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

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Document pages: 15 pages

Abstract: In this paper, we consider the problem of learning a generalized Nashequilibrium (GNE) in strongly monotone games. First, we propose a novelcontinuous-time solution algorithm that uses regular projections andfirst-order information. As second main contribution, we design a data-drivenvariant of the former algorithm where each agent estimates their individualpseudo-gradient via zero-order information, namely, measurements of theirindividual cost function values, as typical of extremum seeking control. Third,we generalize our setup and results for multi-agent systems with nonlineardynamics. Finally, we apply our algorithms to connectivity control in roboticsensor networks and distributed wind farm optimization.

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