English(EN) Sublogarithmic Swap Regret in Multiplayer General-Sum Games via Hybrid Regularization

新的博弈论方法实现次对数交换遗憾值

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-06 04:00

研究人员开发了一种用于多方一般和博弈的新方法，该方法显著减少了交换遗憾值，提高了收敛到相关均衡的效率。这种新颖的方法结合了Blum--Mansour归约和乐观的正则化领导者跟随（optimistic follow-the-regularized-leader）方法，并利用了混合正则化器。该技术在这一环境中首次提供了次对数的个体交换遗憾值保证，对复杂博弈场景中的博弈分布具有影响。 AI

排序理由学术论文发布在arXiv上，详细介绍了博弈论中的一种新理论方法。[lever_c_demoted from research: ic=1 ai=0.1]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Taira Tsuchiya · 2026-08-06 04:00

通过混合正则化实现多人一般和博弈中的亚对数交换遗憾

arXiv:2608.04149v1 Announce Type: cross Abstract: Swap regret governs the rate at which uncoupled learning dynamics converge to correlated equilibria in multiplayer general-sum games. Under full-information feedback, the best previous guarantee when every player follows the same …

本文如何被排名

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通过混合正则化实现多人一般和博弈中的亚对数交换遗憾

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