Sungjoon Lee  Gyu Seon Kim  Taejin Woo  Soohyun Park

10.5626/JOK.2026.53.2.101

Reinforcement Learning Transfer Learning Drone reconnaissance spatial adaptive general-purpose model

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.