SequentialDistributedActors

class maze.train.parallelization.distributed_actors.sequential_distributed_actors.SequentialDistributedActors(env_factory: Callable[], Union[maze.core.env.structured_env.StructuredEnv, maze.core.env.structured_env_spaces_mixin.StructuredEnvSpacesMixin, maze.core.log_stats.log_stats_env.LogStatsEnv]], policy: maze.core.agent.torch_policy.TorchPolicy, n_rollout_steps: int, n_actors: int, batch_size: int, actor_env_seeds: List[int])

Dummy implementation of distributed actors creates the actors as a list. Once the outputs are to

be collected, it simply rolls them out in a loop until is has enough to be returned.

Parameters

actor_env_seeds – A list of seeds for each actors’ env.

broadcast_updated_policy(state_dict: Dict) → None

(overrides DistributedActors)

Store the newest policy in the shared network object

collect_outputs(learner_device: str) → Tuple[maze.core.trajectory_recording.records.structured_spaces_record.StructuredSpacesRecord, float, float, float]

(overrides DistributedActors)

Run the rollouts and collect the outputs.

start() → None

(overrides DistributedActors)

Nothing to do in dummy implementation

stop() → None

(overrides DistributedActors)

Nothing to do in dummy implementation