ray.rllib.evaluation.worker_set.WorkerSet

class ray.rllib.evaluation.worker_set.WorkerSet(*, env_creator: Callable[[EnvContext], Any | gymnasium.Env | None] | None = None, validate_env: Callable[[Any | gymnasium.Env], None] | None = None, default_policy_class: Type[Policy] | None = None, config: AlgorithmConfig | None = None, num_workers: int = 0, local_worker: bool = True, logdir: str | None = None, _setup: bool = True)

Set of EnvRunners with n @ray.remote workers and zero or one local worker.

Where: n >= 0.

Methods

__init__	Initializes a WorkerSet instance.
add_policy	Adds a policy to this WorkerSet's workers or a specific list of workers.
add_workers	Creates and adds a number of remote workers to this worker set.
fetch_ready_async_reqs	Get esults from outstanding asynchronous requests that are ready.
foreach_env	Calls func with all workers' sub-environments as args.
foreach_env_with_context	Calls func with all workers' sub-environments and env_ctx as args.
foreach_policy	Calls func with each worker's (policy, PolicyID) tuple.
foreach_policy_to_train	Apply func to all workers' Policies iff in policies_to_train.
foreach_worker	Calls the given function with each worker instance as the argument.
foreach_worker_async	Calls the given function asynchronously with each worker as the argument.
foreach_worker_with_id	Similar to foreach_worker(), but calls the function with id of the worker too.
healthy_worker_ids	Returns the list of remote worker IDs.
is_policy_to_train	Whether given PolicyID (optionally inside some batch) is trainable.
local_worker	Returns the local rollout worker.
num_healthy_remote_workers	Returns the number of healthy remote workers.
num_healthy_workers	Returns the number of all healthy workers, including the local worker.
num_in_flight_async_reqs	Returns the number of in-flight async requests.
num_remote_worker_restarts	Total number of times managed remote workers have been restarted.
num_remote_workers	Returns the number of remote rollout workers.
probe_unhealthy_workers	Checks for unhealthy workers and tries restoring their states.
reset	Hard overrides the remote workers in this set with the given one.
stop	Calls stop on all rollout workers (including the local one).
sync_weights	Syncs model weights from the given weight source to all remote workers.