HoloOcean
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Related Articles from SNS
Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning
arXiv:2606.08513v1 Announce Type: new Abstract: Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control. This paper explores the feasibility of an end-to-end Deep Reinforcement Learning (DRL) approach that maps raw sensor data directly to thruster commands, reducing manual engineering. We propose a hierarchical reinforcement learning (HRL) architecture splitting the problem into two Markov Decision...
Contextual Multi-Task Reinforcement Learning for Autonomous Reef Monitoring
arXiv:2604.12645v2 Announce Type: replace Abstract: Although autonomous underwater vehicles promise the capability of marine ecosystem monitoring, their deployment is fundamentally limited by the difficulty of controlling vehicles under highly uncertain and non-stationary underwater dynamics. To address these challenges, we employ a data-driven reinforcement learning approach to compensate for unknown dynamics and task variations. Traditional single-task reinforcement learning has a tendency...