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Multiagent Online Learning in Time-Varying Games

Author

Listed:
  • Benoit Duvocelle

    (Department of Quantitative Economics, Maastricht University, NL–6200 MD Maastricht, Netherlands)

  • Panayotis Mertikopoulos

    (Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG, 38000 Grenoble, France; Criteo AI Lab, 38130 Echirolles, France)

  • Mathias Staudigl

    (Department of Advanced Computing Sciences, Maastricht University, NL–6200 MD Maastricht, Netherlands)

  • Dries Vermeulen

    (Department of Quantitative Economics, Maastricht University, NL–6200 MD Maastricht, Netherlands)

Abstract

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.

Suggested Citation

  • Benoit Duvocelle & Panayotis Mertikopoulos & Mathias Staudigl & Dries Vermeulen, 2023. "Multiagent Online Learning in Time-Varying Games," Mathematics of Operations Research, INFORMS, vol. 48(2), pages 914-941, May.
    • Handle: RePEc:inm:ormoor:v:48:y:2023:i:2:p:914-941
    DOI: 10.1287/moor.2022.1283
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    References listed on IDEAS

    as
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