ray.rllib.models.distributions.Distribution.rsample#

abstract Distribution.rsample(*, sample_shape: Tuple[int, ...] = None, return_logp: bool = False, **kwargs) → numpy.array | jnp.ndarray | tf.Tensor | torch.Tensor | Tuple[numpy.array | jnp.ndarray | tf.Tensor | torch.Tensor, numpy.array | jnp.ndarray | tf.Tensor | torch.Tensor][source]#

Draw a re-parameterized sample from the action distribution.

If this method is implemented, we can take gradients of samples w.r.t. the distribution parameters.

Parameters:

  • sample_shape – The shape of the sample to draw.

  • return_logp – Whether to return the logp of the sampled values.

  • **kwargs – Forward compatibility placeholder.

Returns:

The sampled values. If return_logp is True, returns a tuple of the sampled values and its logp.