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.

翻译：近年来，质量多样性（QD）优化作为一类旨在针对给定问题寻找多样化且高性能解的优化分支，日益受到广泛关注。为推动该领域的进一步发展，我们认为QD社区面临两大挑战：一是构建能够表征该领域日益丰富的算法体系的理论框架，二是将该框架实现为可支持不同层次研究者与实践者的软件工具。为应对这些挑战，我们开发了基于高度模块化概念性QD框架的pyribs库。通过替换概念框架（进而替换pyribs库）中的组件，用户既能组合QD文献中的现有算法，更重要的是，还能发现尚未探索的算法变体。此外，pyribs通过配备完善文档与教程的用户友好型API，使该框架兼具简洁性、灵活性与易用性。本文概述pyribs的创建过程，重点阐述其实现的概念框架及指导该库开发的设计原则。