标题： 海洋探测者：一种用于水下任务的AUV鲁棒控制的扩散增强强化学习 显示英文标题

标题： Ocean Diviner: A Diffusion-Augmented Reinforcement Learning for AUV Robust Control in the Underwater Tasks

摘要： 本文提出了一种扩散增强的强化学习（RL）方法，用于鲁棒的自主水下航行器（AUV）控制，解决了水下轨迹规划和动态环境适应中的关键挑战。 所提出的方法集成了三个核心创新：（1）一种基于扩散的轨迹生成框架，能够生成物理可行的多步骤轨迹，通过一种新颖的扩散U-Net架构将当前观测与历史状态和动作结合，实现高维状态编码机制，显著提高了长时程规划的效果。 （2）一种样本高效的混合学习架构，将扩散引导的探索与RL策略优化相结合，其中扩散模型生成多样化的候选动作，而RL评论家选择最优动作，在动态水下环境中实现了更高的探索效率和策略稳定性。 大量的仿真实验验证了该方法的优越鲁棒性和灵活性，在复杂的海洋条件下优于传统控制方法，为水下任务中的AUV操作提供了增强的适应性和可靠性。 显示英文摘要

摘要： This paper presents a diffusion-augmented reinforcement learning (RL) approach for robust autonomous underwater vehicle (AUV) control, addressing key challenges in underwater trajectory planning and dynamic environment adaptation. The proposed method integrates three core innovations: (1) A diffusion-based trajectory generation framework that produces physically feasible multi-step trajectories, enhanced by a high-dimensional state encoding mechanism combining current observations with historical states and actions through a novel diffusion U-Net architecture, significantly improving long-horizon planning. (2) A sample-efficient hybrid learning architecture that synergizes diffusion-guided exploration with RL policy optimization, where the diffusion model generates diverse candidate actions and the RL critic selects optimal actions, achieving higher exploration efficiency and policy stability in dynamic underwater environments. Extensive simulation experiments validating the method's superior robustness and flexibility, outperforms conventional control methods in challenging marine conditions, offering enhanced adaptability and reliability for AUV operations in the underwater tasks.