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Paper Abstract and Keywords
Presentation 2024-11-07 10:00
Enhancing Deep Reinforcement Learning for MEC Task Offloading with Fuzzy Logic Based Pre-training
Jiujie Zhang, Zhaoyang Du, Yangfei Lin, Celimuge Wu (UEC) CQ2024-58
Abstract (in Japanese) (See Japanese page) 
(in English) The joint optimization of task offloading in Mobile Edge Computing (MEC) presents a complex challenge. Deep Reinforcement Learning (DRL) algorithms have been proposed as a solution. However, these algorithms often suffer from arbitrary or random parameter initialization, lacking meaningful semantics and behavioral relevance. Consequently, DRL algorithms require extensive explorations to identify optimal policies, particularly during the initial learning stages. To mitigate this issue, we propose a pre-training framework for DRL algorithms that leverages the strengths of fuzzy logic. Our approach begins with the design and integration of a fuzzy logic-based task offloading module within the framework. This module helps the framework accumulate valuable experience data related to task offloading. Subsequently, this experience data is employed to pre-train the DRL algorithm offline, thereby facilitating the rapid acquisition of more meaningful initial parameters. Simulation experiments, conducted in an MEC task offloading scenario involving multiple mobile devices and edge servers, show that the proposed framework not only boosts the early-stage performance of the baseline DRL algorithm but also enhances long-term stability, as evidenced by reduced average task processing time and increased completion rate. Furthermore, our fuzzy logic offloading module demonstrates performance capabilities comparable to the baseline DRL algorithm.
Keyword (in Japanese) (See Japanese page) 
(in English) Fuzzy Logic / Pre-training / Deep Reinforcement Learning / Task Offloading / Mobile Edge Computing / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 237, CQ2024-58, pp. 6-11, Nov. 2024.
Paper # CQ2024-58 
Date of Issue 2024-10-31 (CQ) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee OFT OCS IEE-CMN ITE-BCT CQ  
Conference Date 2024-11-07 - 2024-11-08 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To CQ 
Conference Code 2024-11-OFT-OCS-CMN-BCT-CQ 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Enhancing Deep Reinforcement Learning for MEC Task Offloading with Fuzzy Logic Based Pre-training 
Sub Title (in English)  
Keyword(1) Fuzzy Logic  
Keyword(2) Pre-training  
Keyword(3) Deep Reinforcement Learning  
Keyword(4) Task Offloading  
Keyword(5) Mobile Edge Computing  
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1st Author's Name Jiujie Zhang  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Zhaoyang Du  
2nd Author's Affiliation The University of Electro-Communications (UEC)
3rd Author's Name Yangfei Lin  
3rd Author's Affiliation The University of Electro-Communications (UEC)
4th Author's Name Celimuge Wu  
4th Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-2 
Date Time 2024-11-07 10:00:00 
Presentation Time 25 minutes 
Registration for CQ 
Paper # CQ2024-58 
Volume (vol) vol.124 
Number (no) no.237 
Page pp.6-11 
#Pages 6 
Date of Issue 2024-10-31 (CQ) 

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