通过多并行进化策略增强MAP-Elites
Enhancing MAP-Elites with Multiple Parallel Evolution Strategies
AI总结：
针对质量多样性算法在高维、不确定性场景下难以高效利用大规模并行评估资源的问题，提出可在单GPU运行多并行进化策略的MEMES算法，在多类任务中性能优于现有QD方法，可生成高性能多样化的可复现解。
摘要：
随着诸多领域中快速、大规模并行评估技术的发展，质量多样性（Quality-Diversity，QD）算法的潜力得到进一步释放——这类算法已在大量应用中展现出良好前景。然而，我们尚未弄清如何最优地利用海量评估资源，因为仅将其用于随机变异并非总能奏效。高维搜索空间是随机变异难以有效搜索的典型场景；另一类场景是不确定性环境，其中解的表现可能优于其真实水平，此时盲目评估更多解反而可能误导QD算法。\n本研究提出MAP-Elites-Multi-ES（MEMES），这是一种基于进化策略（Evolution Strategies，ES）的新型QD算法，旨在更高效地利用快速并行评估资源。MEMES可在单块GPU上同时运行最多100个独立的ES进程，每个进程都有专属的独立目标与专为QD优化设计的重置机制。\n实验表明，在黑盒优化与QD强化学习任务中，MEMES的表现均优于基于梯度和基于变异的QD算法，证明了其跨领域的优势；此外，在相同评估预算下，该方法在不确定性领域中的表现优于基于采样的QD方法。总体而言，MEMES可在易获取的硬件上通过大规模ES优化，生成高性能且具备多样性的可复现解。
英文摘要：
With the development of fast and massively parallel evaluations in many domains, Quality-Diversity (QD) algorithms, that already proved promising in a large range of applications, have seen their potential multiplied. However, we have yet to understand how to best use a large number of evaluations as using them for random variations alone is not always effective. High-dimensional search spaces are a typical situation where random variations struggle to effectively search. Another situation is uncertain settings where solutions can appear better than they truly are and naively evaluating more solutions might mislead QD algorithms. In this work, we propose MAP-Elites-Multi-ES (MEMES), a novel QD algorithm based on Evolution Strategies (ES) designed to exploit fast parallel evaluations more effectively. MEMES maintains multiple (up to 100) simultaneous ES processes, each with its own independent objective and reset mechanism designed for QD optimisation, all on just a single GPU. We show that MEMES outperforms both gradient-based and mutation-based QD algorithms on black-box optimisation and QD-Reinforcement-Learning tasks, demonstrating its benefit across domains. Additionally, our approach outperforms sampling-based QD methods in uncertain domains when given the same evaluation budget. Overall, MEMES generates reproducible solutions that are high-performing and diverse through large-scale ES optimisation on easily accessible hardware.
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