Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition

Hugging Face Daily Papers 10/08/26, 12:00 AM Papers

ai-agents game-playing benchmark heuristic-learning reinforcement-learning adversarial-games

Summary

论文提出 Adversarial Heuristic Learning (AHL) 范式，并介绍 AAArena 基准，包含 12 个真实对抗游戏和 1,920 个历史人类程序，用于评估 AI 智能体能否通过修改可执行策略在长期游戏竞赛中达到顶尖水平。结果显示 Opus 5.5 搭配 Claude Code 获得 6 枚金牌，但在规则复杂的游戏中表现较弱。

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 and evaluation budgets. We evaluate completedmodels model and harness configurations: Opus5.5 with Claude Code earns 6 gold medals, while no evaluated configuration tops the remaining 6 human ladders. Performance is generally weaker in games with more complex rule specifications. Further experiments show that opponent selection and dense feedback support policy improvement, and that agents learn from both on-policy replays of their own matches and off-policy replays of other players' matches. These results highlight HL's potential in adversarial games and identify persistent challenges in game understanding, strategy implementation, and long-horizon policy development.

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Paper page - Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition

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Abstract

Adversarialgameshavedrivenadvancesfromheuristicsearchtoreinforcementlearning,yetlearningandadaptingstrategiesfromlimitedsamplesremainchallenging.AIagentsofferanalternativebyturninggameexperienceintorevisionsofexecutablepolicies.Buildingonheuristiclearning(HL),weformalizeAdversarialHeuristicLearning(AHL),aparadigmthatusesAIagentsaslearningenginestorefinegamepoliciesandsupportingsoftwarewhilekeepingmodelweightsfixed.WeintroduceAAArena,abenchmarkcomprising12authenticadversarialgamesand1,920archivedhumanprograms,withanevaluationprotocolmodeledonreal-worldgamecompetitions.Agentsinterpretrules,chooseopponents,analyzereplays,andrevisegameagentstoachievetheirhighestrankingwithinfixedmatchandevaluationbudgets.Weevaluatecompletedmodelsmodelandharnessconfigurations:Opus5.5withClaudeCodeearns6goldmedals,whilenoevaluatedconfigurationtopstheremaining6humanladders.Performanceisgenerallyweakeringameswithmorecomplexrulespecifications.Furtherexperimentsshowthatopponentselectionanddensefeedbacksupportpolicyimprovement,andthatagentslearnfrombothon-policyreplaysoftheirownmatchesandoff-policyreplaysofotherplayers’matches.TheseresultshighlightHL’spotentialinadversarialgamesandidentifypersistentchallengesingameunderstanding,strategyimplementation,andlong-horizonpolicydevelopment.

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