Skrl AI Agent

<img src="https://img.shields.io/badge/%F0%9F%A4%97%20models-hugging%20face-F8D521"> <br> <span>    </span>

<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, JAX and NVIDIA Warp) and designed with a focus on modularity, readability, simplicity, and transparency of algorithm implementation. In addition to supporting OpenAI Gym, Farama Gymnasium and PettingZoo, ManiSkill, among other environment interfaces, it allows loading and configuring NVIDIA Isaac Lab and MuJoCo Playground 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 branch or its documentation to access the latest updates to be released.

<br>

Citing this library

To cite this library in publications, please use the following reference:

@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}
}