code: def learn(self, batch_size):
      exps = self.memory.sample(batch_size)
      obss, actions, rewards, obs_nexts, dones = self._process(exps)
  
      # Update critic
      dy.renew_cg()
      target_actions = self.actor_target(obs_nexts, batched=True)
      target_values = self.critic_target(
          dy.concatenate(
              [dy.inputTensor(obs_nexts, batched=True), target_actions]
          ),
          batched=True,
      )
      target_values = rewards + 0.99 * target_values.npvalue() * (
          1 - dones
      )
  
      dy.renew_cg()
      values = self.critic(
          np.concatenate([obss, actions]), batched=True
      )
      loss = dy.mean_batches(
          (values - dy.inputTensor(target_values, batched=True)) ** 2
      )
      loss_value_critic = loss.npvalue()
      loss.backward()
      self.trainer_critic.update()
  
      # update actor
      dy.renew_cg()
      actions = self.actor(obss, batched=True)
      obs_and_actions = dy.concatenate(
          [dy.inputTensor(obss, batched=True), actions]
      )
      loss = -dy.mean_batches(
          self.critic(obs_and_actions, batched=True)
      )
      loss_value_actor = loss.npvalue()
      loss.backward()
      self.trainer_actor.update()
  
      self.noise_stddev = (
          (self.noise_stddev - self.noise_stddev_decrease)
          if self.noise_stddev > self.noise_stddev_lower
          else self.noise_stddev_lower
      )
  
      self.actor_target.update(self.actor, soft=True)
      self.critic_target.update(self.critic, soft=True)
  
      return loss_value_actor + loss_value_critic
  
file_path: dynet/examples/reinforcement-learning/ddpg.py
imports: import dynet
  import numpy
  from memory import Memory
  from network import MLP
label_desc: Use image node_id 4 for calling the DDPG obj's underlying member method code with example usage: obj.learn(batch_size) and returns: unknown