text: # import gym
  # env = gym.make('FrozenLake8x8-v0')
  # env.reset()
  # for _ in range(10):
  #     env.render()
  #     env.step(env.action_space.sample()) # take a random action
  # env.close()
  
  
  
  
  # from gym import envs
  # import gym
  
  # frozen = gym.make('FrozenLake8x8-v0')
  
  # numEpisodes = 10
  
  # for episode in range(numEpisodes):
  #     observation = frozen.reset()
  
  #     for epochs in range(100):
  #         # frozen.render()
  #         # print(observation)
  #         action = frozen.action_space.sample()
  #         # print(action)
  
  #         obs, reward, done, info = frozen.step(action)
          
  #         if done: #this is needed to be changed
  #             print("Episode finished after {} timesteps".format(epochs+1))
  #             break
  
  # frozen.close()
          
  import gym
  import numpy as np
  import matplotlib.pyplot as plt
  from tqdm import trange
  
  ngames = 1000
  percentage = []
  scores = []
  
  env = gym.make('FrozenLake8x8-v0')
  
  for i in trange(ngames):
      done = False
      obs = env.reset()
  
      score = 0
  
      while not done:
          action = env.action_space.sample()
          obs, reward, done, info = env.step(action=action)
          score+=reward
      scores.append(score)
  
      if i%10==0:
          average = np.mean(scores[-10:])
          percentage.append(average)
  
  plt.plot(percentage)
  plt.show()
  