First order Mean Field Games on networks

TL;DR

This paper studies deterministic mean field games on networks, establishing existence of equilibria, analyzing the value function via Hamilton-Jacobi equations, and exploring regularity and measure evolution on the network.

Contribution

It introduces a Lagrangian formulation for mean field games on networks and proves the existence of relaxed equilibria with associated PDE characterizations.

Findings

Existence of relaxed equilibria on networks.

Characterization of the value function via Hamilton-Jacobi equations.

Analysis of measure evolution and regularity properties.

Abstract

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 to 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 characterized by a Hamilton-Jacobi problem on the network. Regularity properties of $u$ and a weak form of a Fokker-Planck equation satisfied by $m$ are investigated.