发现隐藏策略结构，让算法更快收敛到最优解。
The Hidden Game Problem
- 设计组合算法识别隐藏高收益策略集
- 实现最优外部与交换悔恨边界，快速收敛
- 适合研究AI对齐与复杂博弈的学者
本文研究一类具有巨大策略空间的游戏，源于AI对齐与语言博弈中的挑战。我们提出隐藏游戏问题：每位玩家均存在一个未知的策略子集，其收益始终高于其余策略。核心问题是能否设计高效后悔最小化算法，以发现并利用此类隐藏结构，在保持整体理性的同时达到子博弈均衡。我们通过组合后悔最小化技术，正面回答了该问题，实现了最优外部与交换悔恨界。该方法确保在隐藏子博弈中快速收敛至相关均衡，并利用隐藏结构提升计算效率。
原文摘要 · Abstract (English)
This paper investigates a class of games with large strategy spaces, motivated by challenges in AI alignment and language games. We introduce the hidden game problem, where for each player, an unknown subset of strategies consistently yields higher rewards compared to the rest. The central question is whether efficient regret minimization algorithms can be designed to discover and exploit such hidden structures, leading to equilibrium in these subgames while maintaining rationality in general. We answer this question affirmatively by developing a composition of regret minimization techniques that achieve optimal external and swap regret bounds. Our approach ensures rapid convergence to correlated equilibria in hidden subgames, leveraging the hidden game structure for improved computational efficiency.
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