# Investigating Routing in the VANET Network: Review and Classification of Approaches
In the first layer, according to the criterion of increasing network performance, our goals include increasing the quality of service, delay reduction and packet delivery ratio. In the second layer, a route is created at the origin and destination of the network, in which the cost and interaction of the routes between intersections are determined using RSUs. Through this algorithm, the best routes are selected based on reducing delay and increasing quality of service. In this layer, the speed of the most optimal routes is shown to the vehicles. In the third layer, the communication between servers, RSUs, traffic lights and vehicles in the environment is defined. Finally, an overview of routing in VANET network is drawn in  
Significance of the Study  
Cloud computing environments have limitations in extensive data processing systems. For example, nodes in a cloud environment encounter many clusters for calculations. Efficient task scheduling and resource allocation plans are required for fast data processing. These plans should distribute tasks on nodes in such a way that resource usage is maximized. For this purpose, we need task scheduling for data management. This research investigates scheduling and task scheduling with the goal of assigning tasks to appropriate resources and checking QoS in the network. A review paper is necessary for future research in various fields such as VANET, 5G, and 6G to choose the most appropriate methods and route. Goals (Objectives)  
This research aims to collect and classify related work in the fields of RL, FUZZY, and DRL in the VANET network to achieve the most optimal routing in the network. We classify the related work into four categories: reinforcement learning, fuzzy logic, overview, and deep reinforcement learning. The papers investigate criteria such as packet delivery ratio and delay, and one of the main goals is to study optimal routing and network overhead control. The areas of use of different protocols such as the type of routing, discovery of optimal routes, and quality of service (QoS) are evaluated. The study also investigates how to design and model different algorithms for routing in the network, and network efficiency for routing, network overhead control, and convergence speed. The Overall Structure of the Paper  
Section 2 presents related work, and reinforcement learning, fuzzy logic, and deep reinforcement learning methods are classified. Section 3 describes the proposals, and Section 4 is devoted to discussion and conclusion. Figure 2 for a better understanding of this issue. Figure 2: Routing in VANET network. **Machine Learning in VANET Networks**  
Machine learning includes reinforcement learning and algorithms in which a learning agent tries to maximize rewards in its environment to achieve a specific goal. By interacting with the environment, the learner selects appropriate actions to apply to the environment. In general, the RL agent's objective is to maximize warehouses' reward. An overview of machine learning in VANET networks is drawn in Figure 3 for better understanding. In general, there are three approaches to reinforcement learning:  
**Value-oriented approach**  
In the value-oriented approach, the aim is to optimize the value function \(\mathbf{V_{\pi}}(\mathbf{s})\). The value function is a function that determines the maximum future reward that the agent receives in each state. The value of each state is equal to the total value of the reward that the agent can expect to gain in the future starting from that state. \[\mathbf{V_{\pi}}(\mathbf{s})=\mathbb{E}_{\pi}\Big{[}\mathbf{R_{t+1}}+\mathbf{\gamma}\mathbf{R_{t+2}} +\mathbf{\gamma}^{2}\mathbf{R_{t+3}}+\ldots\Big{|}\mathbf{S_{t}}=\mathbf{s}\Big{]}\]  
**Policy-oriented approach**  
Policy-oriented reinforcement learning aims to optimize the policy function \(\mathbf{\pi}(\mathbf{s})\) without using the value function. Policy is what determines the behavior of an agent at a given time. The agent learns a policy function. This helps him to map each situation to the best possible action.