ray.rllib.core.learner.learner_group.LearnerGroup.additional_update#
- LearnerGroup.additional_update(*, reduce_fn: ~typing.Callable[[dict | NestedDict], dict | NestedDict] = <function _reduce_mean_results>, **kwargs) Dict[str, Any] | List[Dict[str, Any]] [source]#
Apply additional non-gradient based updates to the Learners.
For example, this could be used to do a polyak averaging update of a target network in off policy algorithms like SAC or DQN.
By default this is a pass through that calls
Learner.additional_update
- Parameters:
reduce_fn – See
update()
documentation for more details.**kwargs – Keyword arguments to pass to each Learner.
- Returns:
A list of dictionaries of results from the updates from each worker.