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arXiv · 2007.11437

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

Suad Krilašević·Sergio Grammatico
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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 a novel continuous-time solution algorithm that uses regular projections and first-order information. As second main contribution, we design a data-driven variant of the former algorithm where each agent estimates their individual pseudo-gradient via zero-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 algorithms to connectivity control in robotic sensor networks and distributed wind farm optimization.

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Suad Krilašević, Sergio Grammatico. 2020-07-21. Learning generalized Nash equilibria in multi-agent dynamical systems via extremum seeking control. https://arxiv.org/abs/2007.11437

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