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一、Gym的安装

1. 命令安装

pip install gym -y

2. 源码安装

git clone https://github.com/openai/gym
cd gym
pip install -e .

3. 示例安装验证
写一示例测试 gym_test.py文件内容如下：

import gym# 创建环境并指定渲染模式
env = gym.make('CartPole-v1', render_mode='human')
env.reset()# 运行1000步
for _ in range(1000):env.render()action = env.action_space.sample()obs, reward, terminated, truncated, info = env.step(action)# 如果环境终止，重置if terminated or truncated:env.reset()
env.close()

测试demo结果如下则安装成功。

# 可视化依赖
pip install pygamepython3 gym_test.py

测试 gym 能否成功运行 MuJoCo 环境

# version1: 纯gym_version >= '0.26.0'
import gymenv = gym.make("Ant-v4", render_mode="human")
observation, info = env.reset()  # reset() 现在返回 (observation, info)for _ in range(1000):action = env.action_space.sample()observation, reward, terminated, truncated, info = env.step(action)  # 新版返回5个值if terminated or truncated:  # 检查是否终止或截断observation, info = env.reset()  # 重置环境env.close()# version2: 兼容性gym_version各版本
import gym
import warnings# 获取当前 gym 版本
gym_version = gym.__version__
print(f"Using gym version: {gym_version}")# 初始化环境
env = gym.make("Ant-v4", render_mode="human")# 根据版本自适应处理 reset() 返回值
if gym_version >= '0.26.0':observation, info = env.reset()
else:observation = env.reset()info = {}  # 旧版没有 infotry:for _ in range(1000):action = env.action_space.sample()# 根据版本自适应处理 step() 返回值if gym_version >= '0.26.0':observation, reward, terminated, truncated, info = env.step(action)done = terminated or truncatedelse:observation, reward, done, info = env.step(action)truncated = False  # 旧版没有 truncated# 渲染环境if gym_version >= '0.26.0':env.render()  # 新版 render() 不需要参数else:env.render(mode='human')  # 旧版需要指定 mode# 检查是否结束if done:if gym_version >= '0.26.0':observation, info = env.reset()else:observation = env.reset()info = {}except KeyboardInterrupt:print("Simulation interrupted by user")finally:env.close()print("Environment closed")

测试结果如下则成功调用Mujoco环境。

二、Gym注册

当需要基于Gym基本框架搭建自定义环境时，如何将自己的环境移到gym中，被其所识别，这即是 Gym注册自定义环境 问题。