Employing machine learning into 6G vehicular networks to support vehicular application services is being widely studied and a hot topic for the latest research works in the literature. This article provides a comprehensive review of research works that integrated reinforcement and deep reinforcement learning algorithms for vehicular networks management with an emphasis on vehicular telecommunications issues. Vehicular networks have become an important research area due to their specific features and applications such as standardization, efficient traffic management, road safety, and infotainment. In such networks, network entities need to make decisions to maximize network performance under uncertainty. To achieve this goal, Reinforcement Learning (RL) can effectively solve decision-making problems. However, the state and action spaces are massive and complex in large-scale wireless networks. Hence, RL may not be able to find the best strategy in a reasonable time. Therefore, Deep Reinforcement Learning (DRL) has been developed to combine RL with Deep Learning (DL) to overcome this issue. In this survey, we first present vehicular networks and give a brief overview of RL and DRL concepts. Then we review RL and especially DRL approaches to address emerging issues in 6G vehicular networks. We finally discuss and highlight some unresolved challenges for further study.

在 6G 车载网络中使用机器学习来支持车载应用服务正在被广泛研究，并且是文献中最新研究工作的热门话题。本文对整合强化和深度强化学习算法用于车载网络管理的研究工作进行了全面回顾，重点是车载电信问题。车载网络因其标准化、高效交通管理、道路安全和信息娱乐等特定特性和应用而成为一个重要的研究领域。在这样的网络中，网络实体需要做出决策以在不确定性下最大化网络性能。为了实现这一目标，强化学习（RL）可以有效地解决决策问题。然而，在大规模无线网络中，状态和动作空间是巨大而复杂的。因此，RL 可能无法在合理的时间内找到最佳策略。因此，已经开发了深度强化学习 (DRL) 以将 RL 与深度学习 (DL) 相结合来克服这个问题。在本次调查中，我们首先介绍了车辆网络，并简要概述了 RL 和 DRL 概念。然后我们回顾了 RL，尤其是 DRL 方法来解决 6G 车载网络中的新问题。我们最后讨论并强调了一些未解决的挑战以供进一步研究。我们首先介绍车辆网络并简要概述 RL 和 DRL 概念。然后我们回顾了 RL，尤其是 DRL 方法来解决 6G 车载网络中的新问题。我们最后讨论并强调了一些未解决的挑战以供进一步研究。我们首先介绍车辆网络并简要概述 RL 和 DRL 概念。然后我们回顾了 RL，尤其是 DRL 方法来解决 6G 车载网络中的新问题。我们最后讨论并强调了一些未解决的挑战以供进一步研究。