code: def _critic_learn(self, obs, action, reward, next_obs, terminal):
      with paddle.no_grad():
          # Compute the target Q value
          target_Q = self.target_model.value(
              next_obs, self.target_model.policy(next_obs)
          )
          terminal = paddle.cast(terminal, dtype="float32")
          target_Q = reward + ((1.0 - terminal) * self.gamma * target_Q)
  
      # Get current Q estimate
      current_Q = self.model.value(obs, action)
  
      # Compute critic loss
      critic_loss = F.mse_loss(current_Q, target_Q)
  
      # Optimize the critic
      self.critic_optimizer.clear_grad()
      critic_loss.backward()
      self.critic_optimizer.step()
      return critic_loss
  
file_path: PARL/parl/algorithms/paddle/ddpg.py
imports: import parl
  import paddle
  import paddle.nn.functional
  from copy import deepcopy
  from parl.utils.utils import check_model_method
label_desc: Use image node_id 4 for calling the DDPG obj's underlying member method code with example usage: obj._critic_learn(obs, action, reward, next_obs, terminal) and returns: critic_loss