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README.md

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 # gym
 
-A toolkit for developing and comparing reinforcement learning algorithms.
+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
+
+[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://pre-commit.com/) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+## 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).