arXiv Machine Learning

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).

arXiv Machine Learning

5d ago

Multi-AUV Ad-hoc network-based Target Tracking: A Value Gradient Guidance Multi-Agent Diffusion Reinforcement Learning Approach

arXiv:2608. 12436v1 Announce Type: new Abstract: Multi-AUV ad-hoc network-based target tracking requires networked autonomous underwater vehicles (AUVs) to cooperatively track maneuvering targets under constrained acoustic communication, dynamic topology, and uncertain ocean disturbances.

By Jiaao Ma, Chuan Lin, Guangjie Han, Shengchao Zhu, Qian Zhu, Ying Liu, Zhenyu Wang

agents diffusion 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 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 AI

5d ago

OGR-MARL: Option-Guided Residual Multi-Agent Reinforcement Learning for Heterogeneous USV Cooperative Pursuit in Constrained Port Waterways

arXiv:2608. 12995v1 Announce Type: new Abstract: Heterogeneous USV cooperative pursuit in constrained port waterways requires evader interception under navigation, traffic, and role constraints.

By Mao Jiayang, Wang Lanfeng, Peng Zhao-Han

agents reinforcement-learning

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 AI

Jun 11

Diffusing to Coordinate: Efficient Online Multi-Agent Diffusion Policies

arXiv:2602. 18291v2 Announce Type: replace Abstract: Online Multi-Agent Reinforcement Learning (MARL) is a prominent framework for efficient agent coordination.

By Zhuoran Li, Hai Zhong, Xun Wang, Qingxin Xia, Lihua Zhang, Longbo Huang

agents diffusion reinforcement-learning multimodal benchmarks

arXiv AI

Aug 11

Critic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Grids

arXiv:2603. 28385v2 Announce Type: replace-cross Abstract: Maritime surveillance missions, such as search and rescue and environmental monitoring, rely on the efficient allocation of sensing assets over vast and geometrically complex areas.

By Carlos S. Sep\'ulveda, Gonzalo A. Ruz

llms reinforcement-learning

arXiv Machine Learning

Jul 3

Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning

arXiv:2607. 02037v1 Announce Type: cross Abstract: Autonomous surface vehicles vary widely in hydrodynamic and actuation characteristics, yet most controllers are designed for single-platform deployment.

By Ruiheng Jiang, Thomas Bi, Raffaello D'Andrea, Aswin Ramachandran

agents reinforcement-learning fine-tuning

arXiv AI

Aug 3

RAPiD: Reward-Guided Consistency Distillation of Diffusion Planners for Real-Time Autonomous Driving

arXiv:2602. 07339v2 Announce Type: replace Abstract: Diffusion-based trajectory planners can model multi-modal driving behavior, but their iterative denoising process introduces a latency bottleneck for real-time closed-loop deployment.

By Ruturaj Reddy, Hrishav Bakul Barua, Junn Yong Loo, Thanh Thi Nguyen, Ganesh Krishnasamy

agents diffusion reinforcement-learning robotics efficiency multimodal safety

arXiv Machine Learning

Jul 20

Dichotomous Diffusion Policy Optimization

arXiv:2601. 00898v3 Announce Type: replace Abstract: Diffusion-based policies have gained growing popularity in solving a wide range of decision-making tasks due to their superior expressiveness and controllable generation during inference.

By Ruiming Liang, Yinan Zheng, Kexin Zheng, Tianyi Tan, Jianxiong Li, Liyuan Mao, Zhihao Wang, Guang Chen, Hangjun Ye, Jingjing Liu, Jinqiao Wang, Xianyuan Zhan

agents diffusion reinforcement-learning robotics multimodal benchmarks

arXiv Machine Learning

Jul 16

Task-Oriented Sensing and Covert Transmissions for Collaborative Multi-AUV Systems

arXiv:2607. 13880v1 Announce Type: new Abstract: In underwater covert cooperative missions, autonomous underwater vehicles (AUVs) often cannot rely on active sonar to continuously obtain complete information, since active sensing and frequent communications increase the risk of exposure.

By Xueyao Zhang, Chenyang Yan, Bo Yang, Xuelin Cao, Zhiwen Yu, Bin Guo, George C. Alexandropoulos, Merouane Debbah, Chau Yuen

agents reinforcement-learning

Hugging Face Trending Papers

Jul 15

Task-Oriented Sensing and Covert Transmissions for Collaborative Multi-AUV Systems

In underwater covert cooperative missions, autonomous underwater vehicles (AUVs) often cannot rely on active sonar to continuously obtain complete information, since active sensing and frequent communications increase the risk of exposure. As a result, AUVs primarily rely on passive observation, an approach that yields incomplete local perception and limited task efficiency.

agents reinforcement-learning

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