mirror of
https://github.com/vale981/ray
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91 lines
3 KiB
Python
91 lines
3 KiB
Python
"""Example of specifying an autoregressive action distribution.
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In an action space with multiple components (e.g., Tuple(a1, a2)), you might
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want a2 to be sampled based on the sampled value of a1, i.e.,
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a2_sampled ~ P(a2 | a1_sampled, obs). Normally, a1 and a2 would be sampled
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independently.
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To do this, you need both a custom model that implements the autoregressive
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pattern, and a custom action distribution class that leverages that model.
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This examples shows both.
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"""
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import argparse
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import os
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import ray
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from ray import tune
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from ray.rllib.examples.env.correlated_actions_env import CorrelatedActionsEnv
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from ray.rllib.examples.models.autoregressive_action_model import \
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AutoregressiveActionModel, TorchAutoregressiveActionModel
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from ray.rllib.examples.models.autoregressive_action_dist import \
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BinaryAutoregressiveDistribution, TorchBinaryAutoregressiveDistribution
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from ray.rllib.models import ModelCatalog
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from ray.rllib.utils.test_utils import check_learning_achieved
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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="PPO",
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help="The RLlib-registered algorithm to use.")
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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("--num-cpus", type=int, default=0)
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parser.add_argument(
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"--as-test",
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action="store_true",
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help="Whether this script should be run as a test: --stop-reward must "
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"be achieved within --stop-timesteps AND --stop-iters.")
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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=200.0,
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help="Reward at which we stop training.")
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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init(num_cpus=args.num_cpus or None)
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ModelCatalog.register_custom_model(
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"autoregressive_model", TorchAutoregressiveActionModel
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if args.framework == "torch" else AutoregressiveActionModel)
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ModelCatalog.register_custom_action_dist(
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"binary_autoreg_dist", TorchBinaryAutoregressiveDistribution
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if args.framework == "torch" else BinaryAutoregressiveDistribution)
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config = {
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"env": CorrelatedActionsEnv,
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"gamma": 0.5,
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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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"model": {
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"custom_model": "autoregressive_model",
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"custom_action_dist": "binary_autoreg_dist",
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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, stop=stop, config=config, verbose=1)
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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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