An overview: Attention mechanisms in multi-agent reinforcement learning

In recent years, in the field of Multi-Agent Systems (MAS), significant progress has been made in the research of algorithms that combine Reinforcement Learning (RL) with Attention Mechanism (AM). However, there is a lack of comprehensive reviews in this field. Based on this, this paper does the following work. Firstly, it reviews the classical algorithms of RL and AM; Secondly, it systematically introduces the combination of RL and AM; Thirdly, it sorts out their application in the field of single-agent and multi-agent, and pays attention to and looks forward to the challenges and future research prospects in this field; The last part offers a comprehensive analysis of the challenges encountered by research in this field and anticipates future research paths. The research offers a conceptual understanding and theoretical foundation for future applications of RL using AM and facilitates further in-depth study in this field for researchers.