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https://github.com/bulletphysics/bullet3
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42 lines
912 B
Python
42 lines
912 B
Python
import gym
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from envs.bullet.racecarZEDGymEnv import RacecarZEDGymEnv
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from baselines import deepq
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import datetime
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def callback(lcl, glb):
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# stop training if reward exceeds 199
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total = sum(lcl['episode_rewards'][-101:-1]) / 100
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totalt = lcl['t']
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is_solved = totalt > 2000 and total >= -50
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return is_solved
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def main():
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env = RacecarZEDGymEnv(renders=False)
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model = deepq.models.cnn_to_mlp(
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convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
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hiddens=[256],
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dueling=False
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)
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act = deepq.learn(
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env,
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q_func=model,
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lr=1e-3,
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max_timesteps=10000,
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buffer_size=50000,
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exploration_fraction=0.1,
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exploration_final_eps=0.02,
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print_freq=10,
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callback=callback
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)
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print("Saving model to racecar_zed_model.pkl")
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act.save("racecar_zed_model.pkl")
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if __name__ == '__main__':
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main()
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