A Systematic Review on Knowledge Transfer in Multi-Agent Systems Using Reinforcement Learning

This paper presents a systematic review on knowledge transfer in multi-agent systems using reinforcement learning. The review followed the PRISMA protocol, analyzing relevant articles from the IEEE Xplore, Scopus, and Web of Science databases. It highlighted research focused on multi-agent reinforcement learning, with works aiming to develop generic algorithms or frameworks for knowledge transfer. Recent approaches enhance agents’ efficiency and adaptability, reducing learning time. However, challenges related to communication robustness and knowledge representations’ compatibility persist.