Enhanced DRL Strategy for Distributed Edge Computing in Vehicular Networks

The rapid growth of connected vehicles has posed significant challenges in managing computational offloading in vehicular networks. However, recent advancements in artificial intelligence, big data, and cloud computing have opened up new possibilities for optimizing computational offloading in vehicular edge computing (VEC) systems. This article addresses the critical and pressing issue of leveraging these new technologies to optimize the allocation of vehicular computational offloading resources at edge servers, with the aim of improving the efficiency of computational offloading services while reducing costs. We focus on the computational offloading problem in a distributed edge computing system, where computational tasks from vehicles can be processed by multiple edge computing resource units (ECRUs). To tackle this problem, we first model the optimization of computational offloading resources as a Markov Decision Process (MDP), taking into account system income, cost expenditure, and other relevant domains. We then propose an Enhanced Deep Q-Learning (EDQL) approach, which leverages reinforcement learning (RL) to solve the optimization problem effectively. Our simulation results demonstrate that the proposed EDQL approach achieves fast convergence speed, high convergence stability, and superior performance compared to existing methods.