如何在OpenAI中创建新的Gym环境

技术背景

在使用机器学习让AI智能体学习玩视频游戏时，有时候现有的OpenAI Gym环境无法满足需求，需要创建自定义的环境。OpenAI Gym是一个用于开发和比较强化学习算法的工具包，允许用户创建、注册和使用各种环境。

实现步骤

1. 创建新的仓库和PIP包结构

首先，需要创建一个新的仓库，其结构如下：

gym-foo/
  README.md
  setup.py
  gym_foo/
    __init__.py
    envs/
      __init__.py
      foo_env.py
      foo_extrahard_env.py

2. 编写环境类

在foo_env.py中，需要实现一个继承自gym.Env的类，示例代码如下：

import gym
import hfo_py

class FooEnv(gym.Env):
    metadata = {'render.modes': ['human']}

    def __init__(self):
        pass

    def _step(self, action):
        """
        Parameters
        ----------
        action :

        Returns
        -------
        ob, reward, episode_over, info : tuple
            ob (object) :
                an environment-specific object representing your observation of
                the environment.
            reward (float) :
                amount of reward achieved by the previous action. The scale
                varies between environments, but the goal is always to increase
                your total reward.
            episode_over (bool) :
                whether it's time to reset the environment again. Most (but not
                all) tasks are divided up into well-defined episodes, and done
                being True indicates the episode has terminated. (For example,
                perhaps the pole tipped too far, or you lost your last life.)
            info (dict) :
                 diagnostic information useful for debugging. It can sometimes
                 be useful for learning (for example, it might contain the raw
                 probabilities behind the environment's last state change).
                 However, official evaluations of your agent are not allowed to
                 use this for learning.
        """
        self._take_action(action)
        self.status = self.env.step()
        reward = self._get_reward()
        ob = self.env.getState()
        episode_over = self.status != hfo_py.IN_GAME
        return ob, reward, episode_over, {}

    def _reset(self):
        pass

    def _render(self, mode='human', close=False):
        pass

    def _take_action(self, action):
        pass

    def _get_reward(self):
        """ Reward is given for XY. """
        if self.status == FOOBAR:
            return 1
        elif self.status == ABC:
            return self.somestate ** 2
        else:
            return 0

3. 使用自定义环境

在代码中导入并使用自定义环境：

import gym
import gym_foo
env = gym.make('MyEnv-v0')

最佳实践

• 参考现有环境：可以参考OpenAI Gym中现有的环境代码，了解如何实现不同的功能，如状态表示、奖励函数设计等。
• 使用包装器：如果要基于现有的环境进行修改，可以使用Gym提供的包装器（Wrapper）来避免重复编写代码。

常见问题

1. 导入未使用警告

如果遇到gym_foo imported but unused的警告，可以在导入语句后面添加# noqa来告诉编辑器忽略该警告。

2. 导入错误

如果在按照上述步骤操作后遇到gym_foo导入错误，可以执行pip install -e .命令来解决。