ray.rllib.policy.eager_tf_policy_v2.EagerTFPolicyV2.action_sampler_fn#

EagerTFPolicyV2.action_sampler_fn(model: ModelV2, *, obs_batch: numpy.array | jnp.ndarray | tf.Tensor | torch.Tensor, state_batches: numpy.array | jnp.ndarray | tf.Tensor | torch.Tensor, **kwargs) → Tuple[numpy.array | jnp.ndarray | tf.Tensor | torch.Tensor, numpy.array | jnp.ndarray | tf.Tensor | torch.Tensor, numpy.array | jnp.ndarray | tf.Tensor | torch.Tensor, List[numpy.array | jnp.ndarray | tf.Tensor | torch.Tensor]][source]#

Custom function for sampling new actions given policy.

Parameters:

  • model – Underlying model.

  • obs_batch – Observation tensor batch.

  • state_batches – Action sampling state batch.

Returns:

Sampled action Log-likelihood Action distribution inputs Updated state