  Deep Reinforcement Learning (DRL) emerges as a prime solution for Unmanned
Aerial Vehicle (UAV) trajectory planning, offering proficiency in navigating
high-dimensional spaces, adaptability to dynamic environments, and making
sequential decisions based on real-time feedback. Despite these advantages, the
use of DRL for UAV trajectory planning requires significant retraining when the
UAV is confronted with a new environment, resulting in wasted resources and
time. Therefore, it is essential to develop techniques that can reduce the
overhead of retraining DRL models, enabling them to adapt to constantly
changing environments. This paper presents a novel method to reduce the need
for extensive retraining using a double deep Q network (DDQN) model as a
pretrained base, which is subsequently adapted to different urban environments
through Continuous Transfer Learning (CTL). Our method involves transferring
the learned model weights and adapting the learning parameters, including the
learning and exploration rates, to suit each new environment specific
characteristics. The effectiveness of our approach is validated in three
scenarios, each with different levels of similarity. CTL significantly improves
learning speed and success rates compared to DDQN models initiated from
scratch. For similar environments, Transfer Learning (TL) improved stability,
accelerated convergence by 65%, and facilitated 35% faster adaptation in
dissimilar settings.
