10 citations · 10 across the 5 of their papers we have counts for
5 papers
Stochastic Semi-Gradient Descent for Learning Mean Field Games with Population-Aware Function Approximation
Chenyu Zhang, Xu Chen, Xuan Di
Mean field games (MFGs) model interactions in large-population multi-agent systems through population distributions. Traditional learning methods for MFGs are based on fixed-point…
Graphon Mean Field Games with a Representative Player: Analysis and Learning Algorithm
Fuzhong Zhou, Chenyu Zhang, Xu Chen +1
We propose a discrete time graphon game formulation on continuous state and action spaces using a representative player to study stochastic games with heterogeneous interaction amo…
A Single Online Agent Can Efficiently Learn Mean Field Games
Chenyu Zhang, Xu Chen, Xuan Di
Mean field games (MFGs) are a promising framework for modeling the behavior of large-population systems. However, solving MFGs can be challenging due to the coupling of forward pop…
Learn to Tour: Operator Design For Solution Feasibility Mapping in Pickup-and-delivery Traveling Salesman Problem
Bowen Fang, Xu Chen, Xuan Di
This paper aims to develop a learning method for a special class of traveling salesman problems (TSP), namely, the pickup-and-delivery TSP (PDTSP), which finds the shortest tour al…
Physics-Informed Deep Learning for Traffic State Estimation
Rongye Shi, Zhaobin Mo, Kuang Huang +2
Traffic state estimation (TSE), which reconstructs the traffic variables (e.g., density) on road segments using partially observed data, plays an important role on efficient traffi…