Multiagent Online Learning in Time-Varying Games

被引：5

作者：

Duvocelle, Benoit ^{[1
]}

Mertikopoulos, Panayotis ^{[2
,3
]}

Staudigl, Mathias ^{[4
]}

Vermeulen, Dries ^{[1
]}

机构：

[1] Maastricht Univ, Dept Quantitat Econ, NL-6200 MD Maastricht, Netherlands

[2] Univ Grenoble Alpes, LIG, Grenoble INP, CNRS,Inria, F-38000 Grenoble, France

[3] Criteo AI Lab, F-38130 Echirolles, France

[4] Maastricht Univ, Dept Adv Comp Sci, NL-6200 MD Maastricht, Netherlands

来源：

MATHEMATICS OF OPERATIONS RESEARCH | 2023年 / 48卷 / 02期

关键词：

dynamic regret; Nash equilibrium; mirror descent; time-varying games; STOCHASTIC-APPROXIMATION; OPTIMIZATION; DYNAMICS; CONVERGENCE; GRADIENT; DESCENT; PLAY; FORM;

D O I：

10.1287/moor.2022.1283

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

We examine the long-run behavior of multiagent online learning in games that evolve over time. Specifically, we focus on a wide class of policies based on mirror descent, and we show that the induced sequence of play (a) converges to a Nash equilibrium in time-varying games that stabilize in the long run to a strictly monotone limit, and (b) it stays asymptotically close to the evolving equilibrium of the sequence of stage games (assuming they are strongly monotone). Our results apply to both gradient- and payoff-based feedback-that is, when players only get to observe the payoffs of their chosen actions.

引用

页码：914 / 941

页数：28

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