Multi-Agent DRL for Resource Allocation in Vehicular Networks: A Comparative Study

In recent years, extensive research has been conducted on radio resource allocation (RRA) in vehicular networks. Many studies have employed multi-agent Deep Reinforcement Learning (DRL) as an effective approach for making decentralized RRA decisions in highly dynamic and uncertain vehicular environments. However, a systematic evaluation and comparison of various multi-agent DRL algorithms in vehicular contexts remain lacking. In this paper, we address this gap by framing RRA problems in Cellular Vehicle-to-Everything (C-V2X) networks as a series of multi-agent interference games with ascending complexity as more realistic factors are introduced. We benchmark performance of classical multi-agent DRL algorithms in these environments. Our results offer insights into the relative significance of different Multi-Agent Reinforcement Learning (MARL) challenges in C-V2X RRA tasks, along with a comparative evaluation of multiple algorithms.