UAV Path Planning for Aviation Optimazition Based on Doubly Decoupled Reinforced Network

Deep reinforcement learning models have achieved promising results in the path planning problem for uncrewed aerial vehicles (UAVs). However, their update mechanisms can lead to overestimation and poor stability. This study addresses these issues by using a more realistic reward function, assigning priorities to experiences in the experience replay pool, and employing double decoupling of state and action values in Q-networks. We train the improved Deep Q-Network (DQN) algorithm for three-dimensional environment simulation experiments in a simulated environment. The 3D simulation experiments compare the algorithm with the A-star and unimproved DQN algorithms. The experimental results show that the algorithm has been improved, demonstrating its enhanced performance in the final path planning results. Moreover, the final testing results reveal that the UAV can safely reach the target point from the starting point.