A Reinforcement Learning Assisted Relative Distance based MAC in Vehicular Networks

Abstract

Many efforts have been done to increase the performance of vehicle-to-vehicle (V2V) services, such as basic safety message (BSM) and collision avoidance warning. However, high dynamics, such as topology and channel condition, still pose big challenges to resource allocation tasks in vehicular networks. A previous work, relative distance based MAC [1], is proposed to address merging collision. The dynamics can not be fully addressed because thresholds are used. Therefore, we intuitively adapt a dueling deep Q-network [2] to tune the threshold based on the aforementioned work to further address merging collision. The simulation results demonstrate the improvement of the proposed algorithm.