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.
Qiang-Qiang Jiang, Jia-Mei Jin, Xu Xin et al.
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