Vehicle as a Service: Fuzzy Reward-Based Multi-Agent Deep Reinforcement Learning for Task Scheduling in Vehicular Edge Computing
TL;DR
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.
Abstract
Under the vehicle as a service (VaaS) paradigm, intelligent connected vehicles continuously generate large-scale, computation-intensive perception data processing tasks. However, limited onboard computing resources and power supply prevent vehicles from handling these tasks efficiently. Vehicular edge computing (VEC) extends available computing resources through vehicle–infrastructure collaboration. Nevertheless, continuous vehicle mobility causes intermittent communication links between vehicles and roadside units, posing new challenges for VEC task scheduling. Therefore, this paper proposes a reinforcement learning-based VEC task scheduling approach that integrates a fuzzy reward mechanism with multi-agent proximal policy optimization (FRMPPO). First, a system architecture is developed by integrating the directed acyclic graph task model, dynamic communication model, and computation model. The scheduling problem is formulated as a partially observable Markov decision process, with the objective of minimizing task completion latency and vehicle energy consumption. Second, a fuzzy reward mechanism is designed to guide the training of multi-agent proximal policy optimization. It takes edge node load pressure and communication state as inputs to adaptively combine local immediate rewards and the global reward, eventually guiding the agents toward a globally optimized cooperative policy. Finally, real-time scheduling decisions under communication intermittency are enabled through a centralized training and decentralized execution framework and gated recurrent unit-based state encoding. Simulation results demonstrate that FRMPPO effectively solves the VEC task scheduling problem, achieving significantly superior performance over existing algorithms in terms of both task completion latency and vehicle energy consumption. The proposed method thereby satisfies the real-time processing demands of perception tasks in VaaS scenarios.
