Abstract
In a non-cooperative dynamic game, each player participating in a changing environment aims to optimize its actions selfishly. In this paper, we focus our analysis on a class of games, namely dynamic potential game in multiagent systems. The problems of the game with constraints and without constraints are both considered; in both cases, we propose algorithms to learn the Nash equilibrium (NE) in a distributed fashion. The idea of NE learning is relied on two-time-scale dynamics and convex optimization. A numerical example is presented to verify the effectiveness of the proposed methods.
| Original language | English |
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| Title of host publication | ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings |
| Publisher | IEEE Computer Society |
| Pages | 266-271 |
| Number of pages | 6 |
| ISBN (Electronic) | 9788993215137 |
| DOIs | |
| State | Published - Dec 13 2017 |
| Event | 17th International Conference on Control, Automation and Systems, ICCAS 2017 - Jeju, Korea, Republic of Duration: Oct 18 2017 → Oct 21 2017 |
Publication series
| Name | International Conference on Control, Automation and Systems |
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| Volume | 2017-October |
| ISSN (Print) | 1598-7833 |
Conference
| Conference | 17th International Conference on Control, Automation and Systems, ICCAS 2017 |
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| Country/Territory | Korea, Republic of |
| City | Jeju |
| Period | 10/18/17 → 10/21/17 |
Funding
This work was supported by the Korea Electric Power Corporation through Korea Electrical Engineering & Science Research Institute under Grant R15XA03-00 and by DGIST (Daegu Gyeongbuk Institute of Science and Technology).
Keywords
- Distributed learning
- Distributed optimization
- Game theory
- Singular perturbation theory