通过频率分析自适应选择游戏训练中的超参数，提升收敛速度。
Frequency-Based Hyperparameter Selection in Games
- 基于振荡动力学的频率估计，动态调整超参数
- 在纯旋转与混合场景中均加速训练，且开销极小
- 适合需要稳定优化的游戏类学习任务
平滑博弈中的学习机制与标准最小化有本质差异，因旋转动力学导致传统超参数调优方法失效。尽管关键，但博弈中的有效调参方法仍研究不足。以LookAhead（LA）为例，虽具优异实证性能，却引入需精细调节的额外参数。本文提出一种基于频率分析的博弈超参数选择方法，分析连续轨迹与离散动力学谱中的振荡特性。基于此，我们提出模态LookAhead（MoLA），可自适应地为具体问题选择超参数。理论证明其收敛性，并在实验中验证：在纯旋转游戏和混合场景下，MoLA均显著加速训练，且计算开销几乎可忽略。
原文摘要 · Abstract (English)
Learning in smooth games fundamentally differs from standard minimization due to rotational dynamics, which invalidate classical hyperparameter tuning strategies. Despite their practical importance, effective methods for tuning in games remain underexplored. A notable example is LookAhead (LA), which achieves strong empirical performance but introduces additional parameters that critically influence performance. We propose a principled approach to hyperparameter selection in games by leveraging frequency estimation of oscillatory dynamics. Specifically, we analyze oscillations both in continuous-time trajectories and through the spectrum of the discrete dynamics in the associated frequency-based space. Building on this analysis, we introduce \emph{Modal LookAhead (MoLA)}, an extension of LA that selects the hyperparameters adaptively to a given problem. We provide convergence guarantees and demonstrate in experiments that MoLA accelerates training in both purely rotational games and mixed regimes, all with minimal computational overhead.
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