Survey of Multi-agent Deep Reinforcement Learning Based on Value Function Factorization
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 bet-ter 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 summa-rized,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.
Multi-agent deep reinforcement learningValue function factorizationFitting abilityConvergence effectScalability
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