code: def _critic_learn(self, obs_n, act_n, target_q):
      pred_q = self.Q(obs_n, act_n)
      cost = layers.reduce_mean(
          layers.square_error_cost(pred_q, target_q)
      )
  
      fluid.clip.set_gradient_clip(
          clip=fluid.clip.GradientClipByNorm(clip_norm=0.5),
          param_list=self.model.get_critic_params(),
      )
  
      optimizer = fluid.optimizer.AdamOptimizer(self.critic_lr)
      optimizer.minimize(
          cost, parameter_list=self.model.get_critic_params()
      )
      return cost
  
file_path: PARL/parl/algorithms/fluid/maddpg.py
imports: import warnings
  from parl.core.fluid import layers
  from copy import deepcopy
  from paddle import fluid
  from parl.core.fluid.algorithm import Algorithm
  from parl.core.fluid.policy_distribution import SoftCategoricalDistribution
  from parl.core.fluid.policy_distribution import SoftMultiCategoricalDistribution
label_desc: Use image node_id 8 for calling the MADDPG obj's underlying member method code with example usage: obj._critic_learn(obs_n, act_n, target_q) and returns: cost