DETERMINISTIC MEAN FIELD GAMES ON NETWORKS: A LAGRANGIAN APPROACH

This paper is devoted to finite horizon deterministic mean field games in which the state space is a network. The agents control their velocity, and when they occupy a vertex, they can enter into any incident edge. The running and terminal costs are assumed to be continuous in each edge but not necessarily globally continuous on the network. A Lagrangian formulation is proposed and studied. It leads to relaxed equilibria consisting of probability measures on admissible trajectories. The existence of such relaxed equilibria is obtained. The proof requires the existence of optimal trajectories and a closed graph property for the map which associates with each point the set of optimal trajectories starting from that point. To any relaxed equilibrium corresponds a mild solution of the mean field game, i.e., a pair (u, m) made of the value function u of a related optimal control problem, and a family m = (m(t))t of probability measures on the network. Given m, the value function u is a viscosity solution of a Hamilton--Jacobi problem on the network. Regularity properties of u and a weak form of a continuity equation satisfied by m are investigated.