Multiagent Deep Reinforcement Learning for Vehicular Computation Offloading in IoT

The development of the Internet of Things (IoT) and intelligent vehicles brings a comfortable environment for users. Various emerging vehicular applications using artificial intelligence (AI) technologies are expected to enrich users' daily life. However, how to execute computation-intensive applications on resource-constrained vehicles based on AI still faces great challenges. In this article, we consider the vehicular computation offloading problem in mobile-edge computing (MEC), in which multiple mobile vehicles select nearby MEC servers to offload their computing tasks. We propose a multiagent deep reinforcement learning (DRL)-based computation offloading scheme, in which the uncertainty of a multivehicle environment is considered so that the vehicles can make offloading decisions to achieve an optimal long-term reward. First, we formalize a formula for the computation offloading problem. The goal of this article is to determine the optimal offloading decision to the MEC server under each observed system state, so as to minimize the total task processing delay in a long-term period. Then, we use a multiagent DRL algorithm to learn an effective solution to the vehicular task offloading problem. To evaluate the performance of the proposed offloading scheme, a large number of simulations are carried out. The simulation results verify the effectiveness and superiority of the proposed scheme.

Automated summary

This article explores the vehicular computation offloading problem in mobile-edge computing and proposes a multiagent deep reinforcement learning-based offloading scheme. The effectiveness and superiority of the proposed scheme are verified through a large number of simulations.