Abstract:  This paper proposes an unmanned aerial vehicle path planning method based on deep reinforcement learning for complex and ever-changing three-dimensional unknown environments. This method optimizes strategies within a limited observation space to address the challenges posed by high complexity and uncertainty. Firstly， within a limited perceptual range， the YOLO network is used to extract obstacle information from the image information. Secondly， this paper designs hazard levels to address the issue of varying amounts of obstacle information at different times， and combines the extracted information from hazard levels with radar information as input to the intelligent agent. Finally， based on the proximal strategy optimization algorithm， an action selection strategy under state decomposition is designed to improve the effectiveness of drone actions. Through simulation evaluation in Gazebo， the experimental results show that compared to the proximal strategy optimization algorithm， the average reward per round has increased by 15.6 percentage points， and the average success rate has increased by 2.6 percentage points.  

Key words:  , unmanned aerial vehicle, path planning, deep reinforcement learning, YOLOv4