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Computer Science > Artificial Intelligence

arXiv:2308.03665 (cs)
[Submitted on 7 Aug 2023]

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

Authors: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.
Subjects: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:2308.03665 [cs.AI]
  (or arXiv:2308.03665v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2308.03665

Submission history

From: Felix Chalumeau [view email]
[v1] Mon, 7 Aug 2023 15:29:44 UTC (2,360 KB)
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