Unmanned aerial vehicles (UAVs) have emerged as a promising technology in search and rescue operations (SAR). UAVs have the ability to provide more timely localization, thus decreasing the crucial duration of SAR operations. Previous work have demonstrated proof-of-concept in regard to localizing missing people by utilizing received signal strength (RSS) and UAVs. The localization system is based on the assumption that the missing person wears an enabled smartphone whose Wi-Fi signal can be intercepted. This thesis proposes a two-staged path planner for UAVs, utilizing RSS-signals and an initial belief regarding the missing person's location. The objective of the first stage is to locate an RSS-signal. By dividing the search area into grids, a hierarchical solution based on several Markov decision processes (MDPs) can be formulated which takes different areas probabilities into consideration. The objective of the second stage is to isolate the RSS-signal and provide a location estimate. The environment is deemed to be partially observable, and the problem is formulated as a partially observable Markov decision process (POMDP). Two different filters, a point mass filter (PMF) and a particle filter (PF), are evaluated in regard to their ability to correctly estimate the state of the environment. The state of the environment then acts as input to a deep Q-network (DQN) which selects appropriate actions for the UAV. Thus, the DQN becomes a path planner for the UAV and the trajectory it generates is compared to trajectories generated by, among others, a greedy-policy.  Results for Stage 1 demonstrate that the path generated by the MDPs prioritizes areas with higher probability, and intuitively seems very reasonable. The results also illustrate potential drawbacks with a hierarchical solution, which potentially can be addressed by considering more factors into the problem. Simulation results for Stage 2 show that both a PMF and a PF can successfully be used to estimate the state of the environment and provide an accurate localization estimate. The PMF generated slightly more accurate estimations compared to the PF. The DQN is successful in isolating the missing person's probable location, by relatively few actions. However, it only performs marginally better than the greedy policy, indicating that it may be a complicated solution to a simpler problem.