Distributed Resource Allocation and Offloading Strategy Based on Deep Reinforcement Learning in V2V

Abstract

Aiming at the communication range of vehicles leaving the edge server, a distributed computing offload scheme is proposed. This scheme divides the vehicle computing intensive tasks into multiple subtasks, makes full use of the computing resources of surrounding vehicles and considers the allocation of communication resources. The problem is modeled as minimizing the maximum processing delay of all subtasks, a resource allocation scheme based on DQN (RADQN) is proposed. The simulation results show that the proposed algorithm has certain advantages compared with the scheme without considering communication resource allocation, and it is still superior to other schemes when the service vehicle speed is fast.