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）算法旨在探索行为空间中多样化且高性能的解决方案，与传统优化方法追求单一最优解不同。对抗性问题对QD方法提出了独特挑战，因为对立双方的竞争特性产生的相互依赖关系使进化过程复杂化。现有应用于此类场景的QD方法通常固定一方，限制了开放性。我们提出世代对抗MAP-Elites（GAME），一种协同进化QD算法，通过交替进化双方（每代交替进化一方）实现双方共同进化。通过集成视觉嵌入模型（VEM），该方法无需特定领域的行为描述符，而是直接基于视频进行操作。我们在三个不同的对抗领域验证了GAME：多智能体对战游戏、软体机器人摔跤环境和卡牌构筑游戏。实验表明：所有组件均不可或缺，VEM在两个不同领域均有效，且GAME找到的解决方案优于单侧QD基线。我们的实验揭示了多种进化现象，包括军备竞赛式动态、通过世代灭绝增强新颖性，以及将中性突变作为迈向最高性能的关键阶梯。尽管GAME成功揭示了所有三个对抗性问题，其真正开放式发现的能力仍受限于本文所用搜索空间的性质。这些发现展示了GAME的广泛适用性，并为未来开放式对抗性协同进化的研究提供了机遇。代码及视频获取地址：https://github.com/Timothee-ANNE/GAME