A Multi-Agent Deep Reinforcement Learning Approach for Computation Offloading in 5G Mobile Edge Computing

Abstract

Mobile edge computing (MEC) in 5G networks has recently emerged as a promising paradigm to enhance the data processing capabilities of mobile devices. Due to portability and cost considerations, mobile devices usually have limited battery and computing resources. Through MEC technology, computing tasks can be offloaded to remote servers, which helps to reduce computing latency and energy consumption. However, an im-proper computing offloading may produce additional overheads such as waiting time and wireless transmission latency, because of the limited resources of remote servers and extra wireless transmissions. In this paper, we propose a multi -agent deep reinforcement learning (MADRL) based decentralized cooperative offloading decision algorithm, which determines whether the computing tasks are executed locally or placed on an appropriate edge node to minimize system costs. Our goal is to learn an optimal online policy from experiences to solve a combinatorial optimization problem at a lower computational complexity, and the factors such as computation delay, energy consumption, communication latency, and waiting time in remote servers are all considered. Experiment results show that our approach is able to reduce by an average of 5.4% execution latency than traditional methods, and outperforms single-agent D RL algorithms.