Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures

TL;DR

This paper reviews recent deep reinforcement learning methods for offloading in vehicular edge computing, focusing on architectures, learning paradigms, and optimization goals to enhance intelligent transportation systems.

Contribution

It provides a comprehensive classification and comparison of DRL-based offloading approaches, highlighting emerging trends, challenges, and future research directions in vehicular edge computing.

Findings

Classified existing DRL approaches by paradigms and architectures

Compared optimization objectives like latency and energy efficiency

Identified open challenges and future research directions

Abstract

The increasing complexity of Intelligent Transportation Systems (ITS) has led to significant interest in computational offloading to external infrastructures such as edge servers, vehicular nodes, and UAVs. These dynamic and heterogeneous environments pose challenges for traditional offloading strategies, prompting the exploration of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) as adaptive decision-making frameworks. This survey presents a comprehensive review of recent advances in DRL-based offloading for vehicular edge computing (VEC). We classify and compare existing works based on learning paradigms (e.g., single-agent, multi-agent), system architectures (e.g., centralized, distributed, hierarchical), and optimization objectives (e.g., latency, energy, fairness). Furthermore, we analyze how Markov Decision Process (MDP) formulations are applied and highlight emerging trends in reward design, coordination mechanisms, and scalability. Finally, we identify open challenges and outline future research directions to guide the development of robust and intelligent offloading strategies for next-generation ITS.