class_docstr: A wrapper for ALE emulator, with configurations to mimic DeepMind DQN settings.
  
  Info:
      score: the accumulated reward in the current game
      gameOver: True when the current game is Over
class_docstr_tok: ["A","wrapper","for","ALE","emulator",",","with","configurations","to","mimic","DeepMind","DQN","settings",".","Info",":","score",":","the","accumulated","reward","in","the","current","game","gameOver",":","True","when","the","current","game","is","Over"]
code: def step(self, act):
      oldlives = self.ale.lives()
      r = 0
      for k in range(self.frame_skip):
          if k == self.frame_skip - 1:
              self.last_raw_screen = self._grab_raw_image()
          r += self.ale.act(self.actions[act])
          newlives = self.ale.lives()
          if self.ale.game_over() or (
              self.live_lost_as_eoe and newlives < oldlives
          ):
              break
  
      isOver = self.ale.game_over()
      if self.live_lost_as_eoe:
          isOver = isOver or newlives < oldlives
  
      info = {"ale.lives": newlives}
      return self._current_state(), r, isOver, info
  
file_path: tensorpack/examples/DeepQNetwork/atari.py
imports: import numpy
  import os
  import threading
  import cv2
  import gym
  import six
  from ale_py import ALEInterface, Action, LoggerMode
  from gym import spaces
  from tensorpack.utils import logger, execute_only_once, get_rng
  from tensorpack.utils.fs import get_dataset_path
label_desc: Use image node_id 8 for calling the AtariPlayer obj's underlying member method code with example usage: obj.step(act) and returns: self,  r,  isOver,  info