Distributed Task Offloading for IoAV Using DDP-DQN

The swift progress of the Internet of Vehicles (IoV) and autonomous driving technology has facilitated the emergence of the Internet of Autonomous Vehicles (IoAV). If delay-sensitive vehicle tasks are not completed on time, it will lead to bad consequences for IoAV. Task offloading technology can solve the problem that the vehicle cannot meet the task requirements. However, highly dynamic vehicle networks and diverse vehicle applications require more intelligent task offloading strategies. Therefore, this paper addresses the distributed task offloading problem in the IoAV to meet diverse vehicle task demands. First, we model the vehicle task offloading problem as a decision problem, and a deep reinforcement learning (DRL) algorithm named DDP-DQN (double-dueling-prioritize-DQN) is applied to complete vehicle tasks more efficiently. Then, we design a reward function to complete the task within the acceptable maximum delay of the task while reducing the consumption of resources. Simulations demonstrate the outperforming of the DDP-DQN compared with other three reinforcement learning algorithms.