QDax: A Library for Quality-Diversity and Population-Based Algorithms with Hardware Acceleration
Abstract
QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimisation algorithms in Jax. The library serves as a versatile tool for optimisation purposes, ranging from black-box optimisation to continuous control. QDax offers implementations of popular QD, Neuroevolution, and Reinforcement Learning (RL) algorithms, supported by various examples. All the implementations can be just-in-time compiled with Jax, facilitating efficient execution across multiple accelerators, including GPUs and TPUs. These implementations effectively demonstrate the framework's flexibility and user-friendliness, easing experimentation for research purposes. Furthermore, the library is thoroughly documented and has 93% test coverage.
Cite
Text
Chalumeau et al. "QDax: A Library for Quality-Diversity and Population-Based Algorithms with Hardware Acceleration." Machine Learning Open Source Software, 2024.Markdown
[Chalumeau et al. "QDax: A Library for Quality-Diversity and Population-Based Algorithms with Hardware Acceleration." Machine Learning Open Source Software, 2024.](https://mlanthology.org/mloss/2024/chalumeau2024jmlr-qdax/)BibTeX
@article{chalumeau2024jmlr-qdax,
title = {{QDax: A Library for Quality-Diversity and Population-Based Algorithms with Hardware Acceleration}},
author = {Chalumeau, Felix and Lim, Bryan and Boige, Raphaël and Allard, Maxime and Grillotti, Luca and Flageat, Manon and Macé, Valentin and Richard, Guillaume and Flajolet, Arthur and Pierrot, Thomas and Cully, Antoine},
journal = {Machine Learning Open Source Software},
year = {2024},
pages = {1-16},
volume = {25},
url = {https://mlanthology.org/mloss/2024/chalumeau2024jmlr-qdax/}
}