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#add parent dir to find package. Only needed for source code build, pip install doesn't need it.
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)
import pybullet
import gym
import numpy as np
import pybullet_envs
import time
def relu(x):
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 main():
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:
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
weights_dense1_w = np.array([
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])
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_final_w = np.array([
[ -0.0133, +0.0628, -0.6441, +0.2383, +0.4270, +0.1927, -0.3628, +0.3698],
[ -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])
if __name__=="__main__":
main()
|
