Reinforcement Learning Enabled Dynamic Resource Allocation in the Internet of Vehicles

Abstract

As an important application scenario of the industrial Internet of things, the Internet of Vehicles can significantly improve road safety, improve traffic management efficiency, and improve people's travel experience. Due to the high dynamics of the Internet of vehicles environment, the traditional resource optimization technologies cannot meet the requirements of the Internet of vehicles for dynamic communication, computing and storage resources optimization management, and artificial intelligence algorithms can adaptively obtain dynamic resource allocation schemes through self-learning. Therefore, adopting artificial intelligence techniques to optimize the dynamic resource of the Internet of Vehicles is the research focus of this article. In this article, we first model the Internet of Vehicles resource allocation problem as a semi-Markov decision process that introduces a resource reservation strategy and a secondary resource allocation mechanism. Then, the reinforcement learning algorithm is used to solve the model. Thereafter, it theoretically analyzes the joint optimization of computing and communication resources, models it as a hierarchical architecture, and uses hierarchical reinforcement learning to obtain the optimal system resource allocation plan. Finally, the results of simulation experiments show that the dynamic resource allocation scheme of the Internet of vehicles based on the reinforcement learning in this article greatly improve resource utilization and user quality of experience with guaranteeing system quality of service compared with the traditional greedy algorithm.