Playing the FrozenLake game

The agent code now plays or explores the environment and it is helpful if we understand how this code runs. Open up Chapter_3_3.py again and follow the exercise:

1. All we need to focus on for this section is how the agent plays the game. Scroll down to the play_game function, as shown in the following:

def play_game(env, policy, display=True):
  env.reset()
  episode = []
  finished = False
  while not finished:
    s = env.env.s
    if display:
      clear_output(True)
      env.render()
      sleep(1)
    timestep = []
    timestep.append(s)
    n = random.uniform(0, sum(policy[s].values()))
    top_range = 0
    action = 0
    for prob in policy[s].items():
      top_range += prob[1]            
      if n < top_range:
        action = prob[0]
        break 
    state, reward, finished, info = env.step(action)
            
    timestep.append(action)
    timestep.append...