# gym

**Repository Path**: desny/gym

## Basic Information

- **Project Name**: gym
- **Description**: 学习gym源码
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2021-10-22
- **Last Updated**: 2024-06-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## 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 currently has two pieces of documentation: the [documentation website](http://gym.openai.com) and the [FAQ](https://github.com/openai/gym/wiki/FAQ). A new and more comprehensive documentation website is in the works.

## 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 and 3.9 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')

# env is created, now we can use it: 
for episode in range(10): 
    obs = env.reset()
    for step in range(50):
        action = env.action_space.sample()  # or given a custom model, action = policy(observation)
        nobs, reward, done, info = env.step(action)
```

## Notable Related Libraries

* [Stable Baselines 3](https://github.com/DLR-RM/stable-baselines3) is a learning library based on the Gym API. It is our recommendation for beginners who want to start learning things quickly.
* [RL Baselines3 Zoo](https://github.com/DLR-RM/rl-baselines3-zoo) builds upon SB3, containing optimal hyperparameters for Gym environments as well as code to easily find new ones. Such tuning is almost always required.
* The [Autonomous Learning Library](https://github.com/cpnota/autonomous-learning-library) and [Tianshou](https://github.com/thu-ml/tianshou) are two reinforcement learning libraries I like that are generally geared towards more experienced users.
* [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.

## Citation

A whitepaper from when OpenAI 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).