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* bulk rename * deprecation warn * update doc * update fig * line length * rename * make pytest comptaible * fix test * fi sys * rename * wip * fix more * lint * update svg * comments * lint * fix use of batch steps
49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
"""Example of using training on CartPole."""
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import argparse
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import ray
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from ray import tune
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from ray.rllib.contrib.alpha_zero.models.custom_torch_models import DenseModel
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from ray.rllib.contrib.alpha_zero.environments.cartpole import CartPole
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from ray.rllib.models.catalog import ModelCatalog
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--num-workers", default=6, type=int)
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parser.add_argument("--training-iteration", default=10000, type=int)
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parser.add_argument("--ray-num-cpus", default=7, type=int)
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args = parser.parse_args()
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ray.init(num_cpus=args.ray_num_cpus)
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ModelCatalog.register_custom_model("dense_model", DenseModel)
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tune.run(
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"contrib/AlphaZero",
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stop={"training_iteration": args.training_iteration},
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max_failures=0,
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config={
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"env": CartPole,
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"num_workers": args.num_workers,
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"rollout_fragment_length": 50,
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"train_batch_size": 500,
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"sgd_minibatch_size": 64,
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"lr": 1e-4,
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"num_sgd_iter": 1,
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"mcts_config": {
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"puct_coefficient": 1.5,
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"num_simulations": 100,
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"temperature": 1.0,
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"dirichlet_epsilon": 0.20,
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"dirichlet_noise": 0.03,
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"argmax_tree_policy": False,
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"add_dirichlet_noise": True,
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},
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"ranked_rewards": {
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"enable": True,
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},
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"model": {
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"custom_model": "dense_model",
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},
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},
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)
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