AI 打游戏拿金牌，但赢不了人类老手
让 AI 自己读规则、挑对手、看录像、改策略，像人类选手一样备战比赛——这套「对抗式启发学习」在 12 个经典对抗游戏里跑了一轮，最强配置拿下 6 块金牌，但剩下 6 条人类排行榜榜首没被撼动。规则越复杂的游戏，AI 表现越差；它确实会从自己的对局录像里学，也会偷看别人的录像学。这不是你明天能用的东西，但它指向一个更远的可能：AI 不再只是会下棋的引擎，而是能自己备战、自己迭代的选手。
📄 原文摘要(英文)
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.