# A Reinforcement Learning Model of Multiple UAVs for Transporting Emergency Relief Supplies
## 6 Conclusions and Future Work  
Supplying isolated shelters with sufficient supplies quickly and adequately during a disaster saves many lives and provides victims with a sense of security. Soon after a disaster occurs, land transportation may be difficult due to damaged roads and traffic congestion, and UAVs offer a promising potential solution to this problem. However, the limited payload of a UAV is insufficient for a single transport to meet the various material demands of shelters, so considering the sequence of multi-UAV destinations and the types of materials to be transported is an important issue in a crisis situation. In this study, we developed a model for transporting emergency relief supplies by multiple UAVs using QL. To evaluate the performance of QL in the UAVRPs, we compared the transportation routes of the previous model. As a result, we were able to develop route planning with a shorter transportation route than the conventional method, which confirms the performance of our method in improving the efficiency of transportation distance. In addition, we confirmed that it is possible to transport high-priority items quickly and to eliminate the disparity in supply among shelters. We were able to quantitatively evaluate the transportation time and the percentage of high-priority items that could be transported within a time limit for each of the disaster response measures. This result may be used to reduce the deviation of the supply to each shelter when the demand for supplies is so large that the transportation of supplies cannot be completed within a certain time limit. In particular, in a disaster such as a tsunami-related disaster, where damage occurs over a wide area, a large number of people may be displaced and the demand for shelters may increase massively at the same time. Since the equity-oriented transportation in this study can prevent disparities in supplies at each shelter, it can be applied as a transportation strategy for supplies, such as medicines and blood, that require small quantities but need to be supplied quickly. For the practical application of this study, it is necessary to grasp the needs of each evacuation center for supplies in advance. This could be solved by utilizing UAVs equipped with communication capabilities as emergency communication networks [36] and by forecasting the demand for supplies [51]. With this information, effective last-mile delivery may be achieved by utilizing this study's method, taking into account the number of UAVs available, payload limitations, and battery limitations. There is room for improvement in the model. In HL such as disaster response, transportation must be planned in as little time as possible due to the uncertainty of demand for supplies. However, QL, like other heuristic methods, requires time for calculation because it must determine various parameters and then perform optimization. It is necessary to extend the UAV transportation planning problem to consider highly uncertain situations, such as the urgency of demand and supplies and the number of UAVs available. Figure 15: Minimum objective values for each number of UAVs in Scenario 1. (**left**) \(N_{U}\) is from 1 to 10. (**right**) \(N_{U}\) is from 3 to 10. Planning the transportation of items by multiple UAVs using the model of this study will enable the stable and rapid transportation of items to isolated disaster areas such as tsunami-flooded areas in the event of an actual disaster. The ultimate goal of this research is to develop a multiple UAV planning tool to optimize the allocation of UAVs and support decision making for disaster relief and supply transportation. Conceptualization and Overall Research Design, S.K. ; methodology, D.H. and E.M.; validation D.H.; visualization D.H. and E.M.; writing--original draft, D.H. and E.M.; writing--review and editing, D.H., E.M. and S.K. ; supervision, E.M. and S.K. ; funding acquisition, E.M. and S.K. All authors have read and agreed to the published version of the manuscript.