Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition· AI 智能体能否进步到顶峰？
Adversarial games have driven advances from heuristic search to reinforcement learning, yet learning and adapting strategies from limited samples remain challenging. AI agents offer an alternative by turning game experience into revisions of executable policies. Building on heuristic learning (HL), we formalize Adversarial Heuristic Learning (AHL), a paradigm that uses AI agents as learning engines to refine game policies and supporting software while keeping model weights fixed. We introduce AAArena, a benchmark comprising 12 authentic adversarial games and 1,920 archived human programs, with an evaluation protocol modeled on real-world game competitions. Agents interpret rules, choose opponents, analyze replays, and revise game agents to achieve their highest ranking within fixed match a
AI智能体通过对抗启发式学习在游戏竞赛中提升策略。
- 核心方法
- 提出对抗启发式学习(AHL)框架，在不改变模型权重的前提下，利用AI智能体作为学习引擎来优化游戏策略和支持软件。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 如何在有限样本下优化和适应对抗游戏策略。
- 关键实验
- 在AAArena基准上进行了大量实验，测试了不同AI智能体的学习和适应能力。
- 主要贡献
- 建立了AAArena基准，包含12个真实对抗游戏和1,920个归档人类程序，用于评估AHL的有效性。
- 意义与局限
- 展示了AI智能体在对抗游戏中通过学习经验改进策略的潜力，但资源消耗和策略泛化能力仍有限。