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
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| 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 |
| Abstract: | 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 |
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