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57544b1ff9
- This PR completes any previously missing PyTorch Model counterparts to TFModels in examples/models. - It also makes sure, all example scripts in the rllib/examples folder are tested for both frameworks and learn the given task (this is often currently not checked) using a --as-test flag in connection with a --stop-reward.
65 lines
2.4 KiB
Python
65 lines
2.4 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 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("--run", type=str, default="PPO") # try PG, PPO, IMPALA
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parser.add_argument("--torch", action="store_true")
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parser.add_argument("--num-cpus", type=int, default=0)
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parser.add_argument("--as-test", action="store_true")
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parser.add_argument("--stop-iters", type=int, default=200)
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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=200)
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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.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.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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"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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"use_pytorch": args.torch,
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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)
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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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