TL;DR
The paper introduces GAME, a coevolutionary QD algorithm that evolves both sides of adversarial problems using a vision embedding model, outperforming one-sided baselines across multiple domains.
Contribution
It presents a novel generational adversarial MAP-Elites algorithm that coevolves adversaries without domain-specific descriptors, validated on diverse adversarial tasks.
Findings
GAME finds better solutions than one-sided QD baselines.
The VEM effectively operates on video data in multiple domains.
Evolutionary phenomena like arms races and neutral mutations are observed.
Abstract
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…
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