def execution_plan(workers: WorkerSet,
config: TrainerConfigDict) -> LocalIterator[dict]:
rollouts = ParallelRollouts(workers, mode="async")
# Collect batches for the trainable policies.
rollouts = rollouts.for_each(
SelectExperiences(workers.trainable_policies()))
# Return training metrics.
return StandardMetricsReporting(rollouts, workers, config)
def execution_plan(workers: WorkerSet,
config: TrainerConfigDict) -> LocalIterator[dict]:
"""Execution plan of the PPO algorithm. Defines the distributed dataflow.
Args:
workers (WorkerSet): The WorkerSet for training the Polic(y/ies)
of the Trainer.
config (TrainerConfigDict): The trainer's configuration dict.
Returns:
LocalIterator[dict]: The Policy class to use with PPOTrainer.
If None, use `default_policy` provided in build_trainer().
"""
rollouts = ParallelRollouts(workers, mode="bulk_sync")
# Collect batches for the trainable policies.
rollouts = rollouts.for_each(
SelectExperiences(workers.trainable_policies()))
# Concatenate the SampleBatches into one.
rollouts = rollouts.combine(
ConcatBatches(
min_batch_size=config["train_batch_size"],
count_steps_by=config["multiagent"]["count_steps_by"],
))
# Standardize advantages.
rollouts = rollouts.for_each(StandardizeFields(["advantages"]))
# Perform one training step on the combined + standardized batch.
if config["simple_optimizer"]:
train_op = rollouts.for_each(
TrainOneStep(
workers,
num_sgd_iter=config["num_sgd_iter"],
sgd_minibatch_size=config["sgd_minibatch_size"]))
else:
train_op = rollouts.for_each(
TrainTFMultiGPU(
workers,
sgd_minibatch_size=config["sgd_minibatch_size"],
num_sgd_iter=config["num_sgd_iter"],
num_gpus=config["num_gpus"],
rollout_fragment_length=config["rollout_fragment_length"],
num_envs_per_worker=config["num_envs_per_worker"],
train_batch_size=config["train_batch_size"],
shuffle_sequences=config["shuffle_sequences"],
_fake_gpus=config["_fake_gpus"],
framework=config.get("framework")))
# Update KL after each round of training.
train_op = train_op.for_each(lambda t: t[1]).for_each(UpdateKL(workers))
# Warn about bad reward scales and return training metrics.
return StandardMetricsReporting(train_op, workers, config) \
.for_each(lambda result: warn_about_bad_reward_scales(config, result))
def execution_plan(workers: WorkerSet, config: TrainerConfigDict,
**kwargs) -> LocalIterator[dict]:
assert len(kwargs) == 0, (
"PPO execution_plan does NOT take any additional parameters")
rollouts = ParallelRollouts(workers, mode="bulk_sync")
# Collect batches for the trainable policies.
rollouts = rollouts.for_each(
SelectExperiences(workers.trainable_policies()))
# Concatenate the SampleBatches into one.
rollouts = rollouts.combine(
ConcatBatches(
min_batch_size=config["train_batch_size"],
count_steps_by=config["multiagent"]["count_steps_by"],
))
# Standardize advantages.
rollouts = rollouts.for_each(StandardizeFields(["advantages"]))
# Perform one training step on the combined + standardized batch.
if config["simple_optimizer"]:
train_op = rollouts.for_each(
TrainOneStep(workers,
num_sgd_iter=config["num_sgd_iter"],
sgd_minibatch_size=config["sgd_minibatch_size"]))
else:
train_op = rollouts.for_each(
MultiGPUTrainOneStep(
workers=workers,
sgd_minibatch_size=config["sgd_minibatch_size"],
num_sgd_iter=config["num_sgd_iter"],
num_gpus=config["num_gpus"],
shuffle_sequences=config["shuffle_sequences"],
_fake_gpus=config["_fake_gpus"],
framework=config.get("framework")))
# Update KL after each round of training.
train_op = train_op.for_each(lambda t: t[1]).for_each(
UpdateKL(workers))
# Warn about bad reward scales and return training metrics.
return StandardMetricsReporting(train_op, workers, config) \
.for_each(lambda result: warn_about_bad_reward_scales(
config, result))
