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）优化作为一种旨在寻找针对给定问题的多样化、高性能解决方案集合的优化分支，其受欢迎程度日益上升。为了进一步发展，我们认为QD社区面临两项挑战：开发一个能够表征该领域日益增长算法集合的框架，并在支持不同领域研究人员和实践者的软件中实现该框架。为应对这些挑战，我们开发了pyribs，这是一个基于高度模块化的概念性QD框架构建的库。通过替换概念框架（进而替换pyribs）中的组件，用户可组合来自QD文献中的各类算法；同样重要的是，他们能够识别尚未探索的算法变体。此外，pyribs使该框架变得简单、灵活且易于使用，其用户友好的API辅以详尽的文档和教程。本文概述了pyribs的创建过程，重点聚焦于其实现的概念框架及指导该库开发的设计原则。