Learning generalized Nash equilibria in multi-agent dynamical systems via extremum seeking control

In this paper, we consider the problem of learning a generalized Nash equilibrium (GNE) in strongly monotone games. First, we propose semi-decentralized and distributed continuous-time solution algorithms that use regular projections and first-order information to compute a GNE with and without a ce...

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Bibliographic Details
Published in:Automatica (Oxford) 2021-11, Vol.133, p.109846, Article 109846
Main Authors: Krilašević, Suad, Grammatico, Sergio
Format: Article
Language:English
Subjects:
Extremum seeking control
Generalized Nash equilibrium learning
Multi-agent systems
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Summary:In this paper, we consider the problem of learning a generalized Nash equilibrium (GNE) in strongly monotone games. First, we propose semi-decentralized and distributed continuous-time solution algorithms that use regular projections and first-order information to compute a GNE with and without a central coordinator. As the second main contribution, we design a data-driven variant of the former semi-decentralized algorithm where each agent estimates their individual pseudogradient via zeroth-order information, namely, measurements of their individual cost function values, as typical of extremum seeking control. Third, we generalize our setup and results for multi-agent systems with nonlinear dynamics. Finally, we apply our methods to connectivity control in robotic sensor networks and almost-decentralized wind farm optimization.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2021.109846