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• W3190641237 abstract "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." @default.
• W3190641237 created "2021-08-16" @default.
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• W3190641237 date "2021-11-01" @default.
• W3190641237 modified "2023-10-05" @default.
• W3190641237 title "Learning generalized Nash equilibria in multi-agent dynamical systems via extremum seeking control" @default.
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• W3190641237 doi "https://doi.org/10.1016/j.automatica.2021.109846" @default.
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