Traditional path planning algorithms for autonomous underwater vehicles (AUV) in 3D marine environments suffer from long search times,strong dependence on environment,and the need for re-planning when environment changes,which fails to meet real-time requirements.To enable AUVs to autonomously learn scenes and make decisions,an improved Dueling Deep Q-Network (DQN) algorithm was proposed,in which the traditional network structure was modified to adapt to AUV path planning scenarios.Additionally,addressing the difficulty of searching for target points in 3D space,an experience distillation replay pool was introduced based on the existing prioritized experience replay pool.This allowed the agent to learn from failure experiences and improved the convergence speed and stability of the model in the early stages.Simulation experimental results demonstrate that the proposed algorithm outperforms traditional path planning algorithms in terms of real-time performance and shorter planned paths.It also surpasses the standard DQN algorithm in terms of convergence speed and stability.