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GRU-OptiCom: Revolutionizing Computation Offloading in Edge Computing Through Meta-Reinforcement Learning with GRU

Aditya Oza,Yash Vardhan Gautam,Anirudh Bhakar,M. K,Kanika Malhotra

Published 2025 in IEEE Region 10 Conference

ABSTRACT

Modern mobile devices often struggle with limited computational capabilities, hindering their ability to efficiently process data-intensive applications such as augmented reality, mobile healthcare, and intelligent navigation. Multi-access Edge Computing (MEC) presents a viable solution by enabling the offloading of complex computational tasks to geographically proximate edge servers. This offloading approach alleviates the processing burden on user devices and significantly reduces end-to-end latency, thereby enabling real-time responsiveness. In this work, we propose GRU-OptiCom, a novel task offloading framework that leverages meta-reinforcement learning (MRL) to dynamically optimize offloading decisions across varying environments. The model incorporates a GRU-based sequence-to-sequence neural architecture for capturing task dependencies, and employs Proximal Policy Optimization (PPO) to ensure stable and efficient training. We evaluate GRU-OptiCom using latency as the primary performance metric and compare its performance with baseline methods such as MRLCO and HEFTbased greedy algorithms. Experimental results demonstrate that GRU-OptiCom consistently achieves lower latency and improved task distribution, setting a new benchmark for adaptive and intelligent task offloading in MEC environments.

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