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https://github.com/wassname/ray.git
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Eric Liang
02583a8598
This implements some of the renames proposed in #4813 We leave behind backwards-compatibility aliases for *PolicyGraph and SampleBatch.
98 lines
4.1 KiB
Python
98 lines
4.1 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from ray.rllib.agents.trainer import Trainer
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from ray.rllib.optimizers import SyncSamplesOptimizer
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from ray.rllib.utils.annotations import override, DeveloperAPI
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@DeveloperAPI
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def build_trainer(name,
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default_policy,
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default_config=None,
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make_policy_optimizer=None,
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validate_config=None,
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get_policy_class=None,
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before_train_step=None,
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after_optimizer_step=None,
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after_train_result=None):
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"""Helper function for defining a custom trainer.
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Arguments:
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name (str): name of the trainer (e.g., "PPO")
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default_policy (cls): the default Policy class to use
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default_config (dict): the default config dict of the algorithm,
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otherwises uses the Trainer default config
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make_policy_optimizer (func): optional function that returns a
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PolicyOptimizer instance given
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(local_evaluator, remote_evaluators, config)
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validate_config (func): optional callback that checks a given config
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for correctness. It may mutate the config as needed.
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get_policy_class (func): optional callback that takes a config and
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returns the policy class to override the default with
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before_train_step (func): optional callback to run before each train()
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call. It takes the trainer instance as an argument.
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after_optimizer_step (func): optional callback to run after each
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step() call to the policy optimizer. It takes the trainer instance
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and the policy gradient fetches as arguments.
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after_train_result (func): optional callback to run at the end of each
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train() call. It takes the trainer instance and result dict as
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arguments, and may mutate the result dict as needed.
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Returns:
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a Trainer instance that uses the specified args.
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"""
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if name.endswith("Trainer"):
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raise ValueError("Algorithm name should not include *Trainer suffix",
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name)
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class trainer_cls(Trainer):
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_name = name
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_default_config = default_config or Trainer.COMMON_CONFIG
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_policy = default_policy
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def _init(self, config, env_creator):
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if validate_config:
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validate_config(config)
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if get_policy_class is None:
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policy = default_policy
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else:
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policy = get_policy_class(config)
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self.local_evaluator = self.make_local_evaluator(
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env_creator, policy)
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self.remote_evaluators = self.make_remote_evaluators(
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env_creator, policy, config["num_workers"])
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if make_policy_optimizer:
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self.optimizer = make_policy_optimizer(
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self.local_evaluator, self.remote_evaluators, config)
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else:
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optimizer_config = dict(
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config["optimizer"],
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**{"train_batch_size": config["train_batch_size"]})
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self.optimizer = SyncSamplesOptimizer(self.local_evaluator,
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self.remote_evaluators,
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**optimizer_config)
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@override(Trainer)
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def _train(self):
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if before_train_step:
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before_train_step(self)
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prev_steps = self.optimizer.num_steps_sampled
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fetches = self.optimizer.step()
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if after_optimizer_step:
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after_optimizer_step(self, fetches)
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res = self.collect_metrics()
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res.update(
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timesteps_this_iter=self.optimizer.num_steps_sampled -
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prev_steps,
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info=res.get("info", {}))
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if after_train_result:
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after_train_result(self, res)
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return res
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trainer_cls.__name__ = name + "Trainer"
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trainer_cls.__qualname__ = name + "Trainer"
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return trainer_cls
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