RoboschoolWalker2d-v1.py

# Taken from the Roboschool Agent Zoo
import gym, roboschool
import numpy as np

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 get_env_and_policy():
    env = gym.make("RoboschoolWalker2d-v1")
    pi = SmallReactivePolicy(env.observation_space, env.action_space)
    return env, pi

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],
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])

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_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])

if __name__=="__main__":
    demo_run()