cd375ab209
* update per-algorithm READMEs to reflect new way of running algorithms * adding a link to repo-wide README * updated README files and deepq.train_cartpole example
31 lines
646 B
Python
31 lines
646 B
Python
import gym
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from baselines import deepq
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def callback(lcl, _glb):
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# stop training if reward exceeds 199
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is_solved = lcl['t'] > 100 and sum(lcl['episode_rewards'][-101:-1]) / 100 >= 199
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return is_solved
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def main():
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env = gym.make("CartPole-v0")
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act = deepq.learn(
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env,
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network='mlp',
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lr=1e-3,
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total_timesteps=100000,
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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 cartpole_model.pkl")
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act.save("cartpole_model.pkl")
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if __name__ == '__main__':
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main()
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