Learning generalized Nash equilibria in multi-agent dynamical systems via extremum seeking control
Systems and Control
2020-07-23 v1 Systems and Control
Optimization and Control
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.
Cite
@article{arxiv.2007.11437,
title = {Learning generalized Nash equilibria in multi-agent dynamical systems via extremum seeking control},
author = {Suad Krilašević and Sergio Grammatico},
journal= {arXiv preprint arXiv:2007.11437},
year = {2020}
}