Artificial Intelligence-Generated Content (AIGC) refers to the paradigm of
automated content generation utilizing AI models. Mobile AIGC services in the
Internet of Vehicles (IoV) network have numerous advantages over traditional
cloud-based AIGC services, including enhanced network efficiency, better
reconfigurability, and stronger data security and privacy. Nonetheless, AIGC
service provisioning frequently demands significant resources. Consequently,
resource-constrained roadside units (RSUs) face challenges in maintaining a
heterogeneous pool of AIGC services and addressing all user service requests
without degrading overall performance. Therefore, in this paper, we propose a
decentralized incentive mechanism for mobile AIGC service allocation, employing
multi-agent deep reinforcement learning to find the balance between the supply
of AIGC services on RSUs and user demand for services within the IoV context,
optimizing user experience and minimizing transmission latency. Experimental
results demonstrate that our approach achieves superior performance compared to
other baseline models.

在本文中，我们提出了一种分散化的激励机制，利用多智能体深度强化学习在车联网环境中平衡 RSUs 上的 AIGC 服务供应和用户对服务的需求，以优化用户体验和减小传输时延。实验结果表明，我们的方法在性能上优于其他基准模型。