Task Offloading Based on the Fusion of Model- and Data-Driven Intelligence for Vehicular Edge Computing Networks

Vehicular edge computing (VEC) is an efficient solution to alleviate the limitations of local computing resources in vehicular networks. However, the high mobility of vehicles and the dynamic variability of network topologies make it significantly challengeable. In this work, we make a fusion of model-driven and data-driven intelligence to design a multi-agent deep reinforcement learning (DRL) solution for task offloading in urban VEC networks. First, computational models for task queue, transmission, computation, energy consumption, and expense are meticulously developed for the VEC network that integrates communication and computation. Vehicular tasks vary in type, urgency, size, and timeframe, leading to different latency requirements. Tasks may be executed locally within the vehicle, at a server after V2I offloading via cellular communications, or in a neighboring vehicle after V2V offloading via millimeter-wave (mmWave) communications. Each of these options incurs different levels of latency, energy consumption, and expense. Second, based on these models and the utility function that combines latency, energy consumption, and expense, an optimization problem for task offloading is formulated. This problem can be interpreted as a Markov decision process with a carefully designed reward function. Third, to address the offloading problem, we propose a multi-agent proximal policy optimization-based task and target selection algorithm (MAPPO-TTSA). This algorithm also utilizes convolutional neural networks to extract features from large-scale states, thereby enhancing their correlation. Fourth, comprehensive training is performed on the observational data to determine the optimal parameters for predicting task offloading. Finally, extensive experiments are conducted, and simulation results are provided to demonstrate that the proposed intelligent task offloading scheme offers significant advantages in terms of average task completion delay and utility level across various scenarios.