# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 3 Artificial Intelligence
### Regression Algorithms
#### 3.1.3 Reinforcement Learning (RL)  
RL is a framework where agents or controllers enhance their actions during interactions with their environment. It maximizes total reward by connecting states to actions via reinforcement signals. In contrast to typical ML algorithms, learners are not given pre \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**ML Types** & **Advantages** & **Uses Areas in VANET** & **Issues** \\ \hline DT & Transparent and user-friendly & Efficient routing option; optimizing traffic signal control; identifying pavement actions shown by malevolent vehicles & Large space required; \\ \hline SVM & Manages dataset; high dimensionality and limited amount of data elements & Avoiding and identifying assaults, i.e., vehicle misbehavior, intrusion, vehicles; cluster optimization; enhancing decision-making process for spectral distribution & Zero-frequency problem; Estimations can be inaccurate in certain instances \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & System resilient, disregarding or removing unimportant attributes; straightforward to build and minimal training needs & Enhances prediction accuracy; mitigates the occurrence of broadcast storms and reduce accidents; identification of malfunctioning vehicle & Zero-frequency problem; Estimations can be inaccurate in certain instances \\ \hline KNN & Optimal categorization using voting & Identify and track instances of effective inside attacks accurately; safeguard privacy by protecting essential details; enhance and preserve cluster stability; identify vehicular mishaps and provide alerts for sites & High time complexity; Euclidean distance measure may not be universally applicable to all datasets; requirement for substantial quantity data \\ \hline Random Forest & Addresses the issue of overfitting; avoids the necessity for feature selection with low input data & Forecasting vehicular traffic jams; enhancing handover assistance for the distribution of multimedia content across vehicles; allocating control channels; high accuracy on large datasets & Analyzing outcomes without adequate hyperparameter tuning; possible misclassification of instances belonging to the minority class \\ \hline Linear Classification & Classification algorithm; outcome by evaluating the numerical sum of the features & Significant increase in the effectiveness of the testing and training processes; capacity to be helpful for certain large-scale applications, including logistic regression, SVM, etc. & Insufficient for managing indivisible data \\ \hline EL & Resistant to overfitting and reduces variance; performs more efficiently using an adaptor instead of a classification tool & Identify and recognize inappropriate actions performed by malevolent nodes; enhance the precision of vehicle geolocation data; optimize the process of transferring control between vehicles and RSUs; improve prediction accuracy, stability in predictions & Leads to computational complexity, potentially affecting the ability to interpret real-time data in dynamic traffic settings \\ \hline Linear Regression & Simple and estimates the value of unfamiliar data by utilizing another correlated and familiar data value; calculates coefficients & Calculate journey time; important for designing routes, managing traffic, and enhancing overall productivity & Limited accuracy in predicting complicated networks; may not maximize radio usage efficiently \\ \hline Logistic Regression & Calculate the probability of a scenario happening or a categorical variable; easier to manage multiple explanatory variables simultaneously & Improve the identification; analysis of message logs & Limited to binary outcomes; Selecting a model is difficult; requires knowledge; overfitting with multicollinearity \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Supervised Learning: primary advantages, uses, and issues in the VANET domain. determined actions. Rather, RL involves trial and error to determine which activities yield the best reward.