ray.rllib.policy.Policy.compute_log_likelihoods#

Policy.compute_log_likelihoods(actions: Union[List[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]], numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor], obs_batch: Union[List[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]], numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor], state_batches: Optional[List[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]]] = None, prev_action_batch: Optional[Union[List[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]], numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]] = None, prev_reward_batch: Optional[Union[List[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]], numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]] = None, actions_normalized: bool = True, in_training: bool = True) → Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor][source]#

Computes the log-prob/likelihood for a given action and observation.

The log-likelihood is calculated using this Policy’s action distribution class (self.dist_class).

Parameters

  • actions – Batch of actions, for which to retrieve the log-probs/likelihoods (given all other inputs: obs, states, ..).

  • 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.

  • actions_normalized – Is the given actions already normalized (between -1.0 and 1.0) or not? If not and normalize_actions=True, we need to normalize the given actions first, before calculating log likelihoods.

  • in_training – Whether to use the forward_train() or forward_exploration() of the underlying RLModule.

Returns

[BATCH_SIZE].

Return type

Batch of log probs/likelihoods, with shape