A system-level approach to generalized feedback Nash equilibrium seeking in partially observed games
arXiv:2503.24159
The paper introduces an algorithm based on System Level Synthesis to compute generalized feedback Nash equilibria in partially observed, linear stochastic dynamic games, ensuring closed-loop stability and handling operational and communication constraints, with a demonstration on a decentralized power grid.
Abstract
This work proposes an algorithm for seeking generalized feedback Nash equilibria (GFNE) in noncooperative dynamic games. The focus is on cyber-physical systems with dynamics which are linear, stochastic, potentially unstable, and partially observed. We employ System Level Synthesis (SLS) to reformulate the problem as the search for an equilibrium profile of closed-loop responses to noise, which can then be used to reconstruct a stabilizing output-feedback policy. Under this setup, we leverage monotone operator theory to design a GFNE-seeking algorithm capable to enforce closed-loop stability, operational constraints, and communication constraints onto the control policies. This algorithm is amenable to numerical implementation and we provide conditions for its convergence. We demonstrate our approach in a simulated experiment on the noncooperative stabilization of a decentralized power grid.