pyribs: A Bare-Bones Python Library for Quality Diversity Optimization
pyribs: A Bare-Bones Python Library for Quality Diversity Optimization
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DOI：
10.1145/3583131.3590374
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发表时间：
2023-03
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影响因子：
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通讯作者：
Bryon Tjanaka;Matthew C. Fontaine;David H. Lee;Yulun Zhang;Nivedit Reddy Balam;N. Dennler;Sujay S. Garlanka;Nikitas Dimitri Klapsis;S. Nikolaidis
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文献类型：
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作者：
Bryon Tjanaka;Matthew C. Fontaine;David H. Lee;Yulun Zhang;Nivedit Reddy Balam;N. Dennler;Sujay S. Garlanka;Nikitas Dimitri Klapsis;S. Nikolaidis
近年来，质量多样性 (QD) 优化越来越受欢迎，这是优化的一个分支，旨在为给定问题找到一系列多样化、高性能的解决方案。为了进一步发展，我们认为 QD 社区面临两个挑战：开发一个框架来代表该领域不断增长的算法阵列，并在支持一系列研究人员和从业者的软件中实现该框架。为了应对这些挑战，我们开发了 Pyribs，这是一个基于高度模块化的概念 QD 框架构建的库。通过替换概念框架中的组件，从而替换 Pyribs 中的组件，用户可以从 QD 文献中编写算法；同样重要的是，他们可以识别未探索的算法变化。此外，pyribs 使该框架变得简单、灵活且易于访问，并具有由大量文档和教程支持的用户友好 API。本文概述了 Pyribs 的创建，重点介绍了它实现的概念框架以及指导图书馆开发的设计原则。 Pyribs 可在 https://pyribs.org 上获取
Recent years have seen a rise in the popularity of quality diversity (QD) optimization, a branch of optimization that seeks to find a collection of diverse, high-performing solutions to a given problem. To grow further, we believe the QD community faces two challenges: developing a framework to represent the field's growing array of algorithms, and implementing that framework in software that supports a range of researchers and practitioners. To address these challenges, we have developed pyribs, a library built on a highly modular conceptual QD framework. By replacing components in the conceptual framework, and hence in pyribs, users can compose algorithms from across the QD literature; equally important, they can identify unexplored algorithm variations. Furthermore, pyribs makes this framework simple, flexible, and accessible, with a user-friendly API supported by extensive documentation and tutorials. This paper overviews the creation of pyribs, focusing on the conceptual framework that it implements and the design principles that have guided the library's development. Pyribs is available at https://pyribs.org