6 papers
-Potential Games for Decentralized Control of Connected and Automated Vehicles
Xuan Di, Anran Hu, Zhexin Wang +1
Designing scalable and safe control strategies for large populations of connected and automated vehicles (CAVs) requires accounting for strategic interactions among heterogeneous a…
Mean-Field Games with Constraints
Anran Hu, Zijiu Lyu
This paper introduces a framework of Constrained Mean-Field Games (CMFGs), where each agent solves a constrained Markov decision process (CMDP). This formulation captures scenarios…
MF-OML: Online Mean-Field Reinforcement Learning with Occupation Measures for Large Population Games
Anran Hu, Junzi Zhang
Reinforcement learning for multi-agent games has attracted lots of attention recently. However, given the challenge of solving Nash equilibria for large population games, existing…
MFGLib: A Library for Mean-Field Games
Xin Guo, Anran Hu, Matteo Santamaria +2
Mean-field games (MFGs) are limiting models to approximate -player games, with a number of applications. Despite the ever-growing numerical literature on computation of MFGs, th…
Continuous-time mean field games: a primal-dual characterization
Xin Guo, Anran Hu, Jiacheng Zhang +1
This paper establishes a primal-dual formulation for continuous-time mean field games (MFGs) and provides a complete analytical characterization of the set of all Nash equilibria (…
Homogenization and Mean-Field Approximation for Multi-Player Games
Rama Cont, Anran Hu
We investigate how the framework of mean-field games may be used to investigate strategic interactions in large heterogeneous populations. We consider strategic interactions in a p…