ETRI-Knowledge Sharing Plaform

KOREAN
논문 검색
Type SCI
Year ~ Keyword

Detail

Conference Paper Industrial General Reinforcement Learning Control Framework System based on Intelligent Edge
Cited 4 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Kwihoon Kim, Yong-Geun Hong
Issue Date
2020-02
Citation
International Conference on Advanced Communications Technology (ICACT) 2020, pp.414-418
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.23919/ICACT48636.2020.9061542
Abstract
This paper is about the intelligent edge-based reinforcement learning control framework technology for manufacturing field solution and features large-scale learning, scalable edge distribution technology that can be applied to various task. In this paper, two items are proposed as features. The first proposes the General Reinforcement Learning Framework(GRLF) in manufacturing, and the second proposes the edge solution technology based on the GRLF in manufacturing. We apply the industrial solution such as grid sorter system for example. As a result of the industrial GRLF based grid sorter system, it was confirmed that when a total of 100 deliveries are randomly received into the grid sorter system by any emitter, all shipments are 100% accurate. It also classifies approximately 0.5 deliveries per step. This shows the efficiency of classifying around 30 deliveries per minute, assuming a step is performed per second.
KSP Keywords
Edge distribution, Edge-based, Field solution, Large-Scale Learning, Learning framework, Reinforcement learning(RL), control framework, learning control