Quality-Diversity (QD) algorithms are designed to generate collections of high-performing solutions while maximizing their diversity in a given descriptor space. However, in the presence of unpredictable noise, the fitness and descriptor of the same solution can differ significantly from one evaluation to another, leading to uncertainty in the estimation of such values. Given the elitist nature of QD algorithms, they commonly end up with many degenerate solutions in such noisy settings. In this work, we introduce Archive Reproducibility Improvement Algorithm (ARIA); a plug-and-play approach that improves the reproducibility of the solutions present in an archive. We propose it as a separate optimization module, relying on natural evolution strategies, that can be executed on top of any QD algorithm. Our module mutates solutions to (1) optimize their probability of belonging to their niche, and (2) maximize their fitness. The performance of our method is evaluated on various tasks, including a classical optimization problem and two high-dimensional control tasks in simulated robotic environments. We show that our algorithm enhances the quality and descriptor space coverage of any given archive by at least 50%.

翻译：质量-多样性（QD）算法旨在生成一组高性能解决方案，同时最大化它们在给定描述子空间中的多样性。然而，在存在不可预测噪声的情况下，同一解决方案的适应度和描述子在不同评估之间可能存在显著差异，导致对这些值的估计存在不确定性。鉴于QD算法的精英主义特性，在此类噪声环境中，它们通常会产生大量退化解决方案。在本文中，我们引入存档可复现性改进算法（ARIA）；这是一种即插即用的方法，用于提升存档中解决方案的可复现性。我们将其作为独立的优化模块提出，该模块基于自然进化策略，可在任何QD算法之上运行。我们的模块对解决方案进行变异，以（1）优化其归属于当前生态位的概率，并（2）最大化其适应度。我们在多种任务上评估了所提方法的性能，包括一个经典优化问题以及两个模拟机器人环境中的高维控制任务。结果表明，我们的算法能将任意给定存档的质量和描述子空间覆盖度提升至少50%。