path: root/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_HalfCheetahBulletEnv_v0_2017may.py

#add parent dir to find package. Only needed for source code build, pip install doesn't need it.
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)

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():
  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()

  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: break


# yapf: disable
weights_dense1_w = np.array(
    [[
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        -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
    ],
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weights_dense2_w = np.array(
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         +0.0008, -0.0075, +0.0959, -0.2459, -0.4452, +0.1211, -0.0521,
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         -0.8141, +0.0871, -0.5981, +0.2595, +0.2109, -0.3252, -0.2758,
         +0.2662, -0.1662, +0.0087, -0.3265, +0.2910, -0.3716, +0.3225,
         +0.0907, +0.0550, +0.4069, +0.3731, -0.0367, -0.0844, -0.7320,
         +0.3318, +0.0839, +0.1493, -0.1054, -0.4476, +0.0671, +0.3858, -0.3728
     ],
     [
         -0.0163, +0.4856, -0.1412, -0.0100, -0.2343, +0.1223, -0.5244,
         -0.4046, +0.1738, -0.0262, +0.0922, -0.6767, +0.2672, +0.1770,
         -0.4969, +0.3919, -0.1103, -0.3749, +0.2580, -0.0209, -0.2500,
         -0.1089, +0.0587, +0.4735, +0.2116, +0.0299, +0.1709, +0.0804,
         +0.8460, +0.1637, +0.1775, -0.3955, +0.2403, -0.1843, +0.0579,
         -0.3362, +0.1129, +0.2148, -0.0079, -0.0662, -0.1286, +0.1830,
         +0.0437, +0.0167, -0.0824, -0.1148, +0.2380, -0.3372, -0.0684,
         -0.3226, +0.2914, +0.0994, -0.1599, +0.0130, +0.1584, +0.0975,
         +0.2723, +0.1877, -0.4828, +0.1463, -0.2304, +0.3497, +0.0522, -0.0404
     ],
     [
         +0.3846, +0.3960, +0.0721, -0.5932, -0.6720, +0.3994, -1.1131,
         +0.0500, -0.0433, -0.5014, -0.2795, +0.0226, -0.0276, -0.3389,
         -0.1971, +0.3228, -0.3439, +0.5770, +0.5260, -0.2861, +0.3810,
         -0.0225, +0.3133, +0.1716, +0.0778, -0.1298, +0.0765, -0.1700,
         +0.5689, +0.2767, -0.5815, +0.2572, -0.7600, -0.1481, +0.2901,
         -0.4648, +0.1381, +0.2972, -0.4802, -0.6701, -0.6240, -0.0784,
         -0.0330, -0.5339, +0.1989, +0.2422, +0.0886, -0.1819, -0.2638,
         +0.0189, -0.1023, +0.3559, +0.2563, +0.0272, -0.1343, +0.2034,
         +0.6096, -0.2862, -0.2925, +0.1162, -0.5740, -0.4293, -0.1544, -0.3657
     ],
     [
         -0.1666, -0.5683, +0.3239, +0.0008, -0.0790, +0.0108, +0.1210,
         +0.2313, -0.0242, -0.3114, +0.1546, -0.0983, -0.1095, -0.5754,
         +0.1360, -0.7269, +0.0714, -0.9205, +0.2401, +0.0983, +0.1060,
         +0.0410, +0.2889, -0.7942, +0.3511, +0.1338, +0.1118, +0.1873,
         -0.4490, +0.2648, -0.1163, +0.1410, -0.0005, -0.0627, +0.2816,
         -0.0261, +0.0111, -0.3929, -0.3200, +0.3405, +0.1021, -0.0294,
         +0.2000, -0.2919, -0.2395, +0.1867, +0.1548, -0.0702, +0.0021,
         +0.5599, -0.2662, -0.4859, -0.1809, +0.2777, +0.2588, +0.1977,
         +0.0975, -0.3367, -0.1211, +0.1057, -0.0247, +0.4044, +0.4802, -0.4940
     ],
     [
         -0.3074, +0.2169, +0.0655, -0.3575, +0.2765, -1.0735, +0.3228,
         -0.7085, -0.1075, -0.1250, +0.1302, -0.2700, -0.6832, -0.0223,
         -0.5448, +0.3824, -0.5548, +0.2615, -0.4700, +1.0847, -0.0443,
         -0.9753, +0.1278, +1.0526, +0.7114, +0.2395, +0.1612, -0.3276,
         +0.3154, +0.4751, -0.3890, -0.2353, +0.6575, -0.1306, -0.4566,
         -0.3303, +0.6051, -0.1868, -0.9390, -0.3405, +0.3341, +0.5198,
         +0.0939, -0.6396, -0.0410, -0.0942, -0.4784, -0.6260, -0.0983,
         -0.3750, -0.1402, -0.0351, +0.0890, -0.5765, +0.3816, -0.5375,
         +0.6580, +0.0448, -0.3544, +0.1669, +0.0808, -0.4698, +0.0085, +0.5292
     ],
     [
         -0.1747, -0.5134, +0.1257, +0.6530, +0.3251, +0.0013, +0.8608,
         -0.6414, +0.3327, -0.3861, +0.5066, -0.8033, +0.1957, -0.2380,
         -0.1649, -0.0760, -0.3093, -0.3283, +0.0661, -0.1066, +0.3787,
         -0.3254, +0.5536, -0.4949, +0.1560, +0.1118, +0.1465, +0.3107,
         +0.5213, +0.1194, +0.2602, +0.0551, -0.0612, +0.1378, -0.1037,
         +0.5086, -0.3383, -0.2024, -0.1786, -0.2193, -0.3955, -0.0600,
         +0.4502, -0.0450, -0.3765, -0.9599, +0.2312, +0.0751, -0.1329,
         +0.1573, -0.2891, -0.0944, -0.4619, +0.3256, +0.2486, -0.2238,
         +0.2054, -0.1677, -1.2201, +0.2986, +0.5681, +0.0700, -0.0916, +0.3844
     ],
     [
         +0.5860, +0.1473, -0.3536, +0.1924, -0.1819, +0.3126, -0.3094,
         +0.4127, -0.3403, -0.3101, +0.4899, +0.3310, +0.3136, -0.1343,
         +0.1557, +0.0007, +0.0431, -0.0531, -0.3300, +0.3820, -0.3771,
         -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_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_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()