@article{M7FE1029C,
title = "Design of a Deep Reinforcement Learning Algorithm forSpatially Adaptive UAV Autonomous Navigation based onTransfer Learning",
journal = "Journal of KIISE, JOK",
year = "2026",
issn = "2383-630X",
doi = "10.5626/JOK.2026.53.2.101",
author = "Sungjoon Lee, Gyu Seon Kim, Taejin Woo, Soohyun Park",
keywords = "Reinforcement Learning, Transfer Learning, Drone, reconnaissance, spatial adaptive, general-purpose model",
abstract = "This study proposes a deep reinforcement learning algorithm for spatially adaptive
unmanned aerial vehicle (UAV) autonomous navigation, utilizing transfer learning to enhance exploration
efficiency across various environments. UAVs are vital for both military and civilian missions but face
challenges when operating in diverse and dynamic settings. Traditional reinforcement learning methods
are inefficient as they necessitate relearning from scratch in new environments. To overcome this
limitation, the study implements transfer learning, which allows knowledge gained in one environment
to be applied in another, thus improving learning speed and energy efficiency. By integrating Deep
Q-Networks (DQN) with transfer learning, UAVs can effectively explore and adapt to different
mission areas. Experimental results indicate that the proposed method achieves faster convergence and
superior exploration performance compared to existing reinforcement learning techniques, highlighting
its potential for practical applications."
}