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…
Can LLMs Understand Social Norms in Autonomous Driving Games?
Boxuan Wang, Haonan Duan, Yanhao Feng +4
Social norm is defined as a shared standard of acceptable behavior in a society. The emergence of social norms fosters coordination among agents without any hard-coded rules, which…
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving
Yongjie Fu, Anmol Jain, Xuan Di +2
The advancement of autonomous driving technologies necessitates increasingly sophisticated methods for understanding and predicting real-world scenarios. Vision language models (VL…
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…
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…