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

页码：4349 / 4354

页数：6

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