Abstract: The Internet of vehicles (IoV) generates a massive amount of network connections and diversified data. To address the challenge that a single agent struggles to collect channel state information and perform service-differentiated resource allocation and link scheduling in dynamic scenarios, a multi-agent deep reinforcement learning-based service-differentiated resource allocation method for IoV is proposed. This method aims to maximize the successful delivery rate of V2V link data packets and the total capacity of V2I links, under the constraint of minimizing interference to emergency service links. It employs deep reinforcement learning algorithms to optimize spectrum allocation and power selection strategies in a single-antenna vehicle-mounted network where multiple cellular users and device-to-device users coexist. Each agent is trained using deep Q-network(DQN), and they interact with the communication environment collectively, achieving coordination through a global reward function. Simulation results show that, in high-load scenarios, compared to traditional random allocation schemes, this scheme increases the total throughput of V2I links by 3.76 Mbps, improves the packet delivery rate of V2V links by 17.1%, and reduces the interference to emergency service links by 1.42 dB compared to ordinary links. This achieves priority guarantee for emergency service links and effectively enhances the overall transmission capacity of V2I and V2V links.

Key words: internet of vehicles, spectrum allocation, reinforcement learning, multi-agent, emergency services