ray.rllib.policy.policy.Policy.compute_actions#

abstract Policy.compute_actions(obs_batch: Union[List[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor, dict, tuple]], numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor, dict, tuple], state_batches: Optional[List[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]]] = None, prev_action_batch: Union[List[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor, dict, tuple]], numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor, dict, tuple] = None, prev_reward_batch: Union[List[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor, dict, tuple]], numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor, dict, tuple] = None, info_batch: Optional[Dict[str, list]] = None, episodes: Optional[List[Episode]] = None, explore: Optional[bool] = None, timestep: Optional[int] = None, **kwargs) → Tuple[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor], List[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]], Dict[str, Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]]][source]#

Computes actions for the current policy.

Parameters

  • obs_batch – Batch of observations.

  • state_batches – List of RNN state input batches, if any.

  • prev_action_batch – Batch of previous action values.

  • prev_reward_batch – Batch of previous rewards.

  • info_batch – Batch of info objects.

  • episodes – List of Episode objects, one for each obs in obs_batch. This provides access to all of the internal episode state, which may be useful for model-based or multi-agent algorithms.

  • explore – Whether to pick an exploitation or exploration action. Set to None (default) for using the value of self.config["explore"].

  • timestep – The current (sampling) time step.

Keyword Arguments

kwargs – Forward compatibility placeholder

Returns

Batch of output actions, with shape like

[BATCH_SIZE, ACTION_SHAPE].

state_outs (List[TensorType]): List of RNN state output

batches, if any, each with shape [BATCH_SIZE, STATE_SIZE].

info (List[dict]): Dictionary of extra feature batches, if any,

with shape like {“f1”: [BATCH_SIZE, …], “f2”: [BATCH_SIZE, …]}.

Return type

actions