A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization

TL;DR

This paper introduces a new deep reinforcement learning approach using Twin Delayed DDPG for decentralized computation offloading in mobile edge computing, improving autonomy and performance over traditional methods.

Contribution

The paper proposes a novel DRL method based on Twin Delayed DDPG for decentralized MEC offloading, addressing weaknesses of existing algorithms and enabling portable user scenarios.

Findings

The proposed method allows users to autonomously learn effective offloading policies.

It outperforms conventional DDPG-based power control strategies.

The approach is effective even with finite feedback in decentralized MEC systems.

Abstract

Mobile edge computing (MEC) allows appliances to offload workloads to neighboring MEC servers that have the potential for computation-intensive tasks with limited computational capabilities. This paper studied how deep reinforcement learning (DRL) algorithms are used in an MEC system to find feasible decentralized dynamic computation offloading strategies, which leads to the construction of an extensible MEC system that operates effectively with finite feedback. Even though the Deep Deterministic Policy Gradient (DDPG) algorithm, subject to their knowledge of the MEC system, can be used to allocate powers of both computation offloading and local execution, to learn a computation offloading policy for each user independently, we realized that this solution still has some inherent weaknesses. Hence, we introduced a new approach for this problem based on the Twin Delayed DDPG algorithm, which enables us to overcome this proneness and investigate cases where mobile users are portable. Numerical results showed that individual users can autonomously learn adequate policies through the proposed approach. Besides, the performance of the suggested solution exceeded the conventional DDPG-based power control strategy.