Multi-agent online learning in time-varying games - POLARIS - Performance analysis and Optimization of LARge Infrastructure and Systems Accéder directement au contenu
Article Dans Une Revue Mathematics of Operations Research Année : 2023

Multi-agent online learning in time-varying games

Résumé

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.
Fichier principal
Vignette du fichier
Main.pdf (737.42 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01891545 , version 1 (21-12-2023)

Licence

Identifiants

Citer

Benoît Duvocelle, Panayotis Mertikopoulos, Mathias Staudigl, Dries Vermeulen. Multi-agent online learning in time-varying games. Mathematics of Operations Research, 2023, 48 (2), pp.914-941. ⟨10.1287/moor.2022.1283⟩. ⟨hal-01891545⟩
164 Consultations
7 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More