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

• URL: http://arxiv.org/abs/2308.03665v1
• Date: Mon, 7 Aug 2023 15:29:44 GMT
• 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 Mac\'e, Arthur Flajolet, Thomas Pierrot, Antoine Cully
• Abstract summary: 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.
• Score: 3.8494302715990845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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