不要仅依赖运气：在不确定环境中增强质量-多样性解的行为可复现性
Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains
AI总结：
针对质量-多样性算法在噪声环境中易产生退化解的问题，提出即插即用的ARIA优化模块，通过自然进化策略变异解以优化生态位归属概率和适应度，使存档质量和描述符空间覆盖率提升至少50%。
摘要：
质量-多样性（QD）算法旨在生成高性能解的集合，同时最大化它们在给定描述符空间中的多样性。然而，在不可预测的噪声存在时，同一解的适应度和描述符在不同评估间可能差异显著，导致这些值估计的不确定性。鉴于QD算法的精英主义本质，它们在这种噪声环境中通常会产生许多退化解。在本研究中，我们引入了存档可复现性改进算法（ARIA）；这是一种即插即用的方法，可提高存档中解的可复现性。我们将其作为一个独立的优化模块提出，依赖自然进化策略，可在任何QD算法之上执行。我们的模块对解进行变异，以（1）优化其属于其生态位的概率，并（2）最大化其适应度。我们方法在多种任务上进行了评估，包括一个经典优化问题和模拟机器人环境中的两个高维控制任务。我们证明，我们的算法可将任何给定存档的质量和描述符空间覆盖率提高至少50%。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
