Multi-agent learning · 2026
RhoMARL: Robust Learning for Heterogeneous Multi-Agent Systems in Dynamic Environments
Zaipeng Xie, Wenhao Fang, Chentai Qiao, Yiming Zhao, and Wenzhan SongMachine Learning, 2026
About this work
A framework for learning in heterogeneous multi-agent systems under changing environmental conditions.
This work represents the group’s interest in robust coordination beyond fixed, homogeneous teams.