  Uncrewed autonomous vehicles (UAVs) have made significant contributions to
reconnaissance and surveillance missions in past US military campaigns. As the
prevalence of UAVs increases, there has also been improvements in counter-UAV
technology that makes it difficult for them to successfully obtain valuable
intelligence within an area of interest. Hence, it has become important that
modern UAVs can accomplish their missions while maximizing their chances of
survival. In this work, we specifically study the problem of identifying a
short path from a designated start to a goal, while collecting all rewards and
avoiding adversaries that move randomly on the grid. We also provide a possible
application of the framework in a military setting, that of autonomous casualty
evacuation. We present a comparison of three methods to solve this problem:
namely we implement a Deep Q-Learning model, an $\varepsilon$-greedy tabular
Q-Learning model, and an online optimization framework. Our computational
experiments, designed using simple grid-world environments with random
adversaries showcase how these approaches work and compare them in terms of
performance, accuracy, and computational time.
