创建自定义 gym env 教程

gym-0.26.1
pygame-2.1.2
自定义环境 GridWolrdEnv

教程参考 官网自定义环境 ，我把一些可能有疑惑的地方讲解下。

首先整体文件结构, 这里省略了wrappers

gym-examples/
  main.py    # 这个是测试自定义的环境
  setup.py  
  gym_examples/
    __init__.py
    envs/
      __init__.py
      grid_world.py

先讲几个基础知识

1. init.py 的作用
   最主要的作用是: 将所在的目录标记为 Python 包的一部分。
   在 Python 中，一个包是一个包含模块（即 .py 文件）的目录，
   而 init.py 文件表明这个目录可以被视为一个包，允许从这个目录导入模块或其他包。
2. class里以 _ 开头的变量，说明是私有变量,以 _ 开头方法被视为私有方法。(默认的规定，但不强制)
3. 实例的变量的初始化可以不在 __init__函数里，比如在这里有些变量就是 在 reset 函数里初始化。

grid_world.py

原版的英文注释已经很清楚了,所以我们这里就是沿用就好了

import gym
from gym import spaces
import pygame
import numpy as np


class GridWorldEnv(gym.Env):
    metadata = {"render_modes": ["human", "rgb_array"], "render_fps":4}

    def __init__(self, render_mode=None, size=5):
        super().__init__()

        self.size = size   # The size of the square grid
        self.window_size = 512  # The size of the PyGame window

        # Observations are dictionaries with the agent's and the target's location.
        # Each location is encoded as an element of {0, ..., `size`}^2, i.e. MultiDiscrete([size, size]).
        self.observation_space = spaces.Dict(
            {
                "agent": spaces.Box(0, size - 1, shape=(2,), dtype=int),
                "target": spaces.Box(0, size - 1, shape=(2,), dtype=int)
            }
        )

        # We have 4 actions, corresponding to "right", "up", "left", "down"
        self.action_space = spaces.Discrete(4)
        """
               The following dictionary maps abstract actions from `self.action_space` to 
               the direction we will walk in if that action is taken.
               I.e. 0 corresponds to "right", 1 to "up" etc.
        """
        self._action_to_direction = {
            0: np.array([1, 0]),
            1: np.array([0, 1]),
            2: np.array([-1, 0]),
            3: np.array([0, -1])
        }

        assert render_mode is None or render_mode in self.metadata["render_modes"]
        self.render_mode = render_mode

        """
               If human-rendering is used, `self.window` will be a reference
               to the window that we draw to. `self.clock` will be a clock that is used
               to ensure that the environment is rendered at the correct framerate in
               human-mode. They will remain `None` until human-mode is used for the
               first time.
        """
        self.window = None
        self.clock = None

    def _get_obs(self):
        return {"agent": self._agent_location, "target": self._target_location}

    def _get_info(self):
        return {"distance": np.linalg.norm(self._agent_location - self._target_location, ord=1)}

    def reset(self, seed=None, options=None):
        # We need the following line to seed self.np_random
        super().reset(seed=seed)

        # Choose the agent's location uniformly at random
        self._agent_location = self.np_random.integers(0, self.size, size=2, dtype=int)

        # We will sample the target's location randomly until it does not coincide with the agent's location
        self._target_location = self._agent_location
        while np.array_equal(self._target_location, self._agent_location):
            self._target_location = self.np_random.integers(
                0, self.size, size=2, dtype=int
            )

        observation = self._get_obs()
        info = self._get_info()

        if self.render_mode == "human":
            self._render_frame()

        return observation, info

    def step(self, action):
        # Map the action (element of {0,1,2,3}) to the direction we walk in
        direction = self._action_to_direction[action]
        # We use `np.clip` to make sure we don't leave the grid
        self._agent_location = np.clip(
            self._agent_location + direction, 0, self.size - 1
        )
        # An episode is done iff the agent has reached the target
        terminated = np.array_equal(self._agent_location, self._target_location)
        reward = 1 if terminated else 0
        observation = self._get_obs()
        info = self._get_info()

        if self.render_mode == "human":
            self._render_frame()

        # truncated = False
        return observation, reward, terminated, False, info

    def render(self):
        if self.render_mode == "rgb_array":
            return self._render_frame()

    def _render_frame(self):
        if self.window is None and self.render_mode == "human":
            pygame.init()
            pygame.display.init()
            self.window = pygame.display.set_mode((self.window_size, self.window_size))
        if self.clock is None and self.render_mode == "human":
            self.clock = pygame.time.Clock()

        canvas = pygame.Surface((self.window_size, self.window_size))
        canvas.fill((255, 255, 255))
        pix_square_size = (
            self.window_size / self.size
        ) # The size of a single grid square in pixels

        # First we draw the target
        pygame.draw.rect(
            canvas,
            (255, 0, 0),
            pygame.Rect(
                pix_square_size * self._target_location,
                (pix_square_size, pix_square_size),
            )
        )
        # Now we draw the agent
        pygame.draw.circle(
            canvas,
            (0, 0, 255),
            (self._agent_location + 0.5) * pix_square_size,
            pix_square_size / 3,
        )
        # Finally, add some gridlines
        for x in range(self.size + 1):
            pygame.draw.line(
                canvas,
                0,
                (0, pix_square_size * x),
                (self.window_size, pix_square_size * x),
                width=3
            )
            pygame.draw.line(
                canvas,
                0,
                (pix_square_size * x, 0),
                (pix_square_size * x, self.window_size),
                width=3
            )

        if self.render_mode == "human":
            # The following line copies our drawings from `canvas` to the visible window
            self.window.blit(canvas, canvas.get_rect())
            pygame.event.pump()
            pygame.display.update()

            # We need to ensure that human-rendering occurs at the predefined framerate.
            # The following line will automatically add a delay to keep the framerate stable.
            self.clock.tick(self.metadata["render_fps"])
        else: # rgb_array
            return np.transpose(
                np.array(pygame.surfarray.pixels3d(canvas)),axes=(1, 0, 2)
            )

    def close(self):
        if self.window is not None:
            pygame.display.quit()
            pygame.quit()

同envs目录下的__init__.py

from gym_examples.envs.grid_world import GridWorldEnv

与envs同级别的__init__.py

这里是必需要通过register先注册环境的

from gym.envs.registration import register

register(
    id='gym_examples/GridWorld-v0',  # 可自定义,但是要唯一，不要与现有的有冲突
    entry_point='gym_examples.envs:GridWorldEnv', # 这个是根据包的路径和类名定义的
    max_episode_steps=300,
)

最外层的setup.py

主要的作用

1. 定义包的元数据包括 包名和版本号。
2. 管理依赖。
3. 如果其他人想要使用你的 gym_examples 包，他们只需要克隆你的代码库，并在包的根目录下运行 pip install .。这会自动安装 gym_examples 包以及指定版本的 gym 和 pygame。

所以本地开发测试的话 不用setup.py也没有问题,它主要是负责定义和管理包的分发和安装。

from setuptools import setup

setup(
    name="gym_examples",
    version="0.0.1",
    install_requires=["gym==0.26.1", "pygame==2.1.2"],
)

测试的 main.py

import gym
import gym_examples  # 这个就是之前定义的包

env = gym.make('gym_examples/GridWorld-v0', render_mode="human")

observation, info = env.reset()
done, truncated = False, False
while not done and not truncated:
    action = env.action_space.sample()
    observation, reward, done, truncated, info = env.step(action)
    
env.close()

实际效果