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Conference Papers Year : 2021

On the characterization of equilibria of nonsmooth minimal-time mean field games with state constraints

Abstract

In this paper, we consider a first-order deterministic mean field game model inspired by crowd motion in which agents moving in a given domain aim to reach a given target set in minimal time. To model interaction between agents, we assume that the maximal speed of an agent is bounded as a function of their position and the distribution of other agents. Moreover, we assume that the state of each agent is subject to the constraint of remaining inside the domain of movement at all times, a natural constraint to model walls, columns, fences, hedges, or other kinds of physical barriers at the boundary of the domain. After recalling results on the existence of Lagrangian equilibria for these mean field games and the main difficulties in their analysis due to the presence of state constraints, we show how recent techniques allow us to characterize optimal controls and deduce that equilibria of the game satisfy a system of partial differential equations, known as the mean field game system.
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Dates and versions

hal-03317961 , version 1 (09-08-2021)
hal-03317961 , version 2 (14-09-2021)

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Saeed Sadeghi Arjmand, Guilherme Mazanti. On the characterization of equilibria of nonsmooth minimal-time mean field games with state constraints. IEEE 60th Conference on Decision and Control (CDC 2021), Dec 2021, Austin, Texas, United States. pp.5300-5305, ⟨10.1109/CDC45484.2021.9683104⟩. ⟨hal-03317961v2⟩
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