Deep Reinforcement Learning for Hybrid Task Scheduling in Collaborative Vehicular Edge Computing

Collaborative Vehicular Edge Computing (CVEC) employs an edge server on the roadside unit and volunteer vehicles as processors to provide vehicle-to-infrastructure (V2I) offloading and vehicle-to-vehicle (V2V) offloading for requester vehicles in computation offloading. Since the processors have heterogeneous computing capabilities, we study a hybrid task scheduling problem to minimize the total service cost of all requester vehicles subject to feasible constraints. More specifically, the service cost of a requester vehicle is formulated as the product of the priority value and weighted sum of the delay and energy consumption of processing the task. We derive delay constraints of the V2V and V2I offloading according to the mobility of the vehicles. Furthermore, we present a deep reinforcement learning approach to solve the above problem in the dynamic vehicular environment. Particularly, we adopt the state-of-the-art Rainbow algorithm to accelerate the convergence and achieve better performance. Finally, we provide numerical results to demonstrate that our approach outperforms the baseline approaches in achieving the faster and more accurate learning.