mirror of
https://github.com/vale981/ray
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87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
import logging
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from ray.rllib.agents.a3c.a3c_tf_policy import A3CTFPolicy
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from ray.rllib.agents.trainer import with_common_config
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from ray.rllib.agents.trainer_template import build_trainer
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from ray.rllib.optimizers import AsyncGradientsOptimizer
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from ray.rllib.utils.experimental_dsl import (AsyncGradients, ApplyGradients,
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StandardMetricsReporting)
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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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"sample_batch_size": 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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# 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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# sample_batch_size 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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def get_policy_class(config):
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if config["use_pytorch"]:
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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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def validate_config(config):
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if config["entropy_coeff"] < 0:
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raise DeprecationWarning("entropy_coeff must be >= 0")
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if config["sample_async"] and config["use_pytorch"]:
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config["sample_async"] = False
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logger.warning(
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"The sample_async option is not supported with use_pytorch: "
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"Multithreading can be lead to crashes if used with pytorch.")
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def make_async_optimizer(workers, config):
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return AsyncGradientsOptimizer(workers, **config["optimizer"])
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# Experimental distributed execution impl; enable with "use_exec_api": True.
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def execution_plan(workers, config):
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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 that
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# only the worker sending the gradient is updated with new 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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A3CTrainer = build_trainer(
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name="A3C",
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default_config=DEFAULT_CONFIG,
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default_policy=A3CTFPolicy,
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get_policy_class=get_policy_class,
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validate_config=validate_config,
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make_policy_optimizer=make_async_optimizer,
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execution_plan=execution_plan)
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