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dd70720578
* bulk rename * deprecation warn * update doc * update fig * line length * rename * make pytest comptaible * fix test * fi sys * rename * wip * fix more * lint * update svg * comments * lint * fix use of batch steps
75 lines
2.9 KiB
Python
75 lines
2.9 KiB
Python
import math
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from ray.rllib.agents.a3c.a3c import DEFAULT_CONFIG as A3C_CONFIG, \
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validate_config, get_policy_class
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from ray.rllib.optimizers import SyncSamplesOptimizer, MicrobatchOptimizer
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from ray.rllib.agents.a3c.a3c_tf_policy import A3CTFPolicy
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from ray.rllib.agents.trainer_template import build_trainer
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from ray.rllib.utils import merge_dicts
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from ray.rllib.utils.experimental_dsl import (
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ParallelRollouts, ConcatBatches, ComputeGradients, AverageGradients,
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ApplyGradients, TrainOneStep, StandardMetricsReporting)
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A2C_DEFAULT_CONFIG = merge_dicts(
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A3C_CONFIG,
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{
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"rollout_fragment_length": 20,
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"min_iter_time_s": 10,
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"sample_async": False,
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# A2C supports microbatching, in which we accumulate gradients over
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# batch of this size until the train batch size is reached. This allows
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# training with batch sizes much larger than can fit in GPU memory.
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# To enable, set this to a value less than the train batch size.
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"microbatch_size": None,
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},
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)
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def choose_policy_optimizer(workers, config):
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if config["microbatch_size"]:
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return MicrobatchOptimizer(
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workers,
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train_batch_size=config["train_batch_size"],
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microbatch_size=config["microbatch_size"])
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else:
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return SyncSamplesOptimizer(
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workers, train_batch_size=config["train_batch_size"])
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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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rollouts = ParallelRollouts(workers, mode="bulk_sync")
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if config["microbatch_size"]:
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num_microbatches = math.ceil(
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config["train_batch_size"] / config["microbatch_size"])
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# In microbatch mode, we want to compute gradients on experience
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# microbatches, average a number of these microbatches, and then apply
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# the averaged gradient in one SGD step. This conserves GPU memory,
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# allowing for extremely large experience batches to be used.
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train_op = (
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rollouts.combine(
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ConcatBatches(min_batch_size=config["microbatch_size"]))
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.for_each(ComputeGradients(workers)) # (grads, info)
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.batch(num_microbatches) # List[(grads, info)]
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.for_each(AverageGradients()) # (avg_grads, info)
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.for_each(ApplyGradients(workers)))
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else:
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# In normal mode, we execute one SGD step per each train batch.
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train_op = rollouts \
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.combine(ConcatBatches(
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min_batch_size=config["train_batch_size"])) \
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.for_each(TrainOneStep(workers))
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return StandardMetricsReporting(train_op, workers, config)
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A2CTrainer = build_trainer(
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name="A2C",
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default_config=A2C_DEFAULT_CONFIG,
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default_policy=A3CTFPolicy,
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get_policy_class=get_policy_class,
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make_policy_optimizer=choose_policy_optimizer,
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validate_config=validate_config,
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execution_plan=execution_plan)
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