Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230300170-9.doi: 10.11896/jsjkx.230300170

• Artificial Intelligenc • Previous Articles     Next Articles

Survey of Multi-agent Deep Reinforcement Learning Based on Value Function Factorization

GAO Yuzhao, NIE Yiming   

  1. National Innovation Institute of Defense Technology,Academic of Military Science,Beijing 100071,China
  • Published:2024-06-06
  • About author:GAO Yuzhao,born in 1994,postgra-duate.His main research interests include multi-agent deep reinforcement learning,UGV and task planning.
    NIE Yiming,born in 1982,associate research fellow.His main research inte-rests include intelligence unmanned systems and UGV.

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Abstract: The multi-agent deep reinforcement learning is an extension of the deep reinforcement learning method to the multi-agents problem,in which the multi-agents deep reinforcement learning based on the value function factorization has achieved better performance and is a hotspot for research and application at present.This paper introduces the main principles and framework of the multi-agents deep reinforcement learning based on the value function factorization.Based on the recent related research,three research hotspots are summarized:the problem of improving the fitting ability of mixing network,the problem of improving the convergence effect and the problem of improving the scalability of algorithms,and the reasons for the three hotspot problems are analyzed in terms of algorithm constraints,environmental complexity and neural network limitations.The existing research is classified according to the problems to be solved and the methods to be used,the common points of similar methods are summarized,and the advantages and disadvantages of different methods are analyzed;the application of multi-agent deep reinforcement learning method based on value function decomposition in two hot fields of network node control and unmanned formation control is expounded.

Key words: Multi-agent deep reinforcement learning, Value function factorization, Fitting ability, Convergence effect, Scalability

CLC Number: 

  • TP181
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