Task offloading algorithm of vehicle edge computing environment based on Dueling-DQN

With the wide application of the Internet of vehicles, the rapid development of intelligent vehicles provides drivers and passengers with a good driving and riding experience. However, how to process a large amount of data messages in real time on resource-limited vehicle terminals is still a huge challenge, and it will cause great energy consumption to terminal devices. In this paper, a semi-online task distribution and offloading algorithm based on Dueling-DQN is proposed for time-varying complex vehicle environments. Firstly, the vehicle offloading system is built by the reinforcement learning, because the original optimization problem is a joint optimization problem with high complexity, and divided into the vehicle scheduling sub-problem and the vehicle computation resources optimization subproblem. We predicts different vehicle offloading behaviour, and calculates total rewards value after a series of vehicle offloading action, so as to update the vehicle offloading decisions. The simulation results show that this algorithm can improve the efficiency and energy consumption of computation tasks to a certain extent.