code: def critic_learn(self, obs, action, reward, next_obs, terminal):
      next_obs_action, next_obs_log_pi = self.sample(next_obs)
      qf1_next_target, qf2_next_target = self.target_critic.value(
          next_obs, next_obs_action
      )
      min_qf_next_target = (
          layers.elementwise_min(qf1_next_target, qf2_next_target)
          - next_obs_log_pi * self.alpha
      )
  
      terminal = layers.cast(terminal, dtype="float32")
      target_Q = (
          reward + (1.0 - terminal) * self.gamma * min_qf_next_target
      )
      target_Q.stop_gradient = True
  
      current_Q1, current_Q2 = self.critic.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/sac.py
imports: from parl.core.fluid import layers
  from copy import deepcopy
  import numpy
  from paddle import fluid
  from paddle.fluid.layers import Normal
  from parl.core.fluid.algorithm import Algorithm
label_desc: Use image node_id 6 for calling the SAC obj's underlying member method code with example usage: obj.critic_learn(obs, action, reward, next_obs, terminal) and returns: cost