# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 5 Comparison of QL-Based Position-Aware Routing Protocols
Similarly, PARRoT also gives higher PDR in highly dynamic FANETs because it updates the discount factor and Q-values of the neighbor link according to the relative trajectory knowledge (by calculating the predictive link duration). To adaptively train the QL model according to the mobility dynamism in FANETs, as discussed in Section 4, several algorithms adaptively set the learning rate and discount factor. QGEO considers only two different discount factor values according to inter-UAV distances. In contrast, QMR controls both the learning rate and discount factor according to the normalized one-hop delay and changes in the neighbor sets. The QTAR algorithm adopts the same technique but considers two-hop delay to control the learning rate. As a result, it can produce a more precise Q-value and adopts the dynamical topology behavior more adaptively compared to others. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Protocol** & **Year** & **Mobility Model** & **Localization** & **Neighbor Information** & **MAC Protocol** & **Exploration Strategy** & **Simulation Tool** \\ \hline QGEO [39] & 2017 & Gaussian Markov & GPS & One-hop & IEEE 802.11g & \(\times\) & NS-3 \\ \hline RFLQGEO [50] & 2019 & Gaussian Markov & GPS & One-hop & IEEE 802.11g & \(\times\) & NS-3 \\ \hline QLCLRP [51] & 2019 & - & - & One-hop & CSMA/MAS & UCB & WSNet \\ \hline QMR [22] & 2020 & - & GPS & One-hop & IEEE 802.11 & One-hop PTS & WSNet \\ \hline QLMF [57] & 2020 & Random & - & One-hop & - & \(\times\) & MATLAB \\ \hline Q-FANET [24] & 2021 & RWP & GPS & One-hop & IEEE 802.11 & \(\in\)-greedy & WSNet \\ \hline QTR [23] & 2021 & Gaussian Markov & GPS & Two-hop & IEEE 802.11n & Two-hop PTS & MATLAB \\ \hline PARRoT [29] & 2021 & RWP, DDD, and DCH & GPS & One-hop & IEEE 802.11g & \(\times\) & OMNeT++ \\ \hline SAIQL [56] & 2021 & Piecewise linear mobility & - & One-hop & - & SAO & - \\ \hline
**Protocol** & **Path Strategy** & **Local Minimum Avoidance** & **Adaptive Hello Interval** & **Link Stability** & **Localization Exploration** & **Scalability** \\ \hline QGEO [39] & Single & Yes & No & Medium & Yes & High \\ \hline RFLQGEO [50] & Single & Yes & No & Medium & Yes & High \\ \hline QLCLRP [51] & Single & Yes & No & Medium & Yes & Medium \\ \hline QMR [22] & Single & Yes & No & High & No & High \\ \hline QLMF [57] & Single & - & No & Medium & No & Low \\ \hline Q-FANET [24] & Single & Yes & No & Medium & No & Medium \\ \hline QTR [23] & Single & Yes & Yes (Minimum LD) & High & No & Medium \\ \hline PARRoT [29] & Single & - & No & High & No & High \\ \hline SAIQL [56] & Single & Yes & No & Medium & No & Medium \\ \hline
**Protocol** & **End-to-End Delay Optimization** & **UAV Energy Efficiency** & **Control Overhead** & **PDR** & **Adaptive Learning** \\ \hline QGEO [39] & Yes (MAC delay) & No & Low & Medium & Partial \\ \hline RFLQGEO [50] & Yes (MAC delay) & No & Low & Medium & No \\ \hline QLCLRP [51] & Yes (MAC delay) & No & Low & Medium & Yes \\ \hline QMR [22] & Yes (MAC and queuing delay) & Yes & Low & High & Yes \\ \hline QLMF [57] & Yes & Yes & Medium & Medium & No \\ \hline Q-FANET [24] & Yes (MAC and queuing delay) & No & Low & Medium & No \\ \hline QTR [23] & Yes (MAC, queuing, and transmission delay) & Yes & High & High & Yes \\ \hline PARRoT [29] & \(\times\) & No & Medium & High & Yes \\ \hline SAIQL [56] & \(\times\) & Yes & Medium & Medium & Yes \\ \hline \hline \end{tabular}  
**Note: “-”:** Information is not provided in the corresponding article; “\(\times\)”: The corresponding feature is not supported; DCF: distributed coordination function; LD: link duration.  
\end{table} 
Table 7: Qualitative comparison of QL-based position-aware routing protocols.