Multi-Agent Reinforcement Learning for C-V2X RAT Selection

Abstract

Vehicles are increasingly equipped with advanced V2X communication capabilities. While early V2X apps utilized services such as Cooperative Awareness Messages, recent developments have allowed more advanced applications including cooperative driving, shared perception, and sensor-sharing services. The broader mix of applications leads to heterogeneous requirements for latency and reliability. At the same time multiple communication technologies for V2X are available with pros and cons. Hybrid V2X communication can exploit the distinct advantages at the right moment to fulfill the requirements of the applications. This work studies the decision problem between cellular Uu link, NR-V2X PC5 sidelink, and the simultaneous use of both channels. We address this problem by using the multi-agent reinforcement learning algorithm MAPPO and compare it to five baselines consisting of a deep reinforcement learning (DRL) approach, a static decision tree approach and static channel selection strategies. The methods are evaluated in an urban scenario and with a set of selected communication use cases. The evaluation results show that when compared to the DRL approach, the on-time delivery ratio improves from 0.508 to 0.535 in a single-controlled-vehicle setting and from 0.548 to 0.567 when all vehicles follow the learned policy and reduces the training time by half. The gains result mainly from the advanced applications scenarios, as opposed to scenarios involving exclusively CAM messaging. This indicates future applications will benefit from such adaptive communication strategies and that multi-agent modelling is useful for addressing the underlying decision problem.