text: #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 pybulletgym.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])
  
      @staticmethod
      def act(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("InvertedPendulumSwingupPyBulletEnv-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(0.02)
              a = pi.act(obs)
              obs, r, done, _ = env.step(a)
              score += r
              frame += 1
              still_open = env.render("human")
              if still_open is None:
                  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([
  [ +0.5877, -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],
  [ +0.2540, +0.4159, -0.4193, +0.4756, -0.5615, -0.0777, -0.1692, -0.2047, -0.6844, -0.2723, +0.0727, -0.1912, +0.0989, +0.1546, +0.4719, -0.2639, -0.1997, +0.2235, +0.5461, +0.2992, -1.6747, -0.3055, -0.7582, +0.0934, +0.2088, -0.2527, +0.2810, -0.0126, -0.2710, -0.7904, -0.2154, -0.5613],
  [ -0.2470, +0.1133, -0.1563, -0.4254, +0.5442, +0.1291, +0.2176, +0.0374, -0.8939, -0.4140, +0.1537, -0.1740, +0.9369, -0.0037, -0.4261, -0.7104, -0.7777, +0.1905, -0.5320, -0.1168, +0.0347, -0.0454, -0.3947, -0.6101, +0.2560, -0.2748, -0.0115, +0.0442, -0.0840, -0.0564, -0.2105, -0.3223],
  [ -0.0404, +0.1026, -0.3563, -0.2962, -0.7801, -0.2794, -0.1065, +0.4522, +0.2426, +0.1916, -0.8589, +0.1918, +0.4101, -0.1290, -0.3302, +0.1000, -0.0601, -0.2014, -0.7935, +0.4843, -0.6731, +0.2180, +0.1019, -0.2928, +0.0366, -0.4442, -0.0406, -0.4545, -0.2187, +0.1910, +0.9510, -0.1191],
  [ +0.0344, -0.6187, -0.1423, +0.3670, -0.7356, -0.0288, -0.1769, -0.9789, -1.3008, -0.4707, +0.1346, -0.1823, -0.2180, -0.4896, -0.0455, -0.7968, -0.3335, +0.6360, +0.2356, -0.0207, -0.2652, +0.2302, -0.3929, +0.2243, +0.6438, +0.7061, +0.2904, +0.1324, -0.4476, +0.2047, -0.6898, -0.6214],
  [ -0.0215, -0.7005, -0.0687, +0.0166, -0.3514, -0.0745, +0.0922, +0.5453, +0.0969, +0.0386, -0.0103, +0.1984, -1.0903, -0.2738, -0.4855, +0.3083, +0.2451, +0.5611, -0.3741, +0.2794, +0.0953, -0.3711, -0.1832, -0.2603, +0.3729, +0.2859, -0.3258, -1.2615, +0.0928, -0.1043, +0.1818, +0.1052],
  [ +0.2063, -0.4528, -0.0057, -0.0972, -0.1732, +0.0062, -0.5985, +0.2504, -0.3243, -0.5488, -0.1981, +0.0969, -0.8003, -0.2163, -0.6253, +0.1420, -0.1593, +0.1623, -0.0719, +0.0738, +0.3514, -0.4224, +0.0098, -0.0067, +0.2754, +0.1454, -0.3292, -0.0407, -0.7088, +0.7650, -0.0182, -0.0452],
  [ -0.1059, -0.6218, +0.1371, -0.2479, -0.0653, -0.0035, -0.3983, +0.0243, -0.2188, -0.3608, +0.3230, -0.6048, +0.0848, -0.9398, +0.1182, -0.2141, +0.0755, +0.1749, -0.5544, +0.0777, +0.0288, -0.4650, -0.1328, +0.0272, -0.1134, -0.5497, -0.7305, +0.2035, -0.1138, -0.3764, -0.1077, -0.0619],
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  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([
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  [ +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])
  
  if __name__ == "__main__":
      main()
  