Cooperative multi-agent system for production control using reinforcement learning

被引：34

作者：

Dittrich, Marc-Andre ^{[1
]}

Fohlmeister, Silas ^{[1
]}

机构：

[1] Leibniz Univ Hannover, Inst Prod Engn & Machine Tools IFW, Hannover, Germany

来源：

CIRP ANNALS-MANUFACTURING TECHNOLOGY | 2020年 / 69卷 / 01期

关键词：

Production planning; Machine learning; Multi-agent system;

D O I：

10.1016/j.cirp.2020.04.005

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Multi-agent systems can limit the control problem in complex production systems and solve them more efficiently. However, they often show local optimization tendencies. This paper presents a novel approach for a cooperative multi-agent system, which uses reinforcement learning and considers global key performance indicators. For this purpose, a central deep q-learning module transfers its knowledge to the cooperative order agents. The order agent's experience is stored in a replay memory for subsequent reinforcement learning. Interdependencies between the characteristics of nonlinear production systems and learning parameters are investigated and the performance is evaluated in comparison to conventional methods of production control. (C) 2020 CIRP. Published by Elsevier Ltd. All rights reserved.

引用

页码：389 / 392

页数：4

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