## 7 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Model Based Learning** & **Free-Mode Learning** \\ \hline IV2 XQ [54] & \(\times\) & ✓ \\ \hline QAGR [55] & \(\times\) & ✓ \\ \hline CEPF [56] & \(\times\) & ✓ \\ \hline PFQ-AODV [57] & \(\times\) & ✓ \\ \hline QGrid [58] & \(\times\) & ✓ \\ \hline RRPV [59] & ✓ & \(\times\) \\ \hline QTAR [60] & \(\times\) & ✓ \\ \hline Q-LBR [61] & \(\times\) & ✓ \\ \hline ECTS [62] & \(\times\) & ✓ \\ \hline RLRC [63] & \(\times\) & ✓ \\ \hline GLS [64] & \(\times\) & ✓ \\ \hline RSAR [65] & \(\times\) & ✓ \\ \hline [PERSON] et al. [66] & \(\times\) & ✓ \\ \hline RHR [67] & \(\times\) & ✓ \\ \hline SeScR [68] & \(\times\) & ✓ \\ \hline IRQ [69] & \(\times\) & ✓ \\ \hline QFHR [70] & \(\times\) & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of RL-based routing methods in terms of learning model. in this case, computational cost and communication overhead are increased compared to centralized routing approaches because the network topology information is obtained locally by exchanging beacon messages between vehicles in the network. However, these methods are scalable, are more consistent with the dynamic network environment. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Centralized Learning** & **Distributed Learning** \\ \hline IV2 XQ [54] & ✓ & \(\star\) \\ \hline QAGR [55] & \(\star\) & ✓ \\ \hline CEPF [56] & \(\star\) & ✓ \\ \hline PFQ-AODV [57] & \(\star\) & ✓ \\ \hline QPAGR [55] & \(\star\) & ✓ \\ \hline PEP-AODV [57] & \(\star\) & ✓ \\ \hline QPGrid [58] & \(\star\) & ✓ \\ \hline RRPV [59] & \(\star\) & ✓ \\ \hline QTAR [60] & \(\star\) & ✓ \\ \hline RALR [65] & \(\star\) & ✓ \\ \hline RW et al. [66] & \(\star\) & ✓ \\ \hline RHR [67] & \(\star\) & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Comparison of RL-based routing schemes according to learning process. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Traditional Reinforcement Learning** & **Deep Reinforcement Learning** \\ \hline IV2 XQ [54] & ✓ & \(\star\) \\ \hline QAGR [55] & ✓ & \(\star\) \\ \hline CEPF [56] & ✓ & \(\star\) \\ \hline PFQ-AODV [57] & ✓ & \(\star\) \\ \hline QGrid [58] & ✓ & \(\star\) \\ \hline RRPV [59] & ✓ & \(\star\) \\ \hline QTAR [60] & ✓ & \(\star\) \\ \hline Q-LBR [61] & ✓ & \(\star\) \\ \hline ECTS [62] & ✓ & \(\star\) \\ \hline RLRC [63] & ✓ & \(\star\) \\ \hline GLS [64] & ✓ & \(\star\) \\ \hline RSAR [65] & ✓ & \(\star\) \\ \hline Wu et al. [66] & ✓ & \(\star\) \\ \hline RHR [67] & ✓ & \(\star\) \\ \hline Se\&R [68] & \(\star\) & ✓ \\ \hline IRQ [69] & ✓ & \(\star\) \\ \hline QFHR [70] & ✓ & \(\star\) \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of RL-based routing methods in terms of learning algorithm. Finally, Table 16 has categorized the RL-based routing schemes with regard to routing techniques. In this table, it can be found that IV2 XQ, QGrid, GLS, QAGR, CEPF, RRPV, QTAR, RASR, IRQ, and QFHR are position-based routing approaches. These routing methods do not require information about the entire network and use local information to send data packets. As a result, they have low communication overhead and efficiently consume bandwidth and energy resources. In this type of routing technique, RRPV, QTAR, and RSAR are known as the DTN routing methods. This means that they use the store-carry-forward technique to transfer data packets to the target node. However, this technique has a good routing overhead; but it boosts delay in the data transfer operation. On the other hand, IV2 XQ, GLS, QAGR, CEPF, IRQ, QFHR are known as the non-DTN routing protocols. This means that they use a greedy forwarding technique for data transfer. These methods have a good performance in dense networks. Moreover, they are scalable, have low routing overhead, and consume low memory and bandwidth.