Multi-UAVs Strategies for Ad Hoc Network with Multi-Agent Reinforcement Learning
George Karimata, Jin Nakazato, Gia Khanh Tran, Katsuya Suto, Manabu Tsukada, Hiroshi Esaki · 2024
In recent years, extensive research has focused on leveraging advanced technologies beyond 5G and for Industry 5.0 to promote sustainability and prosperity in society. Our study advances this effort by seeking to create an aerial perspective using Unmanned Aerial Vehicles (UAVs). This paper introduces a method for optimizing UAV deployment strategies using multi-agent reinforcement learning, facilitating the formation of a flying ad hoc network. The results demonstrate practical cooperation among UAVs in flight.