# Reinforcement Learning Environment for Advanced Vehicular Ad Hoc Networks Communication Systems
## 1 Introduction  
A vehicular ad-hoc network (VANET) allows cars and roadside devices to connect with one another [1]. Even if they have no prior knowledge of other vehicles in the region, automobiles are viewed as nodes in a self-organizing network for communication reasons [2]. This means that there is no preexisting infrastructure required for this decentralized wireless ad-hoc network. Each node acts as a host as well as a router, delivering and receiving data between nodes. To communicate between devices, dedicated short-range communication radios (DSRC) are employed [3]. DSRC is a wireless communication system based on the IEEE 802.11p standard that allows automobiles to communicate directly at high speeds and with high security without the need for cellular infrastructure. Also, the 5.9 GHz frequency is used by DSRC to allow low-latency information sharing between automobiles.  
This architecture is in charge of delivering and receiving safety alerts, as well as maintaining passenger and pedestrian safety [4]. It also improves the flow of traffic and the effectiveness of the traffic management system. An onboard unit (OBU) has several sensors such as a GPS, accelerometer, resource command processor, user interface, and read/write storage for storing data. Over an IEEE 802.11p wireless connection, OBUs are in charge of communicating between surrounding devices [5]. The most difficult component of VANET is managing and routing the data required for optimum connectivity.  
The data broadcasted and received by vehicular units in ad-hoc vehicular networks comprise information on the telemetry of the related cars. When information is transmitted over the air, it is very sensitive to interference, which can result in a network outage, putting the lives of drivers and anyone near to them in danger. Because there is no large infrastructure involved in a shared wireless medium, there is a high risk of car-to-car communication failure. In multi-hop communication, nodes or vehicular units function as hosts and routers, forwarding and receiving data from other nodes [6].  
As a result, nodes are chosen based on their connectivity, so the routing algorithm selected is a sensitive issue that must be handled carefully in this type of system. Routing protocols developed for legacy networks may not adequately serve vehicular networks in the near future. Therefore, new alternatives must be developed to solve the routing problem in these complex networks.  
We provide techniques for improving the performance of the methodology we have developed, such as:  
1. Choosing the best path based on signal strength between adjacent nodes and avoiding hosts with high Path Loss to avoid retransmissions;
2. Using a reward feature that assesses the path loss between hosts, choose the route with the longest lifespan;
3. Analyzing performance parameters like route lifetime, number of reconnects, and hop counts. The following are the article's primary contributions:  
* We improve the Reinforcement Learning environment where an agent can learn to find the path with the most extended lifetime. A procedure determines the chosen path based on signal power between the nodes, and as a reward, the path's lifespan is connected.
* The impacts of existing procedures were compared to the novel paradigm suggested in this work utilizing computer-based simulation tools.
* And, we compare the results with two legacy routing algorithms, to prove the efficiency of the proposed work.  
The following is how the remainder of this research paper is organized: The Section 2 looks into the related work. The environment model is discussed in Section 3. Then, in Section 4, the outcomes and discussion are defined. The conclusion in Section 5 brings the paper to a close.