A hybrid seeking approach for robust learning in multi-agent systems

This work presents a hybrid seeking control designed to achieve flexible robust learning in distributed multi-agent systems. We consider a set of n agents, with individual unknown but well behaved dynamics, seeking for an optimal equilibrium by using a hybrid control that requires only measurements of their cost functions. This equilibrium may correspond to a classic maximizer of a global potential function or a Nash equilibrium in a Lyapunov game. Making use of recent results in singular perturbation and averaging theory for hybrid dynamical systems we show convergence of the algorithm to a neighborhood of the optimal equilibrium.