1 citations · 1 across the 2 of their papers we have counts for
3 papers
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…
cs.GT2024★ 1 cited
Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Zida Wu, Mathieu Lauriere, Samuel Jia Cong Chua +3
Mean Field Games (MFGs) have the ability to handle large-scale multi-agent systems, but learning Nash equilibria in MFGs remains a challenging task. In this paper, we propose a dee…