Jupyter python 内核死了 - OpenAI

Question

我是强化学习的新手，我正在尝试使用 OpenAI Gym 环境。
首先，我通过这个命令安装了gym： !pip install gym in jupyter
再次运行以确保它已完全安装后，我得到了以下输出：

Requirement already satisfied: gym in c:\program files\anaconda3\lib\site-packages (0.10.5)
Requirement already satisfied: pyglet>=1.2.0 in c:\program files\anaconda3\lib\site-packages (from gym) (1.3.2)
Requirement already satisfied: six in c:\program files\anaconda3\lib\site-packages (from gym) (1.11.0)
Requirement already satisfied: requests>=2.0 in c:\program files\anaconda3\lib\site-packages (from gym) (2.19.1)
Requirement already satisfied: numpy>=1.10.4 in c:\program files\anaconda3\lib\site-packages (from gym) (1.13.3)
Requirement already satisfied: future in c:\program files\anaconda3\lib\site-packages (from pyglet>=1.2.0->gym) (0.16.0)
Requirement already satisfied: urllib3=1.21.1 in c:\program files\anaconda3\lib\site-packages (from requests>=2.0->gym) (1.23)
Requirement already satisfied: certifi>=2017.4.17 in c:\program files\anaconda3\lib\site-packages (from requests>=2.0->gym) (2018.4.16)
Requirement already satisfied: chardet=3.0.2 in c:\program files\anaconda3\lib\site-packages (from requests>=2.0->gym) (3.0.4)
Requirement already satisfied: idna=2.5 in c:\program files\anaconda3\lib\site-packages (from requests>=2.0->gym) (2.7)

正如 OpenAI Gym Doc 页面所述，我尝试了这段代码以确保一切正常：

import gym
env = gym.make('CartPole-v0')
for i_episode in range(20):
    observation = env.reset() #The process gets started by calling reset(), which returns an initial observation. 
    for t in range(100):
        env.render()
        print(observation)
        action = env.action_space.sample()
        observation, reward, done, info = env.step(action)
        if done:
            print("Episode finished after {} timesteps".format(t+1))
            break

运行此代码后，将打开一个新窗口，所有内容与文档页面上提供的视频完全相同。
但问题是当代理完成时（所有剧集都运行），内核死掉并再次重新启动。

我发现在某些特定版本中， gym.make(close=true)可以解决问题。 但不是在最新版本的 anaconda 和 Gym 中。 另一个解决方案是使用以下代码： env = gym.make('name') env.close() 。 但问题是，这个close()函数会删除env对象，所以每次我们创建环境时。

问候

Answer 1

我不知道如何解决这个问题，但我在gym-retro 中使用了不同的代码，效果很好。

env = retro.make(game="StreetFighterIISpecialChampionEdition-Genesis", state="ChunLiVsBlanka.1star")
env = wrapper(env)
model = PPO2.load("D:/Python/Projects/Hakisa/rl_model_1000000_steps")
obs = env.reset()
total_reward = []
steps = 0
end = False

while end != True and steps < 100:
    env.render()
    action, state = model.predict(obs)
    obs, reward, end, info = env.step(action)
    steps += 1
    total_reward.append(reward)
    time.sleep(0.05)