  This paper focuses on the continuous control of the unmanned aerial vehicle
(UAV) based on a deep reinforcement learning method for a large-scale 3D
complex environment. The purpose is to make the UAV reach any target point from
a certain starting point, and the flying height and speed are variable during
navigation. In this work, we propose a deep reinforcement learning (DRL)-based
method combined with human-in-the-loop, which allows the UAV to avoid obstacles
automatically during flying. We design multiple reward functions based on the
relevant domain knowledge to guide UAV navigation. The role of
human-in-the-loop is to dynamically change the reward function of the UAV in
different situations to suit the obstacle avoidance of the UAV better. We
verify the success rate and average step size on urban, rural, and forest
scenarios, and the experimental results show that the proposed method can
reduce the training convergence time and improve the efficiency and accuracy of
navigation tasks. The code is available on the website
https://github.com/Monnalo/UAV_navigation.
