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Snapshot 0 · 2026-11-04T12:47:27Z · Python · MIT
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A standard API for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
README at capture
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<p align="center">
<a href="https://gymnasium.farama.org/" target = "_blank">
<img src="https://raw.githubusercontent.com/Farama-Foundation/Gymnasium/main/gymnasium-text.png" width="500px" />
</a>
</p>
Gymnasium 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. This is a fork of OpenAI's [Gym](https://github.com/openai/gym) library by its maintainers (OpenAI handed over maintenance a few years ago to an outside team), and is where future maintenance will occur going forward.
The documentation website is at [gymnasium.farama.org](https://gymnasium.farama.org), and we have a public discord server (which we also use to coordinate development work) that you can join here: https://discord.gg/bnJ6kubTg6
## Environments
Gymnasium includes the following families of environments along with a wide variety of third-party environments
* [Classic Control](https://gymnasium.farama.org/environments/classic_control/) - These are classic reinforcement learning based on real-world problems and physics.
* [Box2D](https://gymnasium.farama.org/environments/box2d/) - These environments all involve toy games based around physics control, using box2d based physics and PyGame-based rendering
* [Toy Text](https://gymnasium.farama.org/environments/toy_text/) - These environments are designed to be extremely simple, with small discrete state and action spaces, and hence easy to learn. As a result, they are suitable for debugging implementations of reinforcement learning algorithms.
* [MuJoCo](https://gymnasium.farama.org/environments/mujoco/) - A physics engine based environments with multi-joint control which are more complex than the Box2D environments.
* [Atari](https://ale.farama.org/) - Emulator of Atari 2600 ROMs simulated that have a high range of complexity for agents to learn.
* [Third-party](https://gymnasium.farama.org/environments/third_party_environments/) - A number of environments have been created that are compatible with the Gymnasium API. Be aware of the version that the software was created for and use the `apply_env_compatibility` in `gymnasium.make` if necessary.
## Installation
To install the base Gymnasium library, use `pip install gymnasium`
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 "gymnasium[atari]"` or use `pip install "gymnasium[all]"` to install all dependencies.
## API
The Gymnasium 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 gymnasium as 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 newcomers to when asked for recommendations.
* [CleanRL](https://github.com/vwxyzjn/cleanrl) is a learning library based on the Gymnasium API. It is designed to cater to newer people in the field and provides very good reference implementations.
* [PettingZoo](https://github.com/Farama-Foundation/PettingZoo) is a multi-agent version of Gymnasium with a number of implemented environments, for example, multi-agent Atari environments.
* The Farama Foundation also has a collection of many other [environments](https://farama.org/projects) that are maintained by the same team as Gymnasium and use the Gymnasium API.
## Environment Versioning
Gymnasium 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. These were inherited from Gym.
## Contributing
We welcome contributions from the community!
Please see our [CONTRIBUTING.md](https://github.com/Farama-Foundation/Gymnasium/blob/main/CONTRIBUTING.md) for details on how to get started.
## Support Gymnasium's Development
If you are financially able to do so and would like to support the development of Gymnasium, please join others in the community in [donating to us](https://github.com/sponsors/Farama-Foundation).
## Citation
You can cite Gymnasium using our [related paper](https://proceedings.neurips.cc/paper_files/paper/2025/hash/d7ff1795e8527f6443371c3933bdb52b-Abstract-Datasets_and_Benchmarks_Track.html) as:
```
@inproceedings{NEURIPS2025_d7ff1795,
author = {Towers, Mark and Kwiatkowski, Ariel and Balis, John and De Cola, Gianluca and Deleu, Tristan and Goul\~{a}o, Manuel and Andreas, Kallinteris and Krimmel, Markus and KG, Arjun and Perez-Vicente, Rodrigo and Terry, J and Pierr\'{e}, Andrea and Schulhoff, Sander and Tai, Jun Jet and Tan, Hannah and Younis, Omar G.},
booktitle = {Advances in Neural Information Processing Systems},
doi = {10.52202/085713-4916},
editor = {D. Belgrave and C. Zhang and H. Lin and R. Pascanu and P. Koniusz and M. Ghassemi and N. Chen},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Gymnasium: A Standard Interface for Reinforcement Learning Environments},
url = {https://proceedings.neurips.cc/paper_files/paper/2025/file/d7ff1795e8527f6443371c3933bdb52b-Paper-Datasets_and_Benchmarks_Track.pdf},
volume = {38, Main Conference},
year = {2025}
}
```