The last two decades have seen a clear trend toward crafting intelligent vehicles based on the significant advances in communication and computing paradigms, which provide a safer, stress-free, and more enjoyable driving experience. Moreover, emerging applications and services necessitate massive volumes of data, real-time data processing, and ultrareliable and low-latency communication (URLLC). However, the computing capability of current intelligent vehicles is minimal, making it challenging to meet the delay-sensitive and computation-intensive demand of such applications. In this situation, vehicular task/computation offloading toward the edge cloud (EC) and vehicular cloudlet (VC) seems to be a promising solution to improve the network’s performance and applications’ Quality of Service (QoS). At the same time, artificial intelligence (AI) has dramatically changed people’s lives. Especially for vehicular task offloading applications, AI achieves state-of-the-art performance in various vehicular environments. Motivated by the outstanding performance of integrating reinforcement learning (RL)/deep RL (DRL) to the vehicular task offloading systems, we present a survey on various RL/DRL techniques applied to vehicular task offloading. Precisely, we classify the vehicular task offloading works into two main categories: 1) RL/ DRL solutions leveraging EC and 2) RL/DRL solutions using VC computing. Moreover, the EC section-based RL/DRL solutions are further subcategorized into multiaccess edge computing (MEC) server, nearby vehicles, and hybrid MEC (HMEC). To the best of our knowledge, we are the first to cover RL/DRL-based vehicular task offloading. Also, we provide lessons learned and open research challenges in this field and discuss the possible trend for future research.

中文翻译：

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RL/DRL 使用边缘和车辆 Cloudlet 满足车辆任务卸载：一项调查


在过去的二十年里，基于通信和计算范式的显着进步来制造智能汽车已成为一种明显的趋势，它提供了更安全、无压力和更愉快的驾驶体验。此外，新兴的应用程序和服务需要大量数据、实时数据处理以及超可靠和低延迟通信（URLLC）。然而，当前智能汽车的计算能力很小，很难满足此类应用对延迟敏感和计算密集型的需求。在这种情况下，向边缘云（EC）和车载小云（VC）卸载车辆任务/计算似乎是提高网络性能和应用程序服务质量（QoS）的有前途的解决方案。与此同时，人工智能（AI）极大地改变了人们的生活。特别是对于车辆任务卸载应用，人工智能在各种车辆环境中实现了最先进的性能。受将强化学习 (RL)/深度 RL (DRL) 集成到车辆任务卸载系统的出色性能的激励，我们对应用于车辆任务卸载的各种 RL/DRL 技术进行了调查。准确地说，我们将车辆任务卸载工作分为两大类：1）利用 EC 的 RL/DRL 解决方案和 2）使用 VC 计算的 RL/DRL 解决方案。此外，基于EC部分的RL/DRL解决方案进一步细分为多访问边缘计算（MEC）服务器、附近车辆和混合MEC（HMEC）。据我们所知，我们是第一个涵盖基于 RL/DRL 的车辆任务卸载的人。 此外，我们还提供了该领域的经验教训和公开的研究挑战，并讨论了未来研究的可能趋势。