Quality diversity (QD) is a branch of evolutionary computation that seeks high-quality and behaviorally diverse solutions to a problem. While adversarial problems are common, classical QD cannot be easily applied to them, as both the fitness and the behavior depend on the opposing solutions. Recently, Generational Adversarial MAP-Elites (GAME) has been proposed to coevolve both sides of an adversarial problem by alternating the execution of a multi-task QD algorithm against previous elites, called tasks. The original algorithm selects new tasks based on a behavioral criterion, which may lead to undesired dynamics due to inter-side dependencies. In addition, comparing sets of solutions cannot be done directly using classical QD measures due to side dependencies. In this paper, we (1) use an inter-variants tournament to compare the sets of solutions, ensuring a fair comparison, with 6 measures of quality and diversity, and (2) propose two tournament-informed task selection methods to promote higher quality and diversity at each generation. We evaluate the variants across three adversarial problems: Pong, a Cat-and-mouse game, and a Pursuers-and-evaders game. We show that the tournament-informed task selection method leads to higher adversarial quality and diversity. We hope that this work will help further advance adversarial quality diversity. Code, videos, and supplementary material are available at https://github.com/Timothee-ANNE/GAME_tournament_informed.

翻译：质量多样性（Quality Diversity，QD）是进化计算的一个分支，旨在为问题寻找高质量且行为多样化的解。尽管对抗性问题普遍存在，但经典QD方法难以直接应用于此类问题，因为其适应度与行为均依赖于对抗方的解。最近，研究者提出了代际对抗性MAP-Elites（GAME）算法，通过交替执行多任务QD算法来协同进化对抗问题的双方，其中先前精英解被视作任务。原始算法基于行为准则选择新任务，可能因双方依赖关系导致非期望的动态演化。此外，由于存在对抗方依赖，直接使用经典QD指标比较解集存在困难。本文中，我们（1）采用变体间锦标赛机制比较解集，通过6项质量和多样性指标确保公平比较；（2）提出两种基于锦标赛信息的任务选择方法，以促进每一代获得更高质量和多样性的解。我们在三个对抗性问题（Pong游戏、猫鼠博弈及追逃游戏）中评估了算法变体。实验表明，基于锦标赛信息的任务选择方法能显著提升对抗性质量与多样性。我们希望这项工作能进一步推动对抗性质量多样性研究的发展。代码、视频及补充材料详见 https://github.com/Timothee-ANNE/GAME_tournament_informed。