Quality-Diversity (QD) algorithms seek to discover diverse, high-performing solutions across a behavior space, in contrast to conventional optimization methods that target a single optimum. Adversarial problems present unique challenges for QD approaches, as the competing nature of opposing sides creates interdependencies that complicate the evolution process. Existing QD methods applied to such scenarios typically fix one side, constraining the open-endedness. We present Generational Adversarial MAP-Elites (GAME), a coevolutionary QD algorithm that evolves both sides by alternating which side is evolved at each generation. By integrating a vision embedding model (VEM), our approach eliminates the need for domain-specific behavior descriptors and instead operates on video. We validate GAME across three distinct adversarial domains: a multi-agent battle game, a soft-robot wrestling environment, and a deck building game. We validate that all its components are necessary, that the VEM is effective in two different domains, and that GAME finds better solutions than one-sided QD baselines. Our experiments reveal several evolutionary phenomena, including arms race-like dynamics, enhanced novelty through generational extinction, and the preservation of neutral mutations as crucial stepping stones toward the highest performance. While GAME successfully illuminates all three adversarial problems, its capacity for truly open-ended discovery remains constrained by the nature of the search spaces used in this paper. These findings show GAME's broad applicability and highlight opportunities for future research into open-ended adversarial coevolution. Code and videos are available at: https://github.com/Timothee-ANNE/GAME

翻译：质量多样性(QD)算法旨在发现行为空间中多样化且高性能的解决方案，这与传统优化方法追求单一最优解形成对比。对抗性问题的竞争性本质使得对立双方之间存在相互依赖关系，这为QD方法带来了独特挑战，并导致进化过程复杂化。现有应用于此类场景的QD方法通常固定其中一方，从而限制了开放性的发展。我们提出世代对抗MAP-Elites(GAME)，一种协同进化QD算法，通过在每一代交替进化双方来同时优化两侧。通过集成视觉嵌入模型(VEM)，我们的方法消除了对领域特定行为描述符的需求，转而直接基于视频进行操作。我们在三个不同对抗性领域验证了GAME：多智能体对战游戏、软体机器人摔跤环境以及卡牌构筑游戏。我们验证了所有组件的必要性、VEM在两个不同领域的有效性，并证明GAME能找到优于单侧QD基线的解决方案。实验揭示了多种进化现象，包括类似军备竞赛的动态机制、通过世代灭绝增强新颖性，以及将中性突变保留为通向最高性能的关键垫脚石。尽管GAME成功照亮了所有三个对抗性问题，但其实现真正开放性发现的能力仍受限于本文所使用的搜索空间性质。这些发现展示了GAME的广泛适用性，并为未来开放性对抗性协同进化研究指明了方向。代码和视频详见：https://github.com/Timothee-ANNE/GAME