ray.rllib.policy.sample_batch.MultiAgentBatch

class ray.rllib.policy.sample_batch.MultiAgentBatch(policy_batches: Dict[str, SampleBatch], env_steps: int)

A batch of experiences from multiple agents in the environment.

policy_batches

Dict mapping policy IDs to SampleBatches of experiences.

Type:

Dict[PolicyID, SampleBatch]

count

The number of env steps in this batch.

Methods

__init__	Initialize a MultiAgentBatch instance.
agent_steps	The number of agent steps (there are >= 1 agent steps per env step).
as_multi_agent	Simply returns self (already a MultiAgentBatch).
compress	Compresses each policy batch (per column) in place.
copy	Deep-copies self into a new MultiAgentBatch.
decompress_if_needed	Decompresses each policy batch (per column), if already compressed.
env_steps	The number of env steps (there are >= 1 agent steps per env step).
size_bytes	returns: The overall size in bytes of all policy batches (all columns).
timeslices	Returns k-step batches holding data for each agent at those steps.
to_device	TODO: transfer batch to given device as framework tensor.
wrap_as_needed	Returns SampleBatch or MultiAgentBatch, depending on given policies.