Dansk(DA) Constant regret in general games via higher-order optimism

新算法HOOD保证在一般博弈中实现恒定遗憾

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-04 04:00

研究人员开发了一种名为HOOD（高阶乐观主义与折扣）的新学习算法，该算法保证在一般N方正常形式博弈中实现特定水平的个体遗憾。该算法是乐观的正则化领导者跟随（OptFTRL）算法的一个变体，结合了折扣的（N+1）阶预测器和熵正则化。该方法旨在减少博弈中的振荡，这是以往在此类博弈中实现恒定遗憾的尝试所面临的挑战。这项工作与Liu、Farina和Ozdaglar的独立研究有相似之处，后者也探索了用于遗憾最小化的高阶乐观主义。 AI

影响这项研究可能推动多智能体系统理论理解的进步，并可能影响未来在竞争或合作环境中的AI发展。

排序理由该集群描述了一篇关于博弈论新算法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 Dansk(DA) · Omar Abbadi, Rida Laraki, Panayotis Mertikopoulos · 2026-09-04 04:00

高阶乐观主义在一般博弈中的持续后悔

arXiv:2609.04113v1 Announce Type: new Abstract: We introduce an uncoupled learning algorithm which, when employed by all players of an arbitrary $N$-player normal form game with up to $K$ actions per player, guarantees $O(N^3\log^2 K)$ individual regret, uniformly over the horizo…

本文如何被排名

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高阶乐观主义在一般博弈中的持续后悔

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