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New learning dynamics achieve constant swap regret in general-sum games

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new deterministic and uncoupled learning dynamics for multiplayer general-sum games. These dynamics achieve constant individual swap regret for every player, independent of the game's horizon. The method involves players predicting deviation gains to update transition matrices and playing stationary distributions, with a proof leveraging potential arguments and higher-order prediction analysis. An adversarial variant using a common-prefix switching wrapper maintains self-play bounds and guarantees individual swap regret in adversarial settings. AI

IMPACT This research could inform the development of more robust AI agents capable of strategic decision-making in complex, multi-agent environments.

RANK_REASON The cluster contains an academic paper detailing new theoretical findings in game theory. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New learning dynamics achieve constant swap regret in general-sum games

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The cluster contains an academic paper detailing new theoretical findings in game theory. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Tung Mai ·

    Constant Swap Regret in General-Sum Games via Optimistic Transition Matrices

    arXiv:2609.16751v1 Announce Type: cross Abstract: We give deterministic and uncoupled learning dynamics for finite multiplayer general-sum games under full-information feedback that achieve constant individual swap regret, independent of the horizon $T$. With $n$ players and at m…

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