13 citations · 18 across the 3 of their papers we have counts for
4 papers
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
Mathieu Laurière, Sarah Perrin, Sertan Girgin +8
Mean Field Games (MFGs) have been introduced to efficiently approximate games with very large populations of strategic agents. Recently, the question of learning equilibria in MFGs…
Solving N-player dynamic routing games with congestion: a mean field approach
Theophile Cabannes, Mathieu Lauriere, Julien Perolat +7
The recent emergence of navigational tools has changed traffic patterns and has now enabled new types of congestion-aware routing control like dynamic road pricing. Using the funda…
Quantifying the sustainability impact of Google Maps: A case study of Salt Lake City
Neha Arora, Theophile Cabannes, Sanjay Ganapathy +6
Google Maps uses current and historical traffic trends to provide routes to drivers. In this paper, we use microscopic traffic simulation to quantify the improvements to both trave…
Quasi-Dynamic Traffic Assignment using High Performance Computing
Cy Chan, Anu Kuncheria, Bingyu Zhao +5
Traffic assignment methods are some of the key approaches used to model flow patterns that arise in transportation networks. Since static traffic assignment does not have a notion…