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arXiv · 2308.03665

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

Felix Chalumeau·Bryan Lim·Raphael Boige·Maxime Allard·Luca Grillotti·Manon Flageat·Valentin Macé·Arthur Flajolet·Thomas Pierrot·Antoine Cully
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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.

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Felix Chalumeau, Bryan Lim, Raphael Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Macé, Arthur Flajolet, Thomas Pierrot, Antoine Cully. 2023-08-07. QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration. https://arxiv.org/abs/2308.03665

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