RoboschoolHalfCheetah-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("RoboschoolHalfCheetah-v1")
    pi = SmallReactivePolicy(env.observation_space, env.action_space)
    return env, pi

weights_dense1_w = np.array([
[ -0.4105, +0.5540, +0.2791, +0.4953, -0.0309, +0.8480, +0.1630, +0.0630, +0.7107, +0.0597, +0.2675, -0.1931, -0.2436, +0.2909, +0.3563, +0.4564, -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_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([
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[ +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],
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[ +0.4368, +0.1699, +0.3433, -0.4160, -0.3043, +0.1180],
[ -0.1328, -0.6681, +0.4017, +0.3457, +0.1843, -0.0134],
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[ -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])

if __name__=="__main__":
    demo_run()