10 citations · 12 across the 9 of their papers we have counts for
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cs.LG2026
Multi-Agent Reinforcement Learning via Agent-Specific Preference
Ni Mu, Yao Luan, Yiqin Yang +1
Multi-agent reinforcement learning (MARL) is a powerful framework for solving complex collaborative tasks, but it relies heavily on well-defined global reward functions. Designing…
cs.LG2026
COLLIE: Guiding Skill Discovery in Semantically Coherent Latent Space
Yao Luan, Ni Mu, Hanfei Ge +3
Unsupervised skill discovery (USD) aims to learn diverse behaviors without reward functions, but often results in task-irrelevant or hazardous behaviors due to uniform exploration.…
cs.AI2026
GlobeDiff: State Diffusion Process for Partial Observability in Multi-Agent Systems
Yiqin Yang, Xu Yang, Yuhua Jiang +8
In the realm of multi-agent systems, the challenge of \emph{partial observability} is a critical barrier to effective coordination and decision-making. Existing approaches, such as…