gym README

The project's README from github.com/openai/gym. As one markdown file: README.md.

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Important Notice

The team that has been maintaining Gym since 2021 has moved all future development to Gymnasium, a drop in replacement for Gym (import gymnasium as gym), and Gym will not be receiving any future updates. Please switch over to Gymnasium as soon as you're able to do so. If you'd like to read more about the story behind this switch, please check out this blog post.

Gym

Gym is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API. Since its release, Gym's API has become the field standard for doing this.

Gym documentation website is at https://www.gymlibrary.dev/, and you can propose fixes and changes to it here.

Gym also has a discord server for development purposes that you can join here: https://discord.gg/nHg2JRN489

Installation

To install the base Gym library, use pip install gym.

This does not include dependencies for all families of environments (there's a massive number, and some can be problematic to install on certain systems). You can install these dependencies for one family like pip install gym[atari] or use pip install gym[all] to install all dependencies.

We support Python 3.7, 3.8, 3.9 and 3.10 on Linux and macOS. We will accept PRs related to Windows, but do not officially support it.

API

The Gym API's API models environments as simple Python env classes. Creating environment instances and interacting with them is very simple- here's an example using the "CartPole-v1" environment:

import gym
env = gym.make("CartPole-v1")
observation, info = env.reset(seed=42)

for _ in range(1000):
    action = env.action_space.sample()
    observation, reward, terminated, truncated, info = env.step(action)

    if terminated or truncated:
        observation, info = env.reset()
env.close()

Please note that this is an incomplete list, and just includes libraries that the maintainers most commonly point newcommers to when asked for recommendations.

Environment Versioning

Gym keeps strict versioning for reproducibility reasons. All environments end in a suffix like "_v0". When changes are made to environments that might impact learning results, the number is increased by one to prevent potential confusion.

MuJoCo Environments

The latest "_v4" and future versions of the MuJoCo environments will no longer depend on mujoco-py. Instead mujoco will be the required dependency for future gym MuJoCo environment versions. Old gym MuJoCo environment versions that depend on mujoco-py will still be kept but unmaintained. To install the dependencies for the latest gym MuJoCo environments use pip install gym[mujoco]. Dependencies for old MuJoCo environments can still be installed by pip install gym[mujoco_py].

Citation

A whitepaper from when Gym just came out is available https://arxiv.org/pdf/1606.01540, and can be cited with the following bibtex entry:

@misc{1606.01540,
  Author = {Greg Brockman and Vicki Cheung and Ludwig Pettersson and Jonas Schneider and John Schulman and Jie Tang and Wojciech Zaremba},
  Title = {OpenAI Gym},
  Year = {2016},
  Eprint = {arXiv:1606.01540},
}

Release Notes

There used to be release notes for all the new Gym versions here. New release notes are being moved to releases page on GitHub, like most other libraries do. Old notes can be viewed here.

gym (openai/gym)

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Gym
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这是干什么的?Gym 是一个开源的 Python 库,用于开发和比较强化学习算法。它为学习算法提供了一套与环境交互的标准 API,并在此 API 之上提供了一组标准的环境。
它是我在自己代码里用的库,还是一个独立程序?它是一个 Python 库,需要导入到你自己的训练代码中使用,不是独立应用。环境被建模为简单的 Python 类——你可以创建像 'CartPole-v1' 这样的环境实例并与它交互。
它支持哪些 Python 版本和操作系统?支持 Linux 和 macOS 上的 Python 3.7、3.8、3.9 和 3.10。针对 Windows 的 PR 会被接受,但 Windows 不是官方支持的系统。
Gym 还在积极维护吗?不——这个仓库已经归档了。维护团队把后续开发全部转移到了 Gymnasium,一个可以直接替换的替代品(用法是 import gymnasium as gym),Gym 不会再有任何更新,所以新项目应该直接从 Gymnasium 开始。
使用一个环境大概是什么样子?你把环境实例化为一个 Python 类的实例,比如 'CartPole-v1',然后通过标准 API 与它交互。环境都带有严格的版本后缀,以保证实验结果可以复现。
我可以把它和哪些强化学习库搭配使用?CleanRL 适合新手,提供易于参考的实现;Tianshou 适合喜欢调整算法的有经验的用户;RLlib 用于分布式训练和推理;PettingZoo 用于多智能体环境。
为什么环境名字末尾带有像 'v0' 这样的版本号?这是为了保证可复现性的严格版本管理:只要对环境的改动可能影响学习结果,版本号就会更新,这样不同环境行为下的结果就不会被悄悄地拿来做比较。
MuJoCo 环境有什么变化?从 v4 开始,它们依赖 mujoco 而不是 mujoco-py,安装方式是 pip install gym[mujoco]。基于旧 mujoco-py 的版本仍然可以通过 gym[mujocopy] 获取,但已不再维护。
What are similar projects?dopamine, Gymnasium, AirSim, hypoarena, FluxVLA, lerobot, sktime, dqn-adaptive-cpu-scheduler
Which topics is it in?simulation, algorithms, reinforcement-learning, deprecated, benchmark, openai, framework, research, machine-learning, api, library, ai
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