aiqbench analysis
This research presents ECHO-OFTRL, a novel algorithm that eliminates the polylogarithmic dependence on the time horizon for individual regret in decentralized multi-player games. By integrating an exponential moving average cascade into optimistic follow-the-regularized-leader dynamics, the method achieves deterministic convergence with regret bounds dependent only on player count and action set sizes. The work advances theoretical guarantees for uncoupled learning systems without relying on external coordination or prior knowledge of game structure.
Constant Individual Regret in General Games
arXiv:2608.31166v1 Announce Type: new 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 fully uncoupled. If $m_{\max}$ denotes the largest action-set size, then, simultaneously for every horizon $T\geq1$, it guarantees that each of the $N$ players in the game incurs regret upper bounded by $O(\textrm{poly}(N, \log m_{\max}))$. Our algorithm leverages a new form of optimism inspired by modern filter design.
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 fully uncoupled. If $m_{\max}$ denotes the largest action-set size, then, simultaneously for every horizon $T\geq1$, it guarantees that each of the $N$ players in the game incurs regret upper bounded by $O(\textrm{poly}(N, \log m_{\max}))$. Our algorithm leverages a new form of optimism inspired by modern filter design.
Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT)
arXiv:2608.31166 [cs.LG]
(or arXiv:2608.31166v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.31166 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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