Task Offloading in Vehicular Edge Computing using Deep Reinforcement Learning: A Survey

The increasing demand for Intelligent Transportation Systems (ITS) has introduced significant challenges in managing the complex, computation-intensive tasks generated by modern vehicles while offloading tasks to external computing infrastructures such as edge computing (EC), nearby vehicular , and UAVs has become influential solution to these challenges. However, traditional computational offloading strategies often struggle to adapt to the dynamic and heterogeneous nature of vehicular environments. In this study, we explored the potential of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) frameworks to optimize computational offloading through adaptive, real-time decision-making, and we have thoroughly investigated the Markov Decision Process (MDP) approaches on the existing literature. The paper focuses on key aspects such as standardized learning models, optimized reward structures, and collaborative multi-agent systems, aiming to advance the understanding and application of DRL in vehicular networks. Our findings offer insights into enhancing the efficiency, scalability, and robustness of ITS, setting the stage for future innovations in this rapidly evolving field.