TU Darmstadt / ULB / TUprints

Payoff-Based Approach to Learning Nash Equilibria in Convex Games

Tatarenko, Tatiana ; Kamgarpour, Maryam (2023)
Payoff-Based Approach to Learning Nash Equilibria in Convex Games.
In: IFAC-PapersOnLine, 2017, 50 (1)
doi: 10.26083/tuprints-00023284
Article, Secondary publication, Publisher's Version

[img] Text
Copyright Information: CC BY-NC-ND 4.0 International - Creative Commons, Attribution NonCommercial, NoDerivs.

Download (1MB)
Item Type: Article
Type of entry: Secondary publication
Title: Payoff-Based Approach to Learning Nash Equilibria in Convex Games
Language: English
Date: 2023
Place of Publication: Darmstadt
Year of primary publication: 2017
Publisher: IFAC - International Federation of Automatic Control
Journal or Publication Title: IFAC-PapersOnLine
Volume of the journal: 50
Issue Number: 1
DOI: 10.26083/tuprints-00023284
Corresponding Links:
Origin: Secondary publication service

We consider multi-agent decision making, where each agent optimizes its cost function subject to constraints. Agents’ actions belong to a compact convex Euclidean space and the agents’ cost functions are coupled. We propose a distributed payoff-based algorithm to learn Nash equilibria in the game between agents. Each agent uses only information about its current cost value to compute its next action. We prove convergence of the proposed algorithm to a Nash equilibrium in the game leveraging established results on stochastic processes. The performance of the algorithm is analyzed with a numerical case study.

Uncontrolled Keywords: Multi-agent decision making, game theory, payoff-based algorithm
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-232846
Additional Information:

Zugl. Konferenzveröffentlichung: 20th IFAC World Congress, 09.-14.07.2017, Toulouse, France

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Intelligent Systems
Date Deposited: 01 Mar 2023 13:34
Last Modified: 26 May 2023 07:01
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/23284
PPN: 507986725
Actions (login required)
View Item View Item