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%.

本文提出了一种 Archive Reproducibility Improvement Algorithm（ARIA）的方法，基于自然进化策略，用于改善质量 - 多样性（Quality-Diversity）算法在噪声环境下的可重现性，并对其在优化问题和模拟机器人环境中进行了测试，得出其可以提高给定存档的质量和描述符空间覆盖率至少 50% 的结论。