path: root/examples/pybullet/gym/pybullet_envs/examples/enjoy_TF_InvertedPendulumBulletEnv_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], 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)

  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


# 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
     ],
     [
         +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
     ]])

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([
    [
        +0.0089, +0.2241, -0.0391, +0.1459, -0.0854, -0.0878, +0.2829, -0.1620,
        -0.1694, -0.5211, +0.0155, -0.1298, -0.0629, +0.1074, +0.0150, -0.3583,
        +0.0427, +0.1813, +0.2140, -0.4230, +0.1577, +0.1223, -0.0096, +0.0183,
        -0.1038, -0.5612, -0.0614, -0.0820, -0.0057, -0.2471, +0.0355, -0.1525
    ],
    [
        +0.1555, -0.2934, +0.2690, -0.3218, +0.0101, -0.1188, -0.1798, -0.1405,
        +0.2701, -0.0972, +0.2338, -0.0122, -0.2254, -0.3225, -0.0268, -0.0829,
        +0.4085, +0.0691, -0.1448, -0.0429, -0.2750, -0.2479, +0.0396, +0.0427,
        +0.0205, -0.1462, -0.1481, +0.1365, -0.0903, +0.0094, +0.3665, +0.1163
    ],
    [
        +0.0119, -0.3100, +0.1201, -0.2257, +0.1246, -0.1335, -0.3369, -0.0408,
        +0.3145, +0.2030, +0.1506, +0.0899, -0.1192, -0.2429, +0.0356, +0.0634,
        -0.0706, +0.1119, -0.0402, +0.1011, +0.1281, +0.4318, -0.4644, +0.0039,
        +0.0932, -0.0521, -0.1528, +0.1946, -0.0921, -0.0646, -0.0241, +0.1598
    ],
    [
        -0.1007, +0.3939, -0.2066, +0.0752, -0.1709, -0.0286, +0.0196, +0.1853,
        -0.3619, -0.0449, +0.0334, -0.2673, +0.0640, +0.3055, -0.1184, -0.4550,
        +0.0951, -0.2168, +0.1502, -0.4816, +0.1392, -0.3708, -0.0849, -0.4331,
        -0.0800, -0.0967, +0.1334, -0.3169, -0.0004, -0.3002, -0.0841, -0.1763
    ],
    [
        -0.0492, +0.0308, +0.0824, +0.0568, -0.0038, +0.3196, +0.5089, +0.0391,
        -0.1373, -0.1579, +0.0219, -0.2990, -0.0113, -0.2136, -0.0240, +0.1241,
        -0.1723, -0.0064, -0.0213, -0.2213, -0.0996, -0.0333, -0.4110, -0.2074,
        +0.0427, +0.0323, -0.0920, -0.1846, -0.1037, -0.0381, -0.0763, +0.0875
    ],
    [
        +0.0965, -0.1536, -0.0270, -0.0834, +0.0270, +0.0908, -0.0257, -0.1284,
        +0.1994, +0.2317, +0.0193, +0.0493, -0.0723, -0.2748, +0.0248, -0.0021,
        -0.0483, +0.0610, -0.0056, -0.0575, +0.0930, +0.0749, -0.2599, +0.0223,
        +0.0050, -0.0569, -0.6755, +0.2190, +0.0009, +0.1493, -0.1822, +0.0763
    ],
    [
        -0.0435, +0.3829, -0.2358, +0.3554, -0.1800, +0.0008, -0.0282, -0.0139,
        -0.2745, -0.2293, -0.4456, +0.1709, +0.0687, -0.0696, -0.0877, -0.0978,
        -0.0620, -0.4380, +0.2052, -0.1479, +0.0971, -0.0031, +0.0783, -0.0749,
        -0.2695, -0.0151, -0.0066, +0.0592, -0.0088, -0.0507, -0.0167, -0.2891
    ],
    [
        -0.1797, -0.1446, -0.0609, -0.2840, +0.1933, +0.0366, -0.3077, -0.0018,
        -0.1564, +0.0283, +0.1447, +0.2110, -0.0047, -0.2123, +0.0041, +0.0171,
        +0.2826, +0.1549, -0.1211, +0.1360, +0.1473, +0.1541, -0.1583, +0.0955,
        -0.1047, +0.0530, +0.0667, +0.1454, -0.0860, +0.0602, +0.1970, +0.0716
    ],
    [
        +0.0119, +0.1858, -0.1746, +0.0911, -0.0948, -0.0898, -0.0680, -0.2266,
        -0.1098, +0.0161, +0.0265, +0.1100, -0.3467, -0.0128, -0.2249, +0.0344,
        +0.1421, -0.1222, -0.0196, -0.1188, +0.0428, -0.2318, +0.0998, +0.1017,
        +0.0298, -0.1391, +0.1229, +0.1193, +0.0565, +0.1296, +0.0939, -0.0234
    ],
    [
        +0.1817, +0.2432, -0.2712, +0.0668, -0.1836, +0.0232, -0.0793, +0.0161,
        -0.1585, -0.3731, -0.0243, -0.1066, +0.0928, -0.0499, -0.0692, -0.3354,
        +0.0754, +0.0468, -0.2522, -0.7501, +0.0235, -0.5134, -0.3031, -0.1907,
        -0.2166, -0.1713, -0.0422, +0.0831, +0.0664, -0.0462, +0.1627, -0.4927
    ],
    [
        -0.0342, +0.2310, +0.2736, -0.0703, +0.1941, -0.0428, -0.0868, -0.2146,
        +0.1371, +0.0117, +0.0218, +0.0133, -0.0416, +0.1012, +0.1689, +0.3113,
        +0.0199, +0.1176, +0.0256, +0.0907, +0.0622, +0.3312, -0.0225, -0.0187,
        +0.2089, +0.1381, -0.2949, +0.1525, -0.0514, -0.1416, -0.0381, -0.0133
    ],
    [
        -0.0885, +0.3841, -0.3811, +0.1388, -0.1801, -0.0434, +0.1371, -0.0393,
        +0.2549, -0.4207, -0.2308, +0.0187, -0.0975, +0.2137, -0.0840, -0.0491,
        +0.0424, +0.0060, +0.1007, +0.0315, +0.3005, +0.0501, +0.0516, -0.0521,
        -0.0100, +0.0984, +0.3092, +0.0031, -0.0380, +0.2344, +0.0808, -0.0694
    ],
    [
        -0.0631, +0.0290, +0.1733, -0.0555, +0.1311, -0.0812, +0.1056, -0.1663,
        -0.1272, +0.2717, +0.0247, +0.0730, -0.3714, +0.0042, -0.0490, +0.0222,
        -0.0429, -0.1618, +0.1476, +0.1699, -0.1660, +0.1571, -0.0225, +0.1582,
        +0.1622, -0.0721, -0.1198, +0.1388, -0.1661, +0.0103, -0.1386, +0.0883
    ],
    [
        +0.0306, +0.1041, -0.2540, +0.0423, +0.1098, -0.0204, +0.1478, +0.1917,
        +0.1102, +0.0045, -0.0263, +0.0818, -0.0245, -0.0047, -0.2407, -0.6658,
        +0.0834, +0.0400, +0.1785, -0.5141, +0.3379, -0.5638, -0.0012, -0.2549,
        -0.4172, -0.2134, -0.3793, -0.0736, -0.3442, +0.1044, -0.0489, -0.2967
    ],
    [
        -0.0446, -0.1153, -0.0839, +0.0948, +0.3570, -0.0520, -0.1016, -0.0265,
        +0.4342, +0.2325, +0.1763, -0.2663, -0.0676, -0.0759, +0.0654, +0.2983,
        +0.1185, -0.0233, -0.5232, +0.1075, -0.3284, +0.2703, +0.2164, +0.0092,
        +0.2988, +0.1956, +0.0582, +0.3342, +0.0949, -0.1936, -0.0465, +0.4223
    ],
    [
        +0.0737, -0.0069, -0.1301, +0.3047, -0.2603, +0.0369, -0.2049, +0.0378,
        -0.1846, -0.3474, -0.1353, +0.0965, +0.0956, -0.0692, -0.0440, -0.1767,
        -0.1616, -0.2183, +0.1853, -0.0618, +0.1210, -0.2178, +0.1066, -0.3849,
        -0.2628, +0.1444, +0.2814, -0.2963, +0.0673, +0.0983, +0.0442, +0.0020
    ],
    [
        -0.0978, +0.2645, -0.3750, +0.2824, -0.3906, -0.0070, +0.1920, +0.0911,
        -0.0510, -0.1050, -0.2411, -0.2135, +0.0784, +0.3348, -0.0396, -0.4209,
        -0.0686, -0.2212, +0.3039, -0.4649, -0.0692, -0.5387, +0.0479, -0.4205,
        -0.2557, -0.1031, +0.1378, -0.3875, -0.1900, -0.0253, +0.1212, -0.4374
    ],
    [
        -0.1067, +0.1545, +0.2016, -0.0620, -0.1419, -0.0661, -0.1224, -0.0560,
        +0.1045, -0.2062, -0.2551, +0.2440, -0.1116, +0.1544, -0.2324, +0.0999,
        -0.1832, -0.1226, -0.1774, +0.0629, -0.1170, -0.1375, +0.0839, +0.2029,
        +0.0551, +0.0359, +0.0967, +0.2290, -0.0312, -0.1228, +0.2831, +0.1785
    ],
    [
        -0.1420, +0.1163, +0.0488, -0.0011, -0.1311, -0.1558, -0.0766, -0.0088,
        +0.1877, -0.1547, +0.1304, +0.0347, +0.1132, +0.2750, -0.0574, +0.0080,
        -0.2256, -0.0951, +0.1987, +0.2256, +0.0270, -0.0155, +0.0636, +0.0372,
        +0.2483, -0.1469, -0.2010, -0.0994, -0.1731, +0.0224, +0.0085, -0.1891
    ],
    [
        +0.1037, +0.0015, -0.1525, -0.0444, -0.3130, -0.0318, +0.2370, -0.1492,
        -0.4707, -0.0023, +0.0884, +0.1722, -0.0421, +0.0858, -0.1036, -0.5701,
        +0.1249, -0.2643, -0.0203, -0.1380, +0.0973, -0.2060, +0.1806, +0.3054,
        -0.6548, -0.3282, -0.2969, -0.3984, -0.0448, -0.1802, +0.3282, -0.1891
    ],
    [
        -0.1116, +0.3646, -0.0542, +0.3672, -0.4207, +0.2700, +0.3827, -0.0599,
        -0.3415, -0.2832, -0.0345, +0.1987, +0.0669, +0.1301, -0.3806, -0.2981,
        -0.1917, -0.2028, +0.1687, -0.2010, +0.3607, -0.0199, +0.2971, +0.0390,
        +0.0895, -0.3088, +0.0169, -0.1333, +0.0738, +0.2161, -0.1207, -0.3352
    ],
    [
        -0.0134, +0.3853, -0.2106, +0.1996, -0.2277, -0.0971, +0.0917, -0.2901,
        -0.2493, +0.0295, -0.1438, -0.1902, -0.0074, +0.2240, -0.0277, -0.4374,
        +0.0749, -0.1779, +0.2687, -0.4093, -0.0042, -0.5023, -0.1169, -0.3157,
        +0.0061, +0.0270, +0.0204, -0.4626, -0.1717, -0.2126, +0.1335, -0.5028
    ],
    [
        -0.0813, +0.1958, -0.4203, +0.3027, -0.3896, -0.1201, -0.0383, -0.1807,
        -0.4834, -0.3672, -0.3664, +0.2401, -0.0114, -0.0852, -0.2220, -0.1953,
        +0.0773, -0.0048, +0.1560, -0.1524, +0.0772, -0.2740, +0.1346, -0.3171,
        -0.0648, +0.1633, +0.2050, -0.1560, +0.0270, +0.3009, -0.2798, -0.0756
    ],
    [
        -0.1754, +0.1428, +0.2527, -0.2624, -0.1126, -0.0014, +0.1030, -0.2716,
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    [
        +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_final_b = np.array([+0.0352])
# yapf: enable

if __name__ == "__main__":
  main()