用深度强化学习优化车载边缘计算的智能任务卸载
Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures
- 基于深度强化学习构建自适应卸载决策框架
- 对比了单/多智能体、集中/分布式等不同架构性能
- 适合研究车联网智能调度与边缘计算的学者
智能交通系统（ITS）复杂性的增加，促使将计算任务卸载到边缘服务器、车载节点和无人机等外部基础设施。这些动态异构环境对传统卸载策略构成挑战，推动了强化学习（RL）与深度强化学习（DRL）在自适应决策中的应用。本文综述了面向车载边缘计算（VEC）的最新DRL卸载研究进展。按学习范式（如单智能体、多智能体）、系统架构（如集中式、分布式、分层式）和优化目标（如延迟、能耗、公平性）对现有工作进行分类与比较。分析了马尔可夫决策过程（MDP）的应用，指出奖励设计、协同机制与可扩展性方面的新兴趋势。最后，识别出开放挑战并提出未来研究方向，以指导下一代ITS中鲁棒智能卸载策略的发展。
原文摘要 · Abstract (English)
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.
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