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Journal Articles Mathematics of Operations Research Year : 2022

Multi-agent online learning in time-varying games

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We examine the long-run behavior of multi-agent 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 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-based and payoff-based feedback - i.e., when players only get to observe the payoffs of their chosen actions.

Dates and versions

hal-01891545 , version 1 (09-10-2018)



Benoît Duvocelle, Panayotis Mertikopoulos, Mathias Staudigl, Dries Vermeulen. Multi-agent online learning in time-varying games. Mathematics of Operations Research, In press, ⟨10.1287/moor.2022.1283⟩. ⟨hal-01891545⟩
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