ray.rllib.execution.train_ops.train_one_step#

ray.rllib.execution.train_ops.train_one_step(algorithm, train_batch, policies_to_train=None) → Dict[source]#

Function that improves the all policies in train_batch on the local worker.

from ray.rllib.execution.rollout_ops import synchronous_parallel_sample
algo = [...]
train_batch = synchronous_parallel_sample(algo.env_runner_group)
# This trains the policy on one batch.
print(train_one_step(algo, train_batch)))

{"default_policy": ...}

Updates the NUM_ENV_STEPS_TRAINED and NUM_AGENT_STEPS_TRAINED counters as well as the LEARN_ON_BATCH_TIMER timer of the algorithm object.