ray.rllib.core.learner.learner_group.LearnerGroup#

class ray.rllib.core.learner.learner_group.LearnerGroup(*, config: AlgorithmConfig, module_spec: SingleAgentRLModuleSpec | MultiAgentRLModuleSpec | None = None)[source]#

Bases: Checkpointable

Coordinator of n (possibly remote) Learner workers.

Each Learner worker has a copy of the RLModule, the loss function(s), and one or more optimizers.

PublicAPI (alpha): This API is in alpha and may change before becoming stable.

Methods

`__init__`	Initializes a LearnerGroup instance.
`add_module`	Adds a module to the underlying MultiAgentRLModule.
`foreach_learner`	Calls the given function on each Learner L with the args: (L, **kwargs).
`from_checkpoint`	Creates a new Checkpointable instance from the given location and returns it.
`get_metadata`	Returns JSON writable metadata further describing the implementing class.
`get_stats`	Returns the current stats for the input queue for this learner group.
`get_weights`	Convenience method instead of self.get_state(components=...).
`remove_module`	Removes a module from the Learner.
`restore_from_path`	Restores the state of the implementing class from the given path.
`save_to_path`	Saves the state of the implementing class (or `state`) to `path`.
`set_weights`	Convenience method instead of self.set_state({'learner': {'rl_module': ..}}).
`shutdown`	Shuts down the LearnerGroup.
`update_from_batch`	Performs gradient based update(s) on the Learner(s), based on given batch.
`update_from_episodes`	Performs gradient based update(s) on the Learner(s), based on given episodes.

Attributes