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학술지 Sortation Control Using Multi-Agent Deep Reinforcement Learning in N-Grid Sortation System
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저자
김주봉, 최호빈, 황규영, 김귀훈, 홍용근, 한연희
발행일
202006
출처
Sensors, v.20 no.12, pp.1-20
ISSN
1424-8220
출판사
MDPI
DOI
https://dx.doi.org/10.3390/s20123401
초록
Intralogistics is a technology that optimizes, integrates, automates, and manages the logistics flow of goods within a logistics transportation and sortation center. As the demand for parcel transportation increases, many sortation systems have been developed. In general, the goal of sortation systems is to route (or sort) parcels correctly and quickly. We design an n-grid sortation system that can be flexibly deployed and used at intralogistics warehouse and develop a collaborative multi-agent reinforcement learning (RL) algorithm to control the behavior of emitters or sorters in the system. We present two types of RL agents, emission agents and routing agents, and they are trained to achieve the given sortation goals together. For the verification of the proposed system and algorithm, we implement them in a full-fledged cyber-physical system simulator and describe the RL agents?? learning performance. From the learning results, we present that the well-trained collaborative RL agents can optimize their performance effectively. In particular, the routing agents finally learn to route the parcels through their optimal paths, while the emission agents finally learn to balance the inflow and outflow of parcels.
KSP 제안 키워드
Deep reinforcement learning, Inflow and outflow, Learning performance, Optimal Path, Reinforcement Learning(RL), cyber physical system(CPS), logistics transportation, multi-agent reinforcement learning
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