ETRI-Knowledge Sharing Plaform

ENGLISH

성과물

논문 검색
구분 SCI
연도 ~ 키워드

상세정보

학술대회 Industrial General Reinforcement Learning Control Framework System based on Intelligent Edge
Cited 1 time in scopus Download 4 time Share share facebook twitter linkedin kakaostory
저자
김귀훈, 홍용근
발행일
202002
출처
International Conference on Advanced Communications Technology (ICACT) 2020, pp.414-418
DOI
https://dx.doi.org/10.23919/ICACT48636.2020.9061542
초록
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.
키워드
deep learning, intelligent edge, reinforcement learning, supervised learning
KSP 제안 키워드
Edge distribution, Edge-based, Field solution, Large-scale learning, Learning control, Learning framework, Reinforcement Learning(RL), Supervised Learning, control framework, deep learning(DL)