class_docstr: Categorical Double DQN.
docstr: Compute a batch of target return distributions.
docstr_tok: ["Compute","a","batch","of","target","return","distributions","."]
code: def _compute_target_values(self, exp_batch):
      """Compute a batch of target return distributions."""
  
      batch_next_state = exp_batch["next_state"]
      batch_rewards = exp_batch["reward"]
      batch_terminal = exp_batch["is_state_terminal"]
  
      with pfrl.utils.evaluating(
          self.target_model
      ), pfrl.utils.evaluating(self.model):
          if self.recurrent:
              target_next_qout, _ = pack_and_forward(
                  self.target_model,
                  batch_next_state,
                  exp_batch["next_recurrent_state"],
              )
              next_qout, _ = pack_and_forward(
                  self.model,
                  batch_next_state,
                  exp_batch["next_recurrent_state"],
              )
          else:
              target_next_qout = self.target_model(batch_next_state)
              next_qout = self.model(batch_next_state)
  
      batch_size = batch_rewards.shape[0]
      z_values = target_next_qout.z_values
      n_atoms = z_values.numel()
  
      # next_q_max: (batch_size, n_atoms)
      next_q_max = target_next_qout.evaluate_actions_as_distribution(
          next_qout.greedy_actions.detach()
      )
      assert next_q_max.shape == (batch_size, n_atoms), next_q_max.shape
  
      # Tz: (batch_size, n_atoms)
      Tz = (
          batch_rewards[..., None]
          + (1.0 - batch_terminal[..., None])
          * exp_batch["discount"][..., None]
          * z_values[None]
      )
      return _apply_categorical_projection(Tz, next_q_max, z_values)
  
imports: import pfrl
  from pfrl.agents import categorical_dqn
  from pfrl.agents.categorical_dqn import _apply_categorical_projection
  from pfrl.utils.recurrent import pack_and_forward
label_desc: Use image node_id 1 for calling the CategoricalDoubleDQN obj's underlying member method code with example usage: obj._compute_target_values(exp_batch) and returns: _apply_categorical_projection