A differentiable neural framework for learning state- and time-dependent parameters of finite-state mean field games from population trajectories via implicit differentiation.
A Mean Field Games approach for multi-lane traffic management
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In this work we discuss an Mean Field Games approach to traffic management on multi-lane roads. Such approach is particularly indicated to model self driven vehicles with perfect information of the domain. The mathematical interest of the problem is related to the fact that the system of partial differential equations obtained in this case is not in the classic form, but it consists of two continuity equations (one for each lane) and a variational inequality, coming from the Hamilton-Jacobi theory of the hybrid control.
fields
cs.GT 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Neural Parameter Calibration for Finite-State Mean Field Games
A differentiable neural framework for learning state- and time-dependent parameters of finite-state mean field games from population trajectories via implicit differentiation.