code: def _actor_learn(self, obs, actor_lr, model_id):
      action = self.models[model_id].policy(obs)
      Q = self.models[model_id].value(obs, action)
      cost = layers.reduce_mean(-1.0 * Q)
      optimizer = fluid.optimizer.AdamOptimizer(actor_lr)
      optimizer.minimize(
          cost, parameter_list=self.models[model_id].get_actor_params()
      )
      return cost
  
file_path: PARL/examples/NeurIPS2018-AI-for-Prosthetics-Challenge/multi_head_ddpg.py
imports: import parl
  from parl import layers
  from copy import deepcopy
  from paddle import fluid
label_desc: Use image node_id 5 for calling the MultiHeadDDPG obj's underlying member method code with example usage: obj._actor_learn(obs, actor_lr, model_id) and returns: cost