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.

翻译：智能交通系统（ITS）日益复杂，促使业界对计算任务向边缘服务器、车载节点及无人机等外部基础设施进行卸载产生了浓厚兴趣。这些动态异构环境对传统卸载策略构成挑战，从而推动了将强化学习（RL）和深度强化学习（DRL）作为自适应决策框架的研究探索。本文对基于DRL的车联网边缘计算（VEC）卸载最新进展进行了全面综述。我们依据学习范式（如单智能体、多智能体）、系统架构（如集中式、分布式、分层式）及优化目标（如时延、能耗、公平性）对现有工作进行分类与比较。此外，我们分析了马尔可夫决策过程（MDP）公式的应用方式，并重点阐述了奖励设计、协调机制与可扩展性领域的新兴趋势。最后，我们识别出开放挑战，并勾勒出未来研究方向，以期为下一代ITS中稳健且智能的卸载策略开发提供指引。