Deterministic mean field games with control on the acceleration and state constraints

TL;DR

This paper investigates deterministic mean field games where agents control acceleration within constrained domains, establishing the existence of relaxed equilibria despite challenges posed by non-compact trajectories.

Contribution

It proves the existence of relaxed equilibria in deterministic mean field games with acceleration control and state constraints, addressing non-compactness issues in the proof.

Findings

Existence of relaxed equilibria under suitable assumptions

Handling non-compactness of optimal trajectories

Establishing closed graph properties for trajectory mappings

Abstract

We consider deterministic mean field games in which the agents control their acceleration and are constrained to remain in a domain of R n. We study relaxed equilibria in the Lagrangian setting; they are described by a probability measure on trajectories. The main results of the paper concern the existence of relaxed equilibria under suitable assumptions. The fact that the optimal trajectories of the related optimal control problem solved by the agents do not form a compact set brings a difficulty in the proof of existence. The proof also requires closed graph properties of the map which associates to initial conditions the set of optimal trajectories.