ray.rllib.evaluation.rollout_worker.RolloutWorker.apply_gradients
ray.rllib.evaluation.rollout_worker.RolloutWorker.apply_gradients#
- RolloutWorker.apply_gradients(grads: Union[List[Tuple[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor], Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]]], List[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]], Dict[str, Union[List[Tuple[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor], Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]]], List[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]]]]]) None [source]#
Applies the given gradients to this worker’s models.
Uses the Policy’s/ies’ apply_gradients method(s) to perform the operations.
- Parameters
grads – Single ModelGradients (single-agent case) or a dict mapping PolicyIDs to the respective model gradients structs.
Examples
>>> import gymnasium as gym >>> from ray.rllib.evaluation.rollout_worker import RolloutWorker >>> from ray.rllib.algorithms.pg.pg_tf_policy import PGTF1Policy >>> worker = RolloutWorker( ... env_creator=lambda _: gym.make("CartPole-v1"), ... default_policy_class=PGTF1Policy) >>> samples = worker.sample() >>> grads, info = worker.compute_gradients(samples) >>> worker.apply_gradients(grads)