class_docstr: Small wrapper for gym atari environments.
  Responsible for preprocessing screens and holding on to a screen buffer
  of size action_repeat from which environment state is constructed.
class_docstr_tok: ["Small","wrapper","for","gym","atari","environments",".","Responsible","for","preprocessing","screens","and","holding","on","to","a","screen","buffer","of","size","action_repeat","from","which","environment","state","is","constructed","."]
docstr: Resets the atari game, clears the state buffer.
docstr_tok: ["Resets","the","atari","game",",","clears","the","state","buffer","."]
code: def get_initial_state(self):
      """
      Resets the atari game, clears the state buffer.
      """
      # Clear the state buffer
      self.state_buffer = deque()
  
      x_t = self.env.reset()
      x_t = self.get_preprocessed_frame(x_t)
      s_t = np.stack([x_t for i in range(self.action_repeat)], axis=0)
  
      for i in range(self.action_repeat - 1):
          self.state_buffer.append(x_t)
      return s_t
  
imports: from __future__ import division, print_function, absolute_import
  import threading
  import random
  import numpy
  import time
  from skimage.transform import resize
  from skimage.color import rgb2gray
  from collections import deque
  import gym
  import tensorflow.compat.v1
  import tflearn
label_desc: Use image node_id 2 for calling the AtariEnvironment obj's underlying member method code with example usage: obj.get_initial_state() and returns: s_t