###### Abstract
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Feature** & **Description** \\ \hline Dynamic mobility scenarios & In this network, the moving nodes have dynamic mobility scenarios, which are different from low velocity (below 50 km/h) to high velocity (above 500 km/h). Therefore, this reduces the lifetime of the discovered paths between the nodes. \\ \hline Dynamic topology & In VANET, network topology changes rapidly, the lifetime of the connection links is short, and the network density varies depending on the location and time. \\ \hline Frequent failure of links & Inadequate climate conditions, along with the high velocity of the moving nodes and the repeated topology changes, cause the failure of links between the nodes. \\ \hline Energy and computing resources & In this network, nodes do not have energy restrictions because every vehicle is equipped with a battery with long-lifetime. However, this is a challenge in real-time environments. \\ \hline Predictability & The movement of vehicles is restricted by urban maps. Therefore, their movement is predictable. \\ \hline Different quality of services (QoS) requirements & In VANET, different data services, like multimedia entertainment and video games have different QoS needs such as reliability, delay, and data rates. \\ \hline Large-scale networks & A large number of vehicles participate in VANET. Therefore, they create a large-scale network, particularly in dense urban areas, city centers, entrances to large cities, and highways. \\ \hline Various network densities & Changing traffic flow causes changes in the network density, meaning that in some areas, such as rural areas, the network density is very low or in some areas such as city centers, gridlock, or heavy traffic, the network density is very high. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Important features of vehicular ad hoc networks. and produce a function to map the data to the labels. This model is used for predicting unlabeled data. In unsupervised learning, there is no output related to the inputs, meaning that the data is unlabeled. Unsupervised learning must find the existing patterns and relationships between data samples. In reinforcement learning (RL), the agent and the dynamic environment work in relation to each other. These interactions determines the ideal behavior of the agent with regard to the reward-penalty produced by environment [10]. In VANET, ML techniques, especially RL, try to make vehicles take self-decisions for networking operations such as routing [11; 12]. The agent must obtain knowledge in relation to the environment dynamics based on the collected data to find the most suitable action and achieve a certain purpose, like discovering routes with minimum delay. RL can be used to optimize various issues in VANET such as predicting traffic conditions, estimating network traffic, controlling network congestion, discovering routes, enhancing network security, and resource allocation. Reinforcement learning algorithms are attractive solutions for modifying routing methods in VANET. However, researchers need more research in this area because machine learning, especially reinforcement learning is a significant research subject in VANETs. Note that most review papers related to machine learning and VANET do not focus on RL applications in designing routing protocols. For example, in [13], authors have reviewed various routing methods based on RL in VANETs. Ref. [14] studied various applications of RL and DRL in vehicular network management, but did not consider their applications for improving routing approaches. In [15], authors investigated the importance of artificial intelligence techniques in different areas of VANET, especially routing. However, they do not well explain how they use reinforcement learning techniques for improving vehicular communication. In [16], authors have studied different RL and DRL applications in various Internet of things (IoT) systems.