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Zida Wu

2 accepted papers

2025

Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics

NeurIPS 2025poster

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) for MFGs, existing methods are typically limited to finite spaces or stationary models, hindering their applicability…

Cited by 0SourceScholar
2022

Joint State and Input Estimation of Agent Based on Recursive Kalman Filter Given Prior Knowledge

ICRA 2022poster

Modern autonomous systems are purposed for many challenging scenarios, where agents will face unexpected events and complicated tasks. The presence of disturbance noise with control command and unknown inputs can negatively impact robot performance. Previous research of joint input and state estimat…

Cited by 1SourceScholar