Learning in discrete-time average-cost mean-field games
dc.contributor.author | Anahtarcı, Berkay | |
dc.contributor.author | Karıksız, Can Deha | |
dc.contributor.author | Saldı, Naci | |
dc.date.accessioned | 2022-08-08T13:02:14Z | |
dc.date.available | 2022-08-08T13:02:14Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 0743-1546 | en_US |
dc.identifier.uri | http://hdl.handle.net/10679/7777 | |
dc.identifier.uri | https://ieeexplore.ieee.org/document/9682954 | |
dc.description.abstract | In this paper, we consider learning of discrete-time mean-field games under an average cost criterion. We propose a Q-iteration algorithm via Banach Fixed Point Theorem to compute the mean-field equilibrium when the model is known. We then extend this algorithm to the learning setting by using fitted Q-iteration and establish the probabilistic convergence of the proposed learning algorithm. Our work on learning in average-cost mean-field games appears to be the first in the literature. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2021 60th IEEE Conference on Decision and Control (CDC) | |
dc.rights | restrictedAccess | |
dc.title | Learning in discrete-time average-cost mean-field games | en_US |
dc.type | Conference paper | en_US |
dc.publicationstatus | Published | en_US |
dc.contributor.department | Özyeğin University | |
dc.contributor.authorID | (ORCID 0000-0001-6200-4398 & YÖK ID 331624) Anahtarcı, Berkay | |
dc.contributor.authorID | (ORCID 0000-0001-8890-2196 & YÖK ID ) Karıksız, Deha | |
dc.contributor.authorID | (ORCID 0000-0002-2677-7366 & YÖK ID 283091) Saldı, Naci | |
dc.contributor.ozuauthor | Anahtarcı, Berkay | |
dc.contributor.ozuauthor | Karıksız, Can Deha | |
dc.contributor.ozuauthor | Saldı, Naci | |
dc.identifier.volume | 2021-December | en_US |
dc.identifier.startpage | 3048 | en_US |
dc.identifier.endpage | 3053 | en_US |
dc.identifier.wos | WOS:000781990302115 | |
dc.identifier.doi | 10.1109/CDC45484.2021.9682954 | en_US |
dc.identifier.scopus | SCOPUS:2-s2.0-85126035903 | |
dc.relation.publicationcategory | Conference Paper - International - Institutional Academic Staff |
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