code: def learn(self, buffer, ppo_epoch, num_mini_batch, use_popart):
      if use_popart:
          advantages = buffer.returns[
              :-1
          ] - self.value_normalizer.denormalize(buffer.value_preds[:-1])
      else:
          advantages = buffer.returns[:-1] - buffer.value_preds[:-1]
  
      advantages_copy = advantages.copy()
      advantages_copy[buffer.active_masks[:-1] == 0.0] = np.nan
      mean_advantages = np.nanmean(advantages_copy)
      std_advantages = np.nanstd(advantages_copy)
      advantages = (advantages - mean_advantages) / (
          std_advantages + 1e-5
      )
  
      train_info = {}
      train_info["value_loss"] = 0
      train_info["policy_loss"] = 0
      train_info["dist_entropy"] = 0
  
      for _ in range(ppo_epoch):
          data_generator = buffer.sample_batch(
              advantages, num_mini_batch
          )
          for sample in data_generator:
              (
                  share_obs_batch,
                  obs_batch,
                  actions_batch,
                  value_preds_batch,
                  return_batch,
                  masks_batch,
                  active_masks_batch,
                  old_action_log_probs_batch,
                  adv_targ,
              ) = sample
  
              share_obs_batch = torch.from_numpy(share_obs_batch).to(
                  self.device
              )
              obs_batch = torch.from_numpy(obs_batch).to(self.device)
              actions_batch = torch.from_numpy(actions_batch).to(
                  self.device
              )
              value_preds_batch = torch.from_numpy(
                  value_preds_batch
              ).to(self.device)
              return_batch = torch.from_numpy(return_batch).to(
                  self.device
              )
              active_masks_batch = torch.from_numpy(
                  active_masks_batch
              ).to(self.device)
              old_action_log_probs_batch = torch.from_numpy(
                  old_action_log_probs_batch
              ).to(self.device)
              adv_targ = torch.from_numpy(adv_targ).to(self.device)
  
              value_loss, policy_loss, dist_entropy = self.alg.learn(
                  share_obs_batch,
                  obs_batch,
                  actions_batch,
                  value_preds_batch,
                  return_batch,
                  old_action_log_probs_batch,
                  adv_targ,
                  active_masks_batch,
              )
  
              train_info["value_loss"] += value_loss.item()
              train_info["policy_loss"] += policy_loss.item()
              train_info["dist_entropy"] += dist_entropy.item()
  
      num_updates = ppo_epoch * num_mini_batch
      for k in train_info.keys():
          train_info[k] /= num_updates
      return train_info
  
file_path: PARL/benchmark/torch/mappo/simple_agent.py
imports: import torch
  import numpy
  import parl
label_desc: Use image node_id 3 for calling the SimpleAgent obj's underlying member method code with example usage: obj.learn(buffer, ppo_epoch, num_mini_batch, use_popart) and returns: train_info