The inclusion of physical information in machine learning frameworks has revolutionized
many application areas. This involves enhancing the learning process by incorporating …

Non-cooperative and cooperative games with a very large number of players have many
applications but remain generally intractable when the number of players increases …

This paper proposes a scalable learning framework to solve a system of coupled forward–
backward partial differential equations (PDEs) arising from mean field games (MFGs). The …

We propose a discrete time graphon game formulation on continuous state and action
spaces using a representative player to study stochastic games with heterogeneous …

Reinforcement learning (RL) has been developed for mean field games over graphs (G-
MFG) in social media and network economics, in which the transition of agents between a …

A traffic system can be interpreted as a multiagent system, wherein vehicles choose the most
efficient driving approaches guided by interconnected goals or strategies. This paper aims to …

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 …

This paper proposes a physics-informed neural operator (PINO) framework to solve a
system of coupled forward-backward partial differential equations (PDEs) arising from mean …

Mean field games (MFGs) model the interactions within a large-population multi-agent
system using the population distribution. Traditional learning methods for MFGs are based …

Neural network-based methods for solving Mean-Field Games (MFGs) equilibria have
garnered significant attention for their effectiveness in high-dimensional problems. However …