old_contents: """
  Reinforcement learning maze example.
  
  Red rectangle:          explorer.
  Black rectangles:       hells       [reward = -1].
  Yellow bin circle:      paradise    [reward = +1].
  All other states:       ground      [reward = 0].
  
  This script is the main part which controls the update method of this example.
  The RL is in RL_brain.py.
  
  View more on 莫烦Python: https://morvanzhou.github.io/tutorials/
  """
  
  from maze_env import Maze
  from RL_brain import QTable
  
  
  def update():
      for episode in range(100):
          # initial observation
          observation = env.reset()
  
          while True:
              # fresh env
              env.render()
  
              # RL choose action based on observation
              action = RL.choose_action(str(observation))
  
              # RL take action and get next observation and reward
              observation_, reward, done = env.step(action)
  
              # RL learn from this transition
              RL.learn(str(observation), action, reward, str(observation_))
  
              # swap observation
              observation1 = observation_
  
              # break while loop when end of this episode
              if done:
                  break
  
      # end of game
      print('game over')
      env.destroy()
  
  if __name__ == "__main__":
      env = Maze()
      RL = QTable(actions=list(range(env.n_actions)))
  
      env.after(100, update)
      env.mainloop()
new_contents: """
  Reinforcement learning maze example.
  
  Red rectangle:          explorer.
  Black rectangles:       hells       [reward = -1].
  Yellow bin circle:      paradise    [reward = +1].
  All other states:       ground      [reward = 0].
  
  This script is the main part which controls the update method of this example.
  The RL is in RL_brain.py.
  
  View more on 莫烦Python: https://morvanzhou.github.io/tutorials/
  """
  
  from maze_env import Maze
  from RL_brain import QTable
  
  
  def update():
      for episode in range(100):
          # initial observation
          observation = env.reset()
  
          while True:
              # fresh env
              env.render()
  
              # RL choose action based on observation
              action = RL.choose_action(str(observation))
  
              # RL take action and get next observation and reward
              observation_, reward, done = env.step(action)
  
              # RL learn from this transition
              RL.learn(str(observation), action, reward, str(observation_))
  
              # swap observation
              observation = observation_
  
              # break while loop when end of this episode
              if done:
                  break
  
      # end of game
      print('game over')
      env.destroy()
  
  if __name__ == "__main__":
      env = Maze()
      RL = QTable(actions=list(range(env.n_actions)))
  
      env.after(100, update)
      env.mainloop()
subject: fix typo