Quality-Diversity with Limited Resources
Ren-Jian Wang, Ke Xue, Cong Guan, Chao Qian

TL;DR
This paper introduces RefQD, a novel method for quality-diversity algorithms that significantly reduces resource consumption while maintaining or improving performance, enabling more efficient training with limited computational resources.
Contribution
RefQD decomposes neural networks to share representations, reducing resource overhead and addressing mismatch issues, thus improving resource efficiency in QD algorithms.
Findings
RefQD uses only 16% GPU memory on QDax and 3.7% on Atari.
RefQD achieves comparable or better performance than existing sample-efficient QD algorithms.
RefQD demonstrates excellent performance across various tasks with limited resources.
Abstract
Quality-Diversity (QD) algorithms have emerged as a powerful optimization paradigm with the aim of generating a set of high-quality and diverse solutions. To achieve such a challenging goal, QD algorithms require maintaining a large archive and a large population in each iteration, which brings two main issues, sample and resource efficiency. Most advanced QD algorithms focus on improving the sample efficiency, while the resource efficiency is overlooked to some extent. Particularly, the resource overhead during the training process has not been touched yet, hindering the wider application of QD algorithms. In this paper, we highlight this important research question, i.e., how to efficiently train QD algorithms with limited resources, and propose a novel and effective method called RefQD to address it. RefQD decomposes a neural network into representation and decision parts, and shares…
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Taxonomy
TopicsAuction Theory and Applications
MethodsSparse Evolutionary Training · Focus
