code: def _critic_learn(self, obs, action, reward, next_obs, terminal):
      with paddle.no_grad():
          next_action, next_log_pro = self.sample(next_obs)
          q1_next, q2_next = self.target_model.value(
              next_obs, next_action
          )
          target_Q = (
              paddle.minimum(q1_next, q2_next)
              - self.alpha * next_log_pro
          )
          terminal = paddle.cast(terminal, dtype="float32")
          target_Q = reward + self.gamma * (1.0 - terminal) * target_Q
      cur_q1, cur_q2 = self.model.value(obs, action)
  
      critic_loss = F.mse_loss(cur_q1, target_Q) + F.mse_loss(
          cur_q2, target_Q
      )
  
      self.critic_optimizer.clear_grad()
      critic_loss.backward()
      self.critic_optimizer.step()
      return critic_loss
  
file_path: PARL/parl/algorithms/paddle/oac.py
imports: import parl
  import paddle
  from paddle.distribution import Normal
  import paddle.nn.functional
  from parl.utils.utils import check_model_method
  from copy import deepcopy
  import math
label_desc: Use image node_id 6 for calling the OAC obj's underlying member method code with example usage: obj._critic_learn(obs, action, reward, next_obs, terminal) and returns: critic_loss