ray.rllib.policy.Policy.compute_gradients#

Policy.compute_gradients(postprocessed_batch: ray.rllib.policy.sample_batch.SampleBatch) → Tuple[Union[List[Tuple[Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor], Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor]]], List[Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor]]], Dict[str, Union[numpy.array, tensorflow.python.framework.ops.Tensor, torch.Tensor]]][source]#

Computes gradients given a batch of experiences.

Either this in combination with apply_gradients() or learn_on_batch() must be implemented by subclasses.

Parameters

postprocessed_batch – The SampleBatch object to use for calculating gradients.

Returns

List of gradient output values. grad_info: Extra policy-specific info values.

Return type

grads