Abstract
Deep Reinforcement Learning (DRL) has proven effective in addressing the complexities of task offloading in heterogeneous and dynamic edge computing environments. However, the iterative training process of DRL, particularly in its early stages, is computationally intensive and time-consuming, posing significant challenges for delay-sensitive applications. Furthermore, DRL agents are typically initialized with random weights, as is common in deep learning, resulting in models that lack meaningful behavioral priors and further complicate the learning process. To tackle these issues, this paper leverages fuzzy logic to improve the early-stage performance of DRL agents in task offloading through behavior cloning. By exploiting the high-level reasoning capabilities of fuzzy logic, behavior cloning equips DRL agents with heuristic knowledge, thereby reducing exploration costs and accelerating training in the early learning stages. To facilitate the seamless integration of fuzzy logic–based knowledge into DRL agents, five distinct behavior cloning strategies are introduced: only cloning, norm cloning, fading cloning, runtime cloning, and hybrid cloning. Extensive simulations in multi-access edge computing environments with multiple edge servers and user devices demonstrate that integrating fuzzy logic with DDQN-based reinforcement learning via behavior cloning significantly improves the Average Task Completion Rate (ATCR) and reduces the Average Task Processing Time cost (ATPT) of all user devices over learning episodes during early training stages, while concurrently enhancing the stability of learning progress compared to DRL baseline approach.
| Original language | English |
|---|---|
| Pages (from-to) | (In-Press) |
| Journal | IEEE Transactions on Consumer Electronics |
| Volume | (In-Press) |
| Early online date | 27 Jun 2025 |
| DOIs | |
| Publication status | E-pub ahead of print - 27 Jun 2025 |
Bibliographical note
© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.
This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.
Keywords
- Behavior cloning
- Deep reinforcement learning
- Fuzzy logic
- Task offloading
- Edge computing