Multi-step reinforcement learning-based offloading for vehicle edge computing
Shaodong Han, Yingqun Chen, Guihong Chen, Jiao Yin, Hua Wang, Jinli Cao
Abstract
The Internet of Vehicles (IoV) system has recently attracted more attention. However, IoV applications require massive computations within strict time limits. Computation energy consumption is also a significant concern in IoV applications. Thus, this paper establishes a vehicle edge computing architecture by combining edge computing and IoV to improve the computation ability of IoV. To optimize the offloading computation process, we model the entire process as a Markov decision process (MDP). Computation delay, computation energy consumption and communication quality are considered in a utility function to establish a multi-objective optimization problem. A deep reinforcement learning algorithm based on a multi-step deep Q network (MSDQN) is proposed to solve the MDP without considering the complicated transmission channels. Especially, the optimal multi-step value is found via experiments. Simulation results show that the proposed offloading algorithm can significantly reduce the IoV computation delay and computation energy consumption in processing computationally intensive tasks.