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
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
收藏
页码:914 / 941
页数:28
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