Formation Control of Multi-agent Based on Deep Reinforcement Learning

被引：0

作者：

Pan, Chao ^{[1
]}

Nian, Xiaohong ^{[1
]}

Dai, Xunhua ^{[2
]}

Wang, Haibo ^{[1
]}

Xiong, Hongyun ^{[1
]}

机构：

[1] Cent South Univ, Sch Automat, Changsha 410075, Hunan, Peoples R China

[2] Cent South Univ, Sch Comp Sci & Engn, Changsha 410075, Hunan, Peoples R China

来源：

PROCEEDINGS OF 2022 INTERNATIONAL CONFERENCE ON AUTONOMOUS UNMANNED SYSTEMS, ICAUS 2022 | 2023年 / 1010卷

关键词：

Multi-agent systems; Formation control; Deep reinforcement learning;

D O I：

10.1007/978-981-99-0479-2_104

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 is based on the multi-agent deep deterministic policy gradient (MADDPG) deep reinforcement learning algorithm, combined with the Leader-Follow method to complete the multi-agent circular formation control problem, considering the distance constraints and angle constraints between the agents. Overcome the problem that it is difficult to accurately model objects in previous control methods, and do not need to care about network topology, system order and other preconditions. In addition, for the formation movement problem, we predefine a virtual leader that moves according to a random curve trajectory, and adopt a two-stage training method. Based on the circular formation behavior strategy, each agent continues to train to follow the virtual leader. Simulation experiments verify the effectiveness of the algorithm.

引用

页码：1149 / 1159

页数：11

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