Multi-agent learning · 2026
Boosting Efficient Experience Exchange in Sparse-Reward Multi-Agent Reinforcement Learning
Jianan Zhang, Zaipeng Xie, Nuo Yang, Juguang Jin, and Wenzhan SongMachine Learning, 2026
About this work
This paper studies how agents can exchange useful experience when rewards are sparse and learning signals are limited.
It provides a concrete introduction to exploration and knowledge sharing in cooperative learning.