def postprocess_trajectory(
policy: Policy,
sample_batch: SampleBatch,
other_agent_batches: Optional[Dict[AgentID, SampleBatch]] = None,
episode: Optional[Episode] = None,
) -> SampleBatch:
"""Postprocesses a trajectory and returns the processed trajectory.
The trajectory contains only data from one episode and from one agent.
- If `config.batch_mode=truncate_episodes` (default), sample_batch may
contain a truncated (at-the-end) episode, in case the
`config.rollout_fragment_length` was reached by the sampler.
- If `config.batch_mode=complete_episodes`, sample_batch will contain
exactly one episode (no matter how long).
New columns can be added to sample_batch and existing ones may be altered.
Args:
policy (Policy): The Policy used to generate the trajectory
(`sample_batch`)
sample_batch (SampleBatch): The SampleBatch to postprocess.
other_agent_batches (Optional[Dict[AgentID, SampleBatch]]): Optional
dict of AgentIDs mapping to other agents' trajectory data (from the
same episode). NOTE: The other agents use the same policy.
episode (Optional[Episode]): Optional multi-agent episode
object in which the agents operated.
Returns:
SampleBatch: The postprocessed, modified SampleBatch (or a new one).
"""
return postprocess_nstep_and_prio(policy, sample_batch)
def postprocess_trajectory(
self,
sample_batch: SampleBatch,
other_agent_batches: Optional[Dict[Any, SampleBatch]] = None,
episode: Optional[Episode] = None,
) -> SampleBatch:
return postprocess_nstep_and_prio(self, sample_batch,
other_agent_batches, episode)