Deep Reinforcement Learning Based Resource Management for Multi-Access Edge Computing in Vehicular Networks

Abstract

In this paper, we study joint allocation of the spectrum, computing, and storing resources in a multi-access edge computing (MEC)-based vehicular network. To support different vehicular applications, we consider two typical MEC architectures and formulate multi-dimensional resource optimization problems accordingly, which are usually with high computation complexity and overlong problem-solving time. Thus, we exploit reinforcement learning (RL) to transform the two formulated problems and solve them by leveraging the deep deterministic policy gradient (DDPG) and hierarchical learning architectures. Via off-line training, the network dynamics can be automatically learned and appropriate resource allocation decisions can be rapidly obtained to satisfy the quality-of-service (QoS) requirements of vehicular applications. From simulation results, the proposed resource management schemes can achieve high delay/QoS satisfaction ratios.