如何在 OpenAI 中创建新的健身房环境?

问题描述

我的任务是制作一个 AI 代理，该代理将学习使用机器学习玩电子游戏.我想使用 OpenAI Gym 创建一个新环境，因为我不想使用现有环境.如何创建新的自定义环境?

I have an assignment to make an AI Agent that will learn to play a video game using ML. I want to create a new environment using OpenAI Gym because I don't want to use an existing environment. How can I create a new, custom Environment?

另外，在没有 OpenAI Gym 的帮助下，我是否可以开始开发让 AI Agent 玩特定视频游戏的其他方法?

Also, is there any other way I can start to develop making AI Agent to play a specific video game without the help of OpenAI Gym?

推荐答案

查看我的 banana-健身房适用于极小的环境.

See my banana-gym for an extremely small environment.

查看仓库主页:

https://github.com/openai/gym/blob/master/docs/creating_environments.md

步骤是:

1. 创建一个具有 PIP 包结构的新存储库

应该是这样的

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

有关其内容，请点击上面的链接.没有提到的细节特别是 foo_env.py 中的一些函数应该是什么样子.查看示例和 gym.openai.com/docs/ 有帮助.下面是一个例子:

For the contents of it, follow the link above. Details which are not mentioned there are especially how some functions in foo_env.py should look like. Looking at examples and at gym.openai.com/docs/ helps. Here is an example:

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

使用你的环境

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

示例

1. https://github.com/openai/gym-soccer
2. https://github.com/openai/gym-wikinav
3. https://github.com/alibaba/gym-starcraft
4. https://github.com/endgameinc/gym-malware
5. https://github.com/hackthemarket/gym-trading
6. https://github.com/tambetm/gym-minecraft
7. https://github.com/ppaquette/gym-doom
8. https://github.com/ppaquette/gym-super-mario
9. https://github.com/tuzzer/gym-maze

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