## 7 Discussion
According to this technique, the broadcast interval of the beacon message is dynamically determined using a broadcast control strategy. Table 12 compares various routing methods in terms of learning framework. According to this table, it can be found that the routing approaches such as IV2 XQ, QAGR, CEPF, PFQ-AODV, QGrid, QTAR, ECTS, RLRC, GLS, RSAR, Wu et al., RHR, SeScR, IRQ, QFHR apply single-agent reinforcement learning in their routing process. These routing schemes are \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Control Message** & **Dissemination Process** \\ \hline IV2 XQ [54] & Beacon & Broadcast (for single-hop neighbors locally) \\ \hline \multirow{2}{*}{QAGR [55]} & \(Hellow_{2U}\) & Unicast \\ \cline{2-3}  & \(Hellow_{2U}\) & Broadcast (for single-hop neighbors locally) \\ \hline CEPF [56] & Hello & Broadcast (for single-hop and two-hop neighbors locally) \\ \hline \multirow{3}{*}{PFQ-AODV [57]} & Hello & Broadcast (for single-hop and two-hop neighbors locally) \\ \cline{2-3}  & RREQ & Broadcast (flooding) \\ \cline{2-3}  & RREP & Unicast \\ \hline QGrid [58] & Hello & Broadcast (for single-hop neighbors locally) \\ \hline RRPV [59] & Hello & Broadcast (for single-hop neighbors locally) \\ \hline \multirow{3}{*}{QTAR [60]} & \(Hellow_{2U}\) & Broadcast to neighboring vehicles locally \\ \cline{2-3}  & \(Hello_{R2R}\) & Broadcast to neighboring RSUs locally \\ \hline \multirow{3}{*}{Q-LBR [61]} & Hello & Broadcast to UAVs and sharing ground network congestion \\ \cline{2-3}  & RREQ & Broadcast (Flooding) \\ \cline{2-3}  & RREP & Unicast \\ \hline \multirow{3}{*}{ECTS [62]} & Beacon & Broadcast (for single-hop neighbors locally) \\ \hline \multirow{3}{*}{RLRC [63]} & Hello & Broadcast (for single-hop and two-hop neighbors locally) \\ \cline{2-3}  & RREQ & Broadcast (Flooding) \\ \cline{2-3}  & RREP & Unicast \\ \hline GLS [64] & Hello & Broadcast (for single-hop and two-hop neighbors locally) \\ \hline \multirow{3}{*}{RSAR [65]} & Hello & Broadcast (for single-hop neighbors locally) \\ \cline{2-3}  & RREQ & Broadcast (Flooding) \\ \cline{2-3}  & RREP & Unicast \\ \hline [PERSON] et al. [66] & Hello & Broadcast (for single-hop neighbors locally) \\ \hline RHR [67] & Beacon & Broadcast (for single-hop neighbors locally) \\ \hline SeScR [68] & Beacon & Broadcast (for single-hop neighbors locally) \\ \hline \multirow{3}{*}{IRQ [69]} & Beacon & Broadcast (for single-hop neighbors locally) \\ \cline{2-3}  & Traffic message & Unicast \\ \hline QFHR [70] & Hello & Broadcast (for single-hop neighbors locally) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of various routing methods in terms of control messages. simpler than the multi-agent approaches and have less computational complexity. However, the learning capability and convergence rate of the agent in single-agent routing schemes is less those that in multi-agent approaches. According to Table 12, it can be deduced that only RRPV uses multi-agent technique. In this protocol, several agents (vehicles) interact with each other and share their experiences. This has increased the learning capability of this routing scheme, especially for large-scale networks. However, RRPV has more computational complexity than other single-agent approaches. Moreover, Table 13 compares the routing approaches in terms of learning model. According to this table, it can be found that most routing protocols such as IV2 XQ, QAGR, CEPF, PFQ-AODV, QGrid, QTAR, ECTS, RLRC, GLS, RSAR, [PERSON] et al., RHR, SeScR, IRQ, QFHR use a free-model reinforcement learning framework. In these methods, the agent estimates the value function according to the obtained experience and does not build any model from the learning environment. These routing schemes have less computational complexity compared to model-based routing methods. However, they need more experience compared to model-based techniques and are not flexible. Our studies show that RRPV is only model-based routing method. This method uses fuzzy logic to build the network model.