SOLVING REWARD-COLLECTING PROBLEMS WITH UAVS AGAINST THE STOCHASTIC ADVERSARY THROUGH REINFORCEMENT LEARNING AND ONLINE OPTIMIZATION

Abstract

Unmanned autonomous vehicles (UAV) have made significant contributions to reconnaissance and surveillance missions in past U.S. military campaigns. As the prevalence of UAVs increases, there have also been improvements in counter-UAV technology that make it difficult for UAVs 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 present a comparison of two methods to solve this problem: a Deep 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.