Sortation Control Using Multi-Agent Deep Reinforcement Learning in N-Grid Sortation System
1
Department of Computer Science and Engineering, Korea University of Technology and Education, Cheonan 31253, Korea
2
Department of Knowledge-Converged Super Brain Convergence Research, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(12), 3401; https://doi.org/10.3390/s20123401
Received: 1 May 2020
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Revised: 3 June 2020
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Accepted: 11 June 2020
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Published: 16 June 2020
(This article belongs to the Special Issue New Technologies and Applications for Smart Interactive Cyber-Physical Systems)
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.
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Keywords:
sortation system; n-grid sortation system; reinforcement learning; multi-agent reinforcement learning
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MDPI and ACS Style
Kim, J.-B.; Choi, H.-B.; Hwang, G.-Y.; Kim, K.; Hong, Y.-G.; Han, Y.-H. Sortation Control Using Multi-Agent Deep Reinforcement Learning in N-Grid Sortation System. Sensors 2020, 20, 3401. https://doi.org/10.3390/s20123401
AMA Style
Kim J-B, Choi H-B, Hwang G-Y, Kim K, Hong Y-G, Han Y-H. Sortation Control Using Multi-Agent Deep Reinforcement Learning in N-Grid Sortation System. Sensors. 2020; 20(12):3401. https://doi.org/10.3390/s20123401
Chicago/Turabian StyleKim, Ju-Bong, Ho-Bin Choi, Gyu-Young Hwang, Kwihoon Kim, Yong-Geun Hong, and Youn-Hee Han. 2020. "Sortation Control Using Multi-Agent Deep Reinforcement Learning in N-Grid Sortation System" Sensors 20, no. 12: 3401. https://doi.org/10.3390/s20123401
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