# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 7 Discussion
The blind path issue is another challenge when designing RL-based routing protocols. This issue means that the discovered paths in the network have expired before they reach their deadline, while nodes are unaware of this issue and send their data packets through the failed paths. This issue increases packet loss in the network. This problem has been resolved in QTR, QMR, QLGR, Q-FANET, PARRoT, and TARRAQ. In Table 24, RL-based routing methods are compared in terms of simulation tools, mobility model, localization service, and network environment. Network simulator version 3 (NS3), MATLAB, and WSNet are the most common simulation tools for simulating routing protocols to evaluate their performance in a virtual network environment under different scenarios. These tools help researchers to analyze the performance of these methods carefully before use in real environments. GPS is the most common positioning service, which is used to find the position and speed of UAVs in FANET. However, this positioning service is highly costly in terms of bandwidth consumption. On the other hand, when routing protocols use a positioning system in their routing process, their performance is dependent on the positioning system, meaning that if the positioning system is not accurate and UAVs cannot calculate their position accurately, the routing protocol is also not accurate. FEQ-routing-SA is the only routing technique that does not use position information in the routing process. Researchers can regard this idea in the future because it reduces routing overhead and prevents congestion in the network. Furthermore, random waypoint (RWP) and Gauss-Markov(GM) are the most common mobility models for simulating drone movement on the network. Another important point is that 3 DQ has implemented the movement of UAVs in the network only based on the Gauss-Markov mobility model and relies on this model throughout the routing process. Additionally, TARRAQ has limited drone movement to the RWP mobility model. However, it is not an accurate model for simulating drone movement. When a routing method is designed based on a particular mobility model, this issue makes serious challenges for adapting these routing protocols with other mobility models, as well as the actual motion of UAVs in a real network environment. This weakens the network performance. Another important point is that the FANET is a three-dimensional network environment. However, some RL-based routing methods such as TQNGPSR, QMR, QGeo, QLGR, Q-FANET, OFL-MOR, 3 DQ, and ICRA are simulated in a two-dimensional environment, which is not compatible with the FANET environment. Moreover, Table 25 specifies that RL-based routing methods have been evaluated in terms of some routing criteria. In this evaluation, we consider the most important routing scales, including energy consumption, end-to-end delay, network lifetime, packet delivery rate, throughput, connectivity, and routing overhead. Most researchers evaluate their routing methods in terms of packet delivery rate and end-to-end delay because FANET is a dynamic network that suffers from frequent link failure in the network. This significantly decreases the validity time of the paths formed in the network. Additionally, the path failure causes high packet loss in FANETs compared to other ad hoc networks. Repairing and reconstructing the failed communication routes are also time-consuming. Therefore, the two important purposes for many researchers are (1) reducing delay and (2) increasing the packet delivery rate in the routing protocols designed for FANETs.