Deep Reinforcement Learning-Based Task Partitioning Ratio Decision Mechanism in High-Speed Rail Environments with Mobile Edge Computing Server

Deep Reinforcement Learning-Based Task Partitioning Ratio Decision Mechanism in High-Speed Rail Environments with Mobile Edge Computing Server

High-speed rail (HSR) environments present unique challenges due to their high mobility and dense passenger traffic, resulting in dynamic and unpredictable task generation patterns. Mobile Edge Computing (MEC) has emerged as a transformative paradigm to address these challenges by deploying computat...

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Bibliographic Details
Main Authors: Seolwon Koo, Yujin Lim
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Applied Sciences
Subjects:
task partitioning
Deep Reinforcement Learning (DRL)
High-Speed Railway (HSR)
Mobile Edge Computing (MEC)
Twin Delayed Deep Deterministic policy gradient (TD3)
Online Access:https://www.mdpi.com/2076-3417/15/2/916
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