Deterministic Policy Gradient Based Formation Control for Multi-Agent Systems

被引:0
作者
Hong, Zhiying [1 ]
Wang, Qingling [1 ]
机构
[1] Southeast Univ, Sch Automat, Nanjing, Peoples R China
来源
2019 CHINESE AUTOMATION CONGRESS (CAC2019) | 2019年
关键词
formation control; multi-agent reinforcement learning; deterministic policy gradient;
D O I
10.1109/cac48633.2019.8996660
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper studies the problem of formation control of multi-agent systems with the reinforcement learning method. A novel multi-agent formation control algorithm is first proposed, which adopts the framework of centralized training with decentralized execution, and combines the deterministic policy gradient (DPG) method with multi-agent advantage function. Then, three scenarios under partial observable Markov games are presented to study the multi-agent formation control problem and verify the proposed algorithm. Simulation results show that the proposed algorithm is effective in achieving the multi-agent formation control tasks.
引用
收藏
页码:4349 / 4354
页数:6
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