ray.rllib.policy.sample_batch.MultiAgentBatch.timeslices

MultiAgentBatch.timeslices(k: int) → List[MultiAgentBatch]

Returns k-step batches holding data for each agent at those steps.

For examples, suppose we have agent1 observations [a1t1, a1t2, a1t3], for agent2, [a2t1, a2t3], and for agent3, [a3t3] only.

Calling timeslices(1) would return three MultiAgentBatches containing [a1t1, a2t1], [a1t2], and [a1t3, a2t3, a3t3].

Calling timeslices(2) would return two MultiAgentBatches containing [a1t1, a1t2, a2t1], and [a1t3, a2t3, a3t3].

This method is used to implement “lockstep” replay mode. Note that this method does not guarantee each batch contains only data from a single unroll. Batches might contain data from multiple different envs.