  We consider quadratic, nonmonotone generalized Nash equilibrium problems with
symmetric interactions among the agents. Albeit this class of games is known to
admit a potential function, its formal expression can be unavailable in several
real-world applications. For this reason, we propose a two-layer Nash
equilibrium seeking scheme in which a central coordinator exploits noisy
feedback from the agents to design personalized incentives for them. By making
use of those incentives, the agents compute a solution to an extended game, and
then return feedback measures to the coordinator. We show that our algorithm
returns an equilibrium if the coordinator is endowed with standard learning
policies, and corroborate our results on a numerical instance of a hypomonotone
game.
