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This paper focuses on the continuous control of the unmanned aerial vehicle(UAV) based on a deep reinforcement learning method for a large-scale 3Dcomplex environment. The purpose is to make the UAV reach any target point froma certain starting point, and the flying height and speed are variable duringnavigation. In this work, we propose a deep reinforcement learning (DRL)-basedmethod combined with human-in-the-loop, which allows the UAV to avoid obstaclesautomatically during flying. We design multiple reward functions based on therelevant domain knowledge to guide UAV navigation. The role ofhuman-in-the-loop is to dynamically change the reward function of the UAV indifferent situations to suit the obstacle avoidance of the UAV better. Weverify the success rate and average step size on urban, rural, and forestscenarios, and the experimental results show that the proposed method canreduce the training convergence time and improve the efficiency and accuracy ofnavigation tasks. The code is available on the websitehttps://github.com/Monnalo/UAV_navigation.

UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary 3D Environment