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) ...

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