QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration

Felix Chalumeau, Bryan Lim, Raphael Boige, Maxime Allard, Luca Grillotti +5 more
8/7/2023
cs.AIcs.NE

No official code found. Best implementation: adaptive-intelligent-robotics/QDax (361 stars, MIT, last commit 2025-10-30).

Abstract

QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimization algorithms in Jax. The library serves as a versatile tool for optimization purposes, ranging from black-box optimization 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 tested with 95\% coverage.

View on arXivView PDF

Code Implementations(1)

adaptive-intelligent-robotics/QDaxCommunity100%
MIT

Accelerated Quality-Diversity

36155PythonFeb 11, 202211 months agoMIT
neuroevolutionopen-endednessquality-diversityreinforcement-learning

Cite this paper

@article{chalumeau2023qdax,
  title  = {QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration},
  author = {Felix Chalumeau and Bryan Lim and Raphael Boige and Maxime Allard and Luca Grillotti and Manon Flageat and Valentin Macé and Arthur Flajolet and Thomas Pierrot and Antoine Cully},
  year   = {2023},
  eprint = {2308.03665},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.03665v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2308.03665.svg)](https://codeofpaper.com/paper/2308.03665)

Discussion