A numerical scheme for a mean field game in some queueing systems based on Markov chain approximation method
Not on the map. Why?
Abstract (arXiv)
We use the Markov chain approximation method to construct approximations for the solution of the mean field game (MFG) with reflecting barriers studied in Bayraktar, Budhiraja, and Cohen (2017). The MFG is formulated in terms of a controlled reflected diffusion with a cost function that depends on the reflection terms in addition to the standard variables: state, control, and the mean field term. This MFG arises from the asymptotic analysis of an N-player game for single server queues with strategic servers. By showing that our scheme is an almost contraction, we establish the convergence of this numerical scheme over a small time interval.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Aug 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
3 papers.
- 2024Deep Backward and Galerkin Methods for the Finite State Master Equationcited by 6
- 2022Linear convergence of a policy gradient method for some finite horizon continuous time control problemscited by 4
- 2026Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Datacited by 0
- 2026Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Data25 Jun 2026
- 2024Deep Backward and Galerkin Methods for the Finite State Master Equation8 Mar 2024
- 2022Linear convergence of a policy gradient method for some finite horizon continuous time control problems22 Mar 2022