Learning generalized Nash equilibria in multi-agent dynamical systems via extremum seeking control
S. Krilašević (TU Delft - Team Sergio Grammatico)
S. Grammatico (TU Delft - Team Sergio Grammatico, TU Delft - Team Bart De Schutter)
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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.
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