Quality-Diversity optimisation (QD) has proven to yield promising results across a broad set of applications. However, QD approaches struggle in the presence of uncertainty in the environment, as it impacts their ability to quantify the true performance and novelty of solutions. This problem has been highlighted multiple times independently in previous literature. In this work, we propose to uniformise the view on this problem through four main contributions. First, we formalise a common framework for uncertain domains: the Uncertain QD setting, a special case of QD in which fitness and descriptors for each solution are no longer fixed values but distribution over possible values. Second, we propose a new methodology to evaluate Uncertain QD approaches, relying on a new per-generation sampling budget and a set of existing and new metrics specifically designed for Uncertain QD. Third, we propose three new Uncertain QD algorithms: Archive-sampling, Parallel-Adaptive-sampling and Deep-Grid-sampling. We propose these approaches taking into account recent advances in the QD community toward the use of hardware acceleration that enable large numbers of parallel evaluations and make sampling an affordable approach to uncertainty. Our final and fourth contribution is to use this new framework and the associated comparison methods to benchmark existing and novel approaches. We demonstrate once again the limitation of MAP-Elites in uncertain domains and highlight the performance of the existing Deep-Grid approach, and of our new algorithms. The goal of this framework and methods is to become an instrumental benchmark for future works considering Uncertain QD.

翻译：质量-多样性优化（Quality-Diversity Optimisation，QD）已在广泛的应用领域中展现出良好前景。然而，当环境存在不确定性时，QD方法面临挑战，因为不确定性会削弱其量化解决方案真实性能与新颖性的能力。先前文献已多次独立指出这一问题。本研究通过四项主要贡献，致力于统一对该问题的认识。首先，我们为不确定领域形式化了一个通用框架：不确定QD设定（Uncertain QD setting），这是QD的一种特例，其中每个解的适应度与描述子不再是固定值，而是可能取值的分布。其次，我们提出了一种新的不确定QD方法评估方法论，该方法基于新的逐代采样预算以及一组专门为不确定QD设计的现有和新指标。第三，我们提出了三种新的不确定QD算法：Archive-sampling、Parallel-Adaptive-sampling和Deep-Grid-sampling。这些方法考虑了QD领域近期在硬件加速应用方面的进展，从而能够进行大量并行评估，使采样成为应对不确定性的一种可行方案。第四项也是最后一项贡献，是运用这一新框架及关联比较方法，对现有及新提出的方法进行基准测试。我们再次证明了MAP-Elites在不确定领域中的局限性，并凸显了现有Deep-Grid方法及我们新算法的性能。此框架与方法的目标，是成为未来不确定QD研究的核心基准工具。