arXiv AI July 14, 2026 By Yinuo Wang, Gavin Tao, Yuze Liu, John V. Ringwood

Average-Power-Budgeted Underwater Vehicle Control via Constrained Reinforcement Learning

arXiv:2606. 25680v2 Announce Type: replace-cross Abstract: Underwater vehicles operate from a fixed onboard energy budget that propulsion rapidly depletes, so a controller that completes its task while drawing less thruster power directly extends mission range and endurance.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

reinforcement-learning

arXiv Machine Learning

Jun 9

Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning

arXiv:2606. 08513v1 Announce Type: cross Abstract: Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control.

By Elisei Shafer, Oren Gal

agents reinforcement-learning robotics

arXiv AI

Jun 4

Contextual Multi-Task Reinforcement Learning for Autonomous Reef Monitoring

arXiv:2604. 12645v2 Announce Type: replace-cross 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.

By Melvin Laux, Yi-Ling Liu, Rina Alo, S\"oren T\"opper, Mariela De Lucas Alvarez, Frank Kirchner, Rebecca Adam

agents reinforcement-learning

arXiv Machine Learning

2d ago

CORAL: Curriculum-Optimized Reward Adaptation for LiDAR-Based Goal-Directed Urban Driving

arXiv:2608. 14332v1 Announce Type: cross Abstract: Reinforcement learning is promising for autonomous urban driving, but long-horizon goal-directed navigation asks a policy to acquire several competing behaviors at once--reaching a distant goal, tracking a route, avoiding obstacles, obeying signals--and a fixed objective gives no order in which to learn them.

By Anisa Saleem, Duksu Kim

agents reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 7

Optimality-Informed Neural Networks for Lunar Landing Trajectory Optimization

arXiv:2607. 02741v1 Announce Type: cross Abstract: This paper develops an Optimality-Informed Neural Network (OINN) approach for the energy-optimal, free-final-time powered descent of a lunar lander from any initial position, velocity, and mass within a bounded operating envelope to a fixed landing site with zero terminal velocity.

By Zhenbo Wang

arXiv AI

Jun 3

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles

arXiv:2505. 08222v3 Announce Type: replace-cross Abstract: Autonomous vehicles (AVs) offer a cost-effective solution for scientific missions such as underwater tracking.

By Matteo Gallici, Ivan Masmitja, Mario Mart\'in

llms agents reinforcement-learning robotics

arXiv Machine Learning

6d ago

Diffusion-Guided Cooperative Policy Learning for Target Tracking Based on Underwater Mobile Agent Networks

arXiv:2603. 29426v2 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning (MARL) provides a promising solution for cooperative target tracking in networks of autonomous underwater vehicles (AUVs).

By Jiaao Ma, Chuan Lin, Guangjie Han, Shengchao Zhu, Zhenyu Wang, Chen An

agents diffusion reinforcement-learning

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Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning

Contextual Multi-Task Reinforcement Learning for Autonomous Reef Monitoring

CORAL: Curriculum-Optimized Reward Adaptation for LiDAR-Based Goal-Directed Urban Driving

Optimality-Informed Neural Networks for Lunar Landing Trajectory Optimization

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles

Diffusion-Guided Cooperative Policy Learning for Target Tracking Based on Underwater Mobile Agent Networks