code: def _critic_learn(self, obs, action, reward, next_obs, terminal):
      with paddle.no_grad():
          noise = paddle.randn(action.shape) * self.policy_noise
          noise = paddle.clip(noise, -self.noise_clip, self.noise_clip)
  
          next_action = self.target_model.policy(next_obs) + noise
          next_action = paddle.clip(next_action, -1.0, 1.0)
  
          target_q1, target_q2 = self.target_model.value(
              next_obs, next_action
          )
          target_q = paddle.minimum(target_q1, target_q2)
          terminal = paddle.cast(terminal, dtype="float32")
          target_q = reward + (1.0 - terminal) * self.gamma * target_q
      current_q1, current_q2 = self.model.value(obs, action)
  
      critic_loss = F.mse_loss(current_q1, target_q) + F.mse_loss(
          current_q2, target_q
      )
  
      self.critic_optimizer.clear_grad()
      critic_loss.backward()
      self.critic_optimizer.step()
      return critic_loss
  
file_path: PARL/parl/algorithms/paddle/td3.py
imports: import parl
  import paddle
  import paddle.nn.functional
  from parl.utils.utils import check_model_method
  from copy import deepcopy
label_desc: Use image node_id 4 for calling the TD3 obj's underlying member method code with example usage: obj._critic_learn(obs, action, reward, next_obs, terminal) and returns: critic_loss