Solving reward-collecting problems with UAVs: a comparison of online optimization and Q-learning
This work addresses the challenge of improving UAV mission success and survival in military reconnaissance and surveillance, though it appears incremental as it compares existing methods in a simulated setting.
The paper tackled the problem of finding a short path for UAVs to collect rewards and avoid random adversaries in grid-world environments, comparing Deep Q-Learning, tabular Q-Learning, and online optimization methods in terms of performance, accuracy, and computational time.
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.