# gym

A toolkit for developing and comparing reinforcement learning algorithms.第一批人工智能软件,通过“强化学习”方法建立人工智能系统的工具包

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## Important notice

### Due to issues with the domain registration, the documentation has been moved to [https://www.gymlibrary.dev/](https://www.gymlibrary.dev/) as opposed to the old .ml address.

## 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/](https://www.gymlibrary.dev/), and you can propose fixes and changes to it [here](https://github.com/Farama-Foundation/gym-docs).

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:

```python
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()
```

## Notable Related Libraries

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

* [CleanRL](https://github.com/vwxyzjn/cleanrl) is a learning library based on the Gym API. It is designed to cater to newer people in the field and provides very good reference implementations.
* [Tianshou](https://github.com/thu-ml/tianshou) is a learning library that's geared towards very experienced users and is design to allow for ease in complex algorithm modifications.
* [RLlib](https://docs.ray.io/en/latest/rllib/index.html) is a learning library that allows for distributed training and inferencing and supports an extraordinarily large number of features throughout the reinforcement learning space.
* [PettingZoo](https://github.com/Farama-Foundation/PettingZoo) is like Gym, but for environments with multiple agents.

## 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](https://github.com/openai/gym/releases) on GitHub, like most other libraries do. Old notes can be viewed [here](https://github.com/openai/gym/blob/31be35ecd460f670f0c4b653a14c9996b7facc6c/README.rst).