Equilibrium seeking in games under partial-decision information

The topic of this dissertation is the distributed computation of Generalized Nash Equilibria (GNEs) in multi-agent games with network structure. In particular, we design and analyze algorithms in the partial-decision information scenario (also named fully-distributed algorithms), where each agent can only rely on the information received by some neighbors over a communication graph, although its cost function depends on the actions of possibly all the competitors. This setup is motivated by engineering applications with no central system coordinator, for instance multi-agent autonomous driving or coverage control. While the agents can estimate the unknown variables via local data exchange and consensus protocols, the estimation error introduces critical challenges in the development of algorithms. In fact, the existing schemes for GNE seeking under partial-decision information suffer important limitations, as to performance and conditions to guarantee convergence. In this perspective, this thesis advances the theoretical understanding of games in the partial-decision information scenario, and provides a broad tool kit for designing efficient algorithmic solutions, suitable to cope with complex network interaction and dynamic coupling.