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Journal Article
Sortation Control Using Multi-Agent Deep Reinforcement Learning in N-Grid Sortation System
- Authors
- Ju-Bong Kim, Ho-Bin Choi, Gyu-Young Hwang, Kwihoon Kim, Yong-Geun Hong, Youn-Hee Han
- Issue Date
- 2020-06
- Citation
- Sensors, v.20, no.12, pp.1-20
- ISSN
- 1424-8220
- Publisher
- MDPI
- Language
- English
- Type
- Journal Article
- Abstract
- 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 Keywords
- Deep reinforcement learning, Inflow and outflow, Optimal path, Reinforcement learning(RL), cyber physical system(CPS), learning performance, logistics transportation, multi-agent reinforcement learning
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
