[](https://pypi.org/project/skrl)
[<img src="https://img.shields.io/badge/%F0%9F%A4%97%20models-hugging%20face-F8D521">](https://huggingface.co/skrl)
<br>
[](https://github.com/Toni-SM/skrl)
<span> </span>
[](https://skrl.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/Toni-SM/skrl/actions/workflows/python-test.yml)
[](https://github.com/Toni-SM/skrl/actions/workflows/pre-commit.yml)
<br>
<p align="center">
<a href="https://skrl.readthedocs.io">
<img width="300rem" src="https://raw.githubusercontent.com/Toni-SM/skrl/main/docs/source/_static/data/logo-light-mode.png">
</a>
</p>
<h2 align="center" style="border-bottom: 0 !important;">SKRL - Reinforcement Learning library</h2>
<br>
**skrl** is an open-source modular library for Reinforcement Learning written in Python (on top of [PyTorch](https://pytorch.org/) and [JAX](https://jax.readthedocs.io)) and designed with a focus on modularity, readability, simplicity, and transparency of algorithm implementation. In addition to supporting the OpenAI [Gym](https://www.gymlibrary.dev) / Farama [Gymnasium](https://gymnasium.farama.org) and [DeepMind](https://github.com/deepmind/dm_env) and other environment interfaces, it allows loading and configuring [NVIDIA Isaac Gym](https://developer.nvidia.com/isaac-gym/), [NVIDIA Isaac Orbit](https://isaac-orbit.github.io/orbit/index.html) and [NVIDIA Omniverse Isaac Gym](https://docs.omniverse.nvidia.com/isaacsim/latest/tutorial_gym_isaac_gym.html) environments, enabling agents' simultaneous training by scopes (subsets of environments among all available environments), which may or may not share resources, in the same run.
<br>
### Please, visit the documentation for usage details and examples
<strong>https://skrl.readthedocs.io</strong>
<br>
> **Note:** This project is under **active continuous development**. Please make sure you always have the latest version. Visit the [develop](https://github.com/Toni-SM/skrl/tree/develop) branch or its [documentation](https://skrl.readthedocs.io/en/develop) to access the latest updates to be released.
<br>
### Citing this library
To cite this library in publications, please use the following reference:
```bibtex
@article{serrano2023skrl,
author = {Antonio Serrano-Muñoz and Dimitrios Chrysostomou and Simon Bøgh and Nestor Arana-Arexolaleiba},
title = {skrl: Modular and Flexible Library for Reinforcement Learning},
journal = {Journal of Machine Learning Research},
year = {2023},
volume = {24},
number = {254},
pages = {1--9},
url = {http://jmlr.org/papers/v24/23-0112.html}
}
```
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