Abstract
In smart city scenarios, the use of unmanned aerial vehicle (UAV) networks is one of actively discussed technologies. In this paper, we consider the scenario where carpoolable UAV-based drone taxis configure their optimal routes to deliver packages and passengers in an autonomous and efficient way. In order to realize this application with drone-taxi UAV networks, a multiagent deep reinforcement learning (MADRL) based algorithm is designed and implemented for the optimal route configuration. In the corresponding MADRL formulation, the drone-taxi related states, actions, and rewards are defined in this paper. Lastly, we confirm that our proposed algorithm achieves desired results.
Anna Yoo Jeong Ha
Ph.D. Student in Computer Science
My research interests include adversarial machine learning and security in AI.