DRL-Based Resource Allocation Game With Influence of Review Information for Vehicular Edge Computing Systems
Han Zhang, Hongbin Liang, Xintao Hong, Yiting Yao, Bin Lin, Dongmei Zhao
Abstract
Vehicle Edge Computing (VEC) represents a new technological paradigm. It delivers computational resources via edge nodes situated close to users. This approach not only satisfies the growing computational needs of vehicles but also minimizes communication latency. Such advancements are crucial for the evolution of intelligent transportation systems. To ensure these systems succeed, two key strategies are essential. First, edge servers must be effectively incentivized to engage in computation offloading. Second, vehicles require efficient resource request strategies, particularly when edge resources are limited. In this paper, we consider a duopolistic edge service market for vehicles with the existence of two service stage. For edge servers, they announce their resource pricing strategies before the start of each stage after a game has been played. After the conclusion of the first stage, vehicles generate reviews based on their service experience for both servers. These reviews will affect the vehicle's choice of edge servers in the next stage. Therefore, edge servers must devise effective pricing strategies to optimize their profits over both stages. Vehicles, after making their choice at any stage based on personal preferences, service quality, and resource pricing, must also engage in a game with other vehicles choosing the same server to determine their resource request strategy. In cases where vehicles prefer not to disclose their resource requests and other information, we propose a deep reinforcement learning framework to maximize the utility of each vehicle. Simulation results validate the effectiveness of our resource allocation scheme based on game theory and deep reinforcement learning.