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Research PaperResearchia:202609.01004

Constant Individual Regret in General Games

Mingyang Liu

Abstract

Uncoupled no-regret dynamics provide a decentralized route to equilibrium, but prior guarantees for individual regret retain a polylogarithmic dependence on the horizon. We remove this dependence for every finite $N$-player normal-form game under full-information feedback. We introduce \emph{ECHO-OFTRL}: optimistic follow-the-regularized-leader (OFTRL) equipped with an EMA cascade for high-order optimism (ECHO), where EMA denotes exponential moving average. The algorithm is deterministic and ful...

Submitted: September 1, 2026Subjects: Machine Learning; Data Science

Description / Details

Uncoupled no-regret dynamics provide a decentralized route to equilibrium, but prior guarantees for individual regret retain a polylogarithmic dependence on the horizon. We remove this dependence for every finite NN-player normal-form game under full-information feedback. We introduce \emph{ECHO-OFTRL}: optimistic follow-the-regularized-leader (OFTRL) equipped with an EMA cascade for high-order optimism (ECHO), where EMA denotes exponential moving average. The algorithm is deterministic and fully uncoupled. If mmaxm_{\max} denotes the largest action-set size, then, simultaneously for every horizon T1T\geq1, it guarantees that each of the NN players in the game incurs regret upper bounded by O(poly(N,logmmax))O(\textrm{poly}(N, \log m_{\max})). Our algorithm leverages a new form of optimism inspired by modern filter design.


Source: arXiv:2608.31166v1 - http://arxiv.org/abs/2608.31166v1 PDF: https://arxiv.org/pdf/2608.31166v1 Original Link: http://arxiv.org/abs/2608.31166v1

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Date:
Sep 1, 2026
Topic:
Data Science
Area:
Machine Learning
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