mirror of
https://github.com/vale981/ray
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92 lines
3.2 KiB
Python
92 lines
3.2 KiB
Python
import logging
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from typing import Type
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from ray.rllib.agents.a3c.a3c_tf_policy import A3CTFPolicy
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from ray.rllib.agents.trainer import Trainer, with_common_config
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from ray.rllib.evaluation.worker_set import WorkerSet
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from ray.rllib.execution.rollout_ops import AsyncGradients
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from ray.rllib.execution.train_ops import ApplyGradients
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from ray.rllib.execution.metric_ops import StandardMetricsReporting
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from ray.rllib.policy.policy import Policy
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.typing import TrainerConfigDict
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from ray.util.iter import LocalIterator
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logger = logging.getLogger(__name__)
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# yapf: disable
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# __sphinx_doc_begin__
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DEFAULT_CONFIG = with_common_config({
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# Should use a critic as a baseline (otherwise don't use value baseline;
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# required for using GAE).
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"use_critic": True,
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# If true, use the Generalized Advantage Estimator (GAE)
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# with a value function, see https://arxiv.org/pdf/1506.02438.pdf.
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"use_gae": True,
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# Size of rollout batch
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"rollout_fragment_length": 10,
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# GAE(gamma) parameter
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"lambda": 1.0,
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# Max global norm for each gradient calculated by worker
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"grad_clip": 40.0,
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# Learning rate
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"lr": 0.0001,
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# Learning rate schedule
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"lr_schedule": None,
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# Value Function Loss coefficient
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"vf_loss_coeff": 0.5,
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# Entropy coefficient
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"entropy_coeff": 0.01,
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# Entropy coefficient schedule
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"entropy_coeff_schedule": None,
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# Min time per iteration
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"min_iter_time_s": 5,
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# Workers sample async. Note that this increases the effective
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# rollout_fragment_length by up to 5x due to async buffering of batches.
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"sample_async": True,
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})
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# __sphinx_doc_end__
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# yapf: enable
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class A3CTrainer(Trainer):
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@classmethod
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@override(Trainer)
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def get_default_config(cls) -> TrainerConfigDict:
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return DEFAULT_CONFIG
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@override(Trainer)
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def validate_config(self, config: TrainerConfigDict) -> None:
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super().validate_config(config)
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if config["entropy_coeff"] < 0:
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raise ValueError("`entropy_coeff` must be >= 0.0!")
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if config["num_workers"] <= 0 and config["sample_async"]:
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raise ValueError("`num_workers` for A3C must be >= 1!")
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@override(Trainer)
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def get_default_policy_class(self,
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config: TrainerConfigDict) -> Type[Policy]:
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if config["framework"] == "torch":
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from ray.rllib.agents.a3c.a3c_torch_policy import \
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A3CTorchPolicy
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return A3CTorchPolicy
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else:
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return A3CTFPolicy
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@staticmethod
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@override(Trainer)
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def execution_plan(workers: WorkerSet, config: TrainerConfigDict,
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**kwargs) -> LocalIterator[dict]:
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assert len(kwargs) == 0, (
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"A3C execution_plan does NOT take any additional parameters")
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# For A3C, compute policy gradients remotely on the rollout workers.
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grads = AsyncGradients(workers)
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# Apply the gradients as they arrive. We set update_all to False so
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# that only the worker sending the gradient is updated with new
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# weights.
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train_op = grads.for_each(ApplyGradients(workers, update_all=False))
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return StandardMetricsReporting(train_op, workers, config)
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