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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], 256)
assert weights_dense2_w.shape == (256, 128)
assert weights_final_w.shape == (128, action_space.shape[0])
def act(self, ob):
ob[0] += -1.4 + 0.8
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 demo_run():
env = gym.make("RoboschoolHumanoidFlagrun-v1")
pi = SmallReactivePolicy(env.observation_space, env.action_space)
while 1:
frame = 0
score = 0
restart_delay = 0
obs = env.reset()
while 1:
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
weights_dense1_w = np.array([
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[ -0.0371, +0.2498, +0.0943, -0.0595, -0.1622, -0.0368, -0.0410, -0.0350, +0.1649, -0.1391, +0.1116, +0.1493, +0.4497, +0.0689, +0.1345, +0.0235, -0.1134],
[ +0.0242, +0.3250, -0.0431, -0.0669, +0.0768, +0.3238, -0.1787, -0.0234, -0.2246, -0.0746, -0.0270, -0.0981, -0.0545, -0.0218, -0.0163, +0.1178, -0.0560],
[ +0.0480, +0.0810, +0.0106, +0.0650, -0.0826, +0.0256, -0.2193, -0.1972, +0.0669, +0.1536, +0.1926, +0.2480, -0.3976, -0.0610, +0.1993, -0.0446, +0.0406],
[ -0.1425, +0.0553, +0.0507, +0.0564, +0.0144, -0.0179, +0.0147, -0.1410, -0.0849, +0.0165, -0.1263, +0.0968, +0.0568, +0.1290, -0.0628, -0.1092, +0.1861],
[ +0.0671, +0.0607, +0.0758, -0.0148, +0.0791, +0.1348, -0.2942, -0.1454, -0.0840, +0.1462, +0.1797, +0.0763, +0.0768, +0.0266, +0.0433, -0.1881, +0.0711],
[ +0.0370, -0.0775, -0.0157, -0.0413, -0.0182, +0.3259, -0.1009, -0.2658, +0.1276, +0.0289, -0.0747, +0.1413, +0.0705, -0.0123, +0.0475, +0.2272, -0.0674],
[ +0.0655, +0.0401, -0.0795, +0.1287, +0.0759, +0.0857, -0.0211, +0.0354, +0.0308, +0.0311, -0.2132, -0.0911, -0.3604, +0.0304, -0.1964, -0.0431, +0.0475],
[ -0.0644, -0.0094, -0.1486, +0.3191, -0.2175, -0.1737, -0.0700, +0.1518, -0.0068, +0.4075, +0.0318, +0.1678, -0.1474, +0.0902, +0.0219, +0.2162, -0.0012],
[ +0.1667, +0.1585, +0.2017, +0.2685, -0.0871, -0.2013, -0.0570, +0.0754, +0.1046, +0.1687, -0.0567, -0.0313, -0.1774, -0.0854, +0.0939, +0.1894, +0.0624],
[ +0.0932, -0.0126, +0.0624, +0.3765, +0.0937, -0.0058, +0.0455, +0.1864, -0.0820, -0.1547, +0.1103, +0.0415, +0.0349, -0.0023, +0.3515, +0.0308, +0.0940],
[ -0.0513, +0.0717, -0.0269, -0.0405, -0.0152, -0.0331, +0.1988, -0.0502, +0.0477, -0.1251, +0.2972, -0.0378, +0.2606, -0.0454, +0.2086, +0.0971, -0.0640]
])
weights_final_b = np.array([ -0.0082, -0.0230, +0.0671, +0.1373, +0.2278, +0.0230, +0.0218, +0.2155, +0.0044, +0.3212, +0.1209, +0.2365, +0.0112, +0.0689, -0.1482, +0.0108, +0.0086])
if __name__=="__main__":
demo_run()