Research and Design of an Autonomous Underwater Vehicle Path Planning Method Based on Deep Reinforcement Learning

This study embarks on the complex issue of path planning for intelligent robots in intricate underwater environments, probing into the application of Deep Reinforcement Learning (DRL). Facing the challenges posed by continuous action spaces, traditional methods have shown their constraints, and this research is devoted to bridging this gap. We have implemented the Deep Deterministic Policy Gradient (DDPG) algorithm, a hybrid reinforcement learning method that integrates deep learning with deterministic policy gradients. By establishing an Actor-Critic network and incorporating a target network as well as an experience replay mechanism, this study aims to refine the decision-making process of robots. Key discoveries include the efficacy of the DDPG algorithm in continuous control tasks, which significantly enhances the efficiency and precision of underwater robot path planning. Furthermore, the Off-policy characteristics of the algorithm and the strategy of experience replay substantially increase the efficiency of sample utilization, while the soft update strategy ensures stability throughout the learning trajectory. These attributes together underline the distinctive value of the DDPG algorithm, offering a robust and efficient solution to the path planning of underwater robots.