Qiang-Qiang Jiang

Vehicle as a Service: Fuzzy Reward-Based Multi-Agent Deep Reinforcement Learning for Task Scheduling in Vehicular Edge Computing

A reinforcement learning-based VEC task scheduling approach that integrates a fuzzy reward mechanism with multi-agent proximal policy optimization (FRMPPO) that satisfies the real-time processing demands of perception tasks in VaaS scenarios is proposed.