A fascinating aspect of nature lies in its ability to produce a collection of organisms that are all high-performing in their niche. Quality-Diversity (QD) methods are evolutionary algorithms inspired by this observation, that obtained great results in many applications, from wing design to robot adaptation. Recently, several works demonstrated that these methods could be applied to perform neuro-evolution to solve control problems in large search spaces. In such problems, diversity can be a target in itself. Diversity can also be a way to enhance exploration in tasks exhibiting deceptive reward signals. While the first aspect has been studied in depth in the QD community, the latter remains scarcer in the literature. Exploration is at the heart of several domains trying to solve control problems such as Reinforcement Learning and QD methods are promising candidates to overcome the challenges associated. Therefore, we believe that standardized benchmarks exhibiting control problems in high dimension with exploration difficulties are of interest to the QD community. In this paper, we highlight three candidate benchmarks and explain why they appear relevant for systematic evaluation of QD algorithms. We also provide open-source implementations in Jax allowing practitioners to run fast and numerous experiments on few compute resources.

翻译：自然界的迷人之处在于其能够产生一组在各自生态位中表现卓越的生物体。质量多样性（QD）方法受此观察启发而演化而来，已在从机翼设计到机器人适应等多个应用中取得了显著成果。近年来，多项研究证明这些方法可用于执行神经进化，以解决大规模搜索空间中的控制问题。在此类问题中，多样性本身可成为目标，也可作为在存在欺骗性奖励信号的任务中增强探索的手段。虽然第一个方面已在QD社区得到深入研究，但后者在文献中仍较为少见。探索是多个试图解决控制问题领域（如强化学习）的核心，而QD方法有望克服相关挑战。因此，我们认为，具备探索难度的高维控制问题的标准化基准测试对QD社区具有重要价值。本文重点关注三个候选基准测试，解释其为何适用于系统评估QD算法，并提供了基于Jax的开源实现，使从业者能够利用少量计算资源快速开展大量实验。