3 papers
cs.LG2025
Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics
Lorenzo Magnino, Kai Shao, Zida Wu +2
Mean field games (MFGs) have emerged as a powerful framework for modeling interactions in large-scale multi-agent systems. Despite recent advancements in reinforcement learning (RL…
cs.LG2025
Population-aware Online Mirror Descent for Mean-Field Games with Common Noise by Deep Reinforcement Learning
Zida Wu, Mathieu Lauriere, Matthieu Geist +2
Mean Field Games (MFGs) offer a powerful framework for studying large-scale multi-agent systems. Yet, learning Nash equilibria in MFGs remains a challenging problem, particularly w…
eess.SY2024
Decentralized Input and State Estimation for Multi-agent System with Dynamic Topology and Heterogeneous Sensor Network
Zida Wu, Ankur Mehta
A crucial challenge in decentralized systems is state estimation in the presence of unknown inputs, particularly within heterogeneous sensor networks with dynamic topologies. While…