code: def critic_learn(self, obs, action, reward, next_obs, terminal):
      noise = layers.gaussian_random_batch_size_like(
          action, shape=[-1, action.shape[1]]
      )
      noise = layers.clip(
          noise * self.policy_noise,
          min=-self.noise_clip,
          max=self.noise_clip,
      )
      next_action = self.target_model.policy(next_obs) + noise
      next_action = layers.clip(
          next_action, -self.max_action, self.max_action
      )
  
      next_Q1, next_Q2 = self.target_model.value(next_obs, next_action)
      next_Q = layers.elementwise_min(next_Q1, next_Q2)
  
      terminal = layers.cast(terminal, dtype="float32")
      target_Q = reward + (1.0 - terminal) * self.gamma * next_Q
      target_Q.stop_gradient = True
  
      current_Q1, current_Q2 = self.model.value(obs, action)
      cost = layers.square_error_cost(
          current_Q1, target_Q
      ) + layers.square_error_cost(current_Q2, target_Q)
      cost = layers.reduce_mean(cost)
      optimizer = fluid.optimizer.AdamOptimizer(self.critic_lr)
      optimizer.minimize(cost)
      return cost
  
file_path: PARL/parl/algorithms/fluid/td3.py
imports: from parl.core.fluid import layers
  from copy import deepcopy
  from paddle import fluid
  from parl.core.fluid.algorithm import Algorithm
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: cost