mirror of
https://github.com/vale981/ray
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50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
import argparse
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import ray
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from ray import tune
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from ray.rllib.examples.models.trajectory_view_utilizing_models import \
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FrameStackingCartPoleModel, TorchFrameStackingCartPoleModel
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from ray.rllib.models.catalog import ModelCatalog
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from ray.rllib.utils.framework import try_import_tf
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from ray.rllib.utils.test_utils import check_learning_achieved
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tf1, tf, tfv = try_import_tf()
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parser = argparse.ArgumentParser()
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parser.add_argument("--run", type=str, default="PPO")
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parser.add_argument(
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"--framework", choices=["tf2", "tf", "tfe", "torch"], default="tf")
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parser.add_argument("--as-test", action="store_true")
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parser.add_argument("--stop-iters", type=int, default=50)
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parser.add_argument("--stop-timesteps", type=int, default=100000)
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parser.add_argument("--stop-reward", type=float, default=150.0)
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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init(num_cpus=3)
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ModelCatalog.register_custom_model(
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"frame_stack_model", FrameStackingCartPoleModel
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if args.framework != "torch" else TorchFrameStackingCartPoleModel)
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config = {
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"env": "CartPole-v0",
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"model": {
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"custom_model": "frame_stack_model",
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"custom_model_config": {
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"num_frames": 4,
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}
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},
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"framework": args.framework,
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}
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stop = {
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"training_iteration": args.stop_iters,
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"timesteps_total": args.stop_timesteps,
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"episode_reward_mean": args.stop_reward,
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}
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results = tune.run(args.run, config=config, stop=stop, verbose=2)
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if args.as_test:
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check_learning_achieved(results, args.stop_reward)
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ray.shutdown()
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