ray.rllib.core.learner.learner.Learner.update_from_batch#
- Learner.update_from_batch(batch: MultiAgentBatch, *, minibatch_size: int | None = None, num_iters: int = 1, reduce_fn=-1) dict | NestedDict [source]#
Do
num_iters
minibatch updates given a train batch.You can use this method to take more than one backward pass on the batch. The same
minibatch_size
andnum_iters
will be used for all module ids in MultiAgentRLModule.- Parameters:
batch – A batch of training data to update from.
minibatch_size – The size of the minibatch to use for each update.
num_iters – The number of complete passes over all the sub-batches in the input multi-agent batch.
- Returns:
A
ResultDict
object produced by a call toself.metrics.reduce()
. The returned dict may be arbitrarily nested and must haveStats
objects at all its leafs, allowing components further downstream (i.e. a user of this Learner) to further reduce these results (for example over n parallel Learners).