# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 3 Artificial Intelligence
### Regression Algorithms
#### 3.1.3 Reinforcement Learning (RL)
RL stands out from other algorithms due to its unique actions. They involve trial-and-error investigations and postponed reward calculations. Improved learning algorithms usually include four key components: policy, reward function, value function, and environment. The ML paradigm teaches an agent how to operate locally and decide how to optimize the overall benefit or minimize a cumulative penalty [76]. In contrast to the SL and UL paradigms, the error metric is time-dependent, and the temporal variable is decisive. Specifically, when compared to the supervised approach, RL operates without the need for annotated datasets. In response to the actions of the agent, the environment provides feedback. The RL structure is commonly composed of Markov Decision Support (MDS) systems, which employ dynamic programming techniques to optimize the incentive given. DNN-based frameworks that have substantially enhanced this learning approach have been developed lately [77; 78]. The agent analyzes state St at each step t and selects an action A\({}_{\text{t}}\) in action space A. The new state arrives after the object obtains a scalar reward R\({}_{\text{t}}\) that signifies the standard of the action selected and advances to the subsequent one. The RL method tries how to relate this mixture of situations (St, At) from states to the best possible operation, which is \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 \multirow{4}{*}{K-means} & Beneficial in concealing secret information related and operators; does not require labeled information & Enhances cluster stability in VANET approaches based on clusters; creation of cryptographic algorithms to improve the security & Not capable of determining the optimal number of clusters; maintaining cluster reliability; maximizing cluster similarities \\ \hline \multirow{4}{*}{Hierarchical clustering (HC) to ones} & Connectivity-based clustering model for grouping data points, connect geographically close to ones & Spatial and social proximity for the effective exchange in the cluster; deals with the complicated and ever-changing nature of V2V and V2I; boosts the effectiveness of data transmission, decreases overhead; network scalability \\ \hline \multirow{4}{*}{GMM} & Arrange information into groups; probability distribution & Cluster vehicles based on mobility patterns and locations; detect anomalies and misbehaving vehicles; predict future locations and trajectories & Less efficiency and longer delays in real-time software; overfitting issue; sensitivity to noise \\ \hline \multirow{4}{*}{MC} & Autonomous systems with transparent states & Ability to monitor, gather, and disseminate data regarding traffic, driving conditions, and potential dangers & No history maintenance; future prediction \\ \hline \multirow{4}{*}{HMM} & Used for autonomous systems with partially transparent states & Describe the connection between observations and hidden states that cause them & Restricted accuracy \\ \hline \multirow{4}{*}{PCA} & & Reduce dimensions and lessen operational overhead; capable of reducing dimensionality, predicting Denial of Service (DoS) attacks and identifying driving hazards; optimizing buffering of multimedia contents & Low interpretability of principal components; outliers; affecting network performance and reliability \\ \hline \multirow{4}{*}{AR} & Identification of basic correlations and faults quickly & Identify and recognize road incidents; produce routes and locations & Difficult to find the optimal number of clusters, maintain reliability, and maximize cluster similarities \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Unsupervised Learning: primary advantages, uses, and issues in the VANET domain. an estimate of the long-term benefit of staying in this position. The process by which an RL agent learns has been illustrated in Figure 5.  
The responsibility of mapping conditions to actions performed by the agent lies with the policies.