如何避免此错误(错误:显示曲面退出)时，呈现开放-AI健身房？

Question

我试图解决高山汽车问题在人工智能健身房，但当我使用env. render()时，它是第一次工作，但当我试图再次渲染仿真后，2000年运行，它会出现以下错误(错误:显示曲面退出)。如何避免此错误？

我正在使用windows，并且在一个jupyter笔记本上运行代码。

import gym
import numpy as np 
import sys

#Create gym environment.
discount = 0.95
Learning_rate = 0.01
episodes = 25000
SHOW_EVERY = 2000
    
env = gym.make('MountainCar-v0')

discrete_os_size = [20] *len(env.observation_space.high)
discrete_os_win_size = (env.observation_space.high - env.observation_space.low)/ discrete_os_size
q_table = np.random.uniform(low=-2, high=0, size=(discrete_os_size + [env.action_space.n]))

# convert continuous state to discrete state 
def get_discrete_state(state):
    discrete_State = (state - env.observation_space.low) / discrete_os_win_size
    return tuple(discrete_State.astype(int))




for episode in range(episodes):
    
    if episode % SHOW_EVERY == 0:
        render = True
        print(episode)
    else:
        render = False

    ds = get_discrete_state(env.reset())
    done = False
    while not done:
        action = np.argmax(q_table[ds])
        new_state, reward, done, _ = env.step(action)
        new_discrete_state = get_discrete_state(new_state)
        
        if episode % SHOW_EVERY == 0:
            env.render()


        if not done:
            max_future_q = np.max(q_table[new_discrete_state])
            current_q_value = q_table[ds + (action, )]
            new_q = (1-Learning_rate) * current_q_value + Learning_rate * (reward + 
                 discount * max_future_q )
            q_table[ds + (action, )] = new_q

        elif new_state[0] >= env.goal_position:
            q_table[ds + (action, )] = 0

        ds = new_discrete_state
        
   

    env.close()

Answer 1

我也遇到了同样的问题，因为当您调用env.close()时，它会关闭环境，因此为了再次运行它，您必须创建一个新的环境。如果您想再次运行相同的环境，只需注释env.close()即可。