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Gradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games

Zimmermann, Jan ; Tatarenko, Tatiana ; Willert, Volker ; Adamy, Jürgen (2021)
Gradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games.
Report, Bibliographie

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Kurzbeschreibung (Abstract)

We are concerned with finding Nash Equilibria in agent-based multi-cluster games, where agents are separated into distinct clusters. While the agents inside each cluster collaborate to achieve a common goal, the clusters are considered to be virtual players that compete against each other in a non-cooperative game with respect to a coupled cost function. In such scenarios, the inner-cluster problem and the game between the clusters need to be solved simultaneously. Therefore, the resulting inter-cluster Nash Equilibrium should also be a minimizer of the social welfare problem inside the clusters. In this work, this setup is cast as a distributed optimization problem with sparse state information. Hence, critical information, such as the agent’s cost functions, remain private. We present a distributed algorithm that converges witha linear rate to the optimal solution. Furthermore, we apply our algorithm to an extended cournot game to verify our theoretical results.

Typ des Eintrags: Report
Erschienen: 2021
Autor(en): Zimmermann, Jan ; Tatarenko, Tatiana ; Willert, Volker ; Adamy, Jürgen
Art des Eintrags: Bibliographie
Titel: Gradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games
Sprache: Englisch
Publikationsjahr: 2021
Kollation: 8 Seiten
URL / URN: https://arxiv.org/abs/2102.09406
Zugehörige Links:
Kurzbeschreibung (Abstract):

We are concerned with finding Nash Equilibria in agent-based multi-cluster games, where agents are separated into distinct clusters. While the agents inside each cluster collaborate to achieve a common goal, the clusters are considered to be virtual players that compete against each other in a non-cooperative game with respect to a coupled cost function. In such scenarios, the inner-cluster problem and the game between the clusters need to be solved simultaneously. Therefore, the resulting inter-cluster Nash Equilibrium should also be a minimizer of the social welfare problem inside the clusters. In this work, this setup is cast as a distributed optimization problem with sparse state information. Hence, critical information, such as the agent’s cost functions, remain private. We present a distributed algorithm that converges witha linear rate to the optimal solution. Furthermore, we apply our algorithm to an extended cournot game to verify our theoretical results.

Zusätzliche Informationen:

arXiv:2102.09406 [eess.SY], Submitted on 18 Feb 2021

Sachgruppe der Dewey Dezimalklassifikatin (DDC): 500 Naturwissenschaften und Mathematik > 530 Physik
Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Regelungsmethoden und Robotik (ab 01.08.2022 umbenannt in Regelungsmethoden und Intelligente Systeme)
Hinterlegungsdatum: 06 Dez 2023 09:31
Letzte Änderung: 19 Dez 2024 12:00
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