mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
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import argparse
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from gym.spaces import Box, Discrete
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import os
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from ray.rllib.examples.env.action_mask_env import ActionMaskEnv
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from ray.rllib.examples.models.action_mask_model import \
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ActionMaskModel, TorchActionMaskModel
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--run",
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type=str,
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default="APPO",
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help="The RLlib-registered algorithm to use.")
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parser.add_argument("--num-cpus", type=int, default=0)
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parser.add_argument(
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"--framework",
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choices=["tf", "tf2", "tfe", "torch"],
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default="tf",
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help="The DL framework specifier.")
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parser.add_argument("--eager-tracing", action="store_true")
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parser.add_argument(
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"--stop-iters",
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type=int,
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default=200,
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help="Number of iterations to train.")
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parser.add_argument(
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"--stop-timesteps",
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type=int,
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default=100000,
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help="Number of timesteps to train.")
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parser.add_argument(
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"--stop-reward",
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type=float,
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default=80.0,
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help="Reward at which we stop training.")
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parser.add_argument(
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"--local-mode",
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action="store_true",
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help="Init Ray in local mode for easier debugging.")
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if __name__ == "__main__":
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import ray
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from ray import tune
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args = parser.parse_args()
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ray.init(num_cpus=args.num_cpus or None, local_mode=args.local_mode)
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config = {
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"env": ActionMaskEnv,
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"env_config": {
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"action_space": Discrete(100),
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"observation_space": Box(-1.0, 1.0, (5, )),
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},
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"model": {
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"custom_model": ActionMaskModel
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if args.framework != "torch" else TorchActionMaskModel,
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},
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# Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
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"num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
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"framework": args.framework,
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# Run with tracing enabled for tfe/tf2?
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"eager_tracing": args.eager_tracing,
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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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ray.shutdown()
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