code: def train(self):
      batch = random.sample(self.memory, self.batch_size)
      batch = np.array(batch, dtype=object)
  
      states = np.stack(batch[:, 0]).astype(float)
      actions = batch[:, 1].astype(int)
      rewards = batch[:, 2].astype(float)
      next_states = np.stack(batch[:, 3]).astype(float)
      dones = batch[:, 4].astype(bool)
      not_dones = ~dones
  
      row_idx = np.arange(
          self.batch_size
      )  # used for indexing the batch
  
      # value of the next states with double q learning
      # see https://arxiv.org/abs/1509.06461 for more information on double q learning
      with torch.no_grad():
          next_states = torch.from_numpy(next_states).float().to(DEVICE)
          idx = row_idx, np.argmax(
              self.q_net(next_states).cpu().data.numpy(), 1
          )
          next_state_values = (
              self.target_net(next_states).cpu().data.numpy()[idx]
          )
          next_state_values = next_state_values[not_dones]
  
      # this defines y = r + discount * max_a q(s', a)
      q_targets = rewards.copy()
      q_targets[not_dones] += self.discount * next_state_values
      q_targets = torch.from_numpy(q_targets).float().to(DEVICE)
  
      # this selects only the q values of the actions taken
      idx = row_idx, actions
      states = torch.from_numpy(states).float().to(DEVICE)
      action_values = self.q_net(states)[idx].float().to(DEVICE)
  
      self.opt.zero_grad()
      td_error = self.criterion(q_targets, action_values)
      td_error.backward()
      self.opt.step()
  
      if self.epsilon > self.epsilon_min:
          self.epsilon *= self.epsilon_decay
      else:
          self.epsilon = self.epsilon_min
  
imports: import itertools
  import os
  import random
  from collections import deque
  from time import sleep, time
  import numpy
  import skimage.transform
  import torch
  import torch.nn
  import torch.optim
  from tqdm import trange
  import vizdoom
label_desc: Use image node_id 5 for calling the DQNAgent obj's underlying member method code with example usage: obj.train() without return types