //#define bt_pshufd_ps( _a, _mask ) (__m128) _mm_shuffle_epi32((__m128i)(_a), (_mask) )
 #define bt_pshufd_ps(_a, _mask) _mm_shuffle_ps((_a), (_a), (_mask))
 #define bt_splat3_ps(_a, _i) bt_pshufd_ps((_a), BT_SHUFFLE(_i, _i, _i, 3))