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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Modular Reinforcement Learning (RL) library (implemented in PyTorch, JAX, and NVIDIA Warp) with support for Gymnasium/Gym, NVIDIA Isaac Lab, MuJoCo Playground and other environments
| Date | Stars |
|---|---|
| 2026-07-24 | 1082 |
| 2026-07-25 | 1082 |
| 2026-07-28 | 1082 |
| 2026-07-30 | 1082 |
| 2026-07-31 | 1083 |
| 2026-08-02 | 1083 |
| 2026-08-05 | 1085 |
| 2026-08-06 | 1085 |
Today
— stars today
This week
+3 stars this week
This month
— stars this month
Momentum
3.0
growth rate 0.28%/day
[](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/pre-commit.yml)
[](https://github.com/Toni-SM/skrl/actions/workflows/tests-torch.yml)
[](https://github.com/Toni-SM/skrl/actions/workflows/tests-jax.yml)
[](https://github.com/Toni-SM/skrl/actions/workflows/tests-warp.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>
**Documentation:** <strong>https://skrl.readthedocs.io</strong>
**Description**: ``skrl`` is an open-source modular library for Reinforcement Learning written in Python
(implemented in [PyTorch](https://pytorch.org/), [JAX](https://jax.readthedocs.io) and [NVIDIA Warp](https://nvidia.github.io/warp/))
and designed with a focus on modularity, readability, simplicity, and transparency of algorithm implementation.
In addition to supporting
OpenAI [Gym](https://www.gymlibrary.dev),
Farama [Gymnasium](https://gymnasium.farama.org) and [PettingZoo](https://pettingzoo.farama.org),
[ManiSkill](https://maniskill.readthedocs.io/en/latest/index.html),
among other environment interfaces, it allows loading and configuring
NVIDIA [Isaac Lab](https://isaac-sim.github.io/IsaacLab/index.html) and
[MuJoCo Playground](https://playground.mujoco.org/)
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>
### Refer to the documentation for details and examples: https://skrl.readthedocs.io
<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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Pablo Villanueva Domingo · Computer Vision Center - Universitat Autònoma de Barcelona · Spain
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b017ae593d25a7e2, topic:reinforcement-learning, topic:gymnasium, desc:reinforcement learning
matched fp:b017ae593d25a7e2, topic:deep-learning, topic:jax