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New algorithm HOOD guarantees constant regret in general games

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new learning algorithm called HOOD (higher-order optimism with discounting) that guarantees a specific level of individual regret in general N-player normal form games. This algorithm, a variation of optimistic follow-the-regularized-leader (OptFTRL), combines a discounted (N+1)-th order predictor with entropic regularization. The method aims to reduce oscillations in play, a challenge that has hindered previous attempts to achieve constant regret in such games. This work shares similarities with independent research by Liu, Farina, and Ozdaglar, which also explored higher-order optimism for regret minimization. AI

IMPACT This research could advance theoretical understanding in multi-agent systems, potentially influencing future AI development in competitive or cooperative environments.

RANK_REASON The cluster describes a new academic paper detailing a novel algorithm for game theory. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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New algorithm HOOD guarantees constant regret in general games

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The cluster describes a new academic paper detailing a novel algorithm for game theory. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 Dansk(DA) · Omar Abbadi, Rida Laraki, Panayotis Mertikopoulos ·

    Constant regret in general games via higher-order optimism

    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…

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