arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.
By Panayotis Mertikopoulos
arXiv:2506. 03802v2 Announce Type: replace Abstract: We introduce a learning problem in a generalized two-sided matching market, where agents select actions to interact with their match.
By Andreas Athanasopoulos, Christos Dimitrakakis
arXiv:2510. 14907v2 Announce Type: replace-cross Abstract: We extend the study of learning in games to dynamics that exhibit non-asymptotic stability.
By Geelon So, Yi-An Ma
arXiv:2606. 19883v1 Announce Type: new Abstract: We study a multi-agent multi-armed bandit problem in the competitive setup with two-sided matching markets under a human centric decision making model.
By Ananya Kunisetty, Avishek Ghosh
arXiv:2606. 06486v1 Announce Type: new Abstract: In this paper, we study regret minimization in repeated games with \emph{adaptive} opponents who can respond based on histories of play.
By Mingyang Liu, Asuman Ozdaglar, Tiancheng Yu, Kaiqing Zhang
arXiv:2607. 11752v1 Announce Type: cross Abstract: For decades, static solution concepts (Nash, Correlated, and Coarse Correlated Equilibria) and the Price of Anarchy (PoA) have formed the bedrock of algorithmic game theory, with no-regret learning proving fast convergence to such game-theoretic equilibria.
By Georgios Piliouras, Ian Gemp, Siqi Liu, Luke Marris