code: def _actor_learn(self, obs_n, act_n):
      i = self.agent_index
      this_policy = self.model.policy(obs_n[i])
      sample_this_action = SoftPDistribution(
          logits=this_policy, act_space=self.act_space[self.agent_index]
      ).sample()
  
      action_input_n = act_n + []
      action_input_n[i] = sample_this_action
      eval_q = self.Q(obs_n, action_input_n)
      act_cost = layers.reduce_mean(-1.0 * eval_q)
  
      act_reg = layers.reduce_mean(layers.square(this_policy))
  
      cost = act_cost + act_reg * 1e-3
  
      fluid.clip.set_gradient_clip(
          clip=fluid.clip.GradientClipByNorm(clip_norm=0.5),
          param_list=self.model.get_actor_params(),
      )
  
      optimizer = fluid.optimizer.AdamOptimizer(self.actor_lr)
      optimizer.minimize(
          cost, parameter_list=self.model.get_actor_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 7 for calling the MADDPG obj's underlying member method code with example usage: obj._actor_learn(obs_n, act_n) and returns: cost