2026-10-13T18:53:58Z https://www.scidb.cn/oai

10.57760/sciencedb.34348 2026-04-30T08:50:32Z

2026-04-30 Reinforcement Learning information for Autonomous Marine Data Collection doi:10.57760/sciencedb.34348 en Autonomous data collection using Unmanned Surface Vehicles is crucial for applications such as marine ranching but remains challenging in uncertain ocean environments. This study proposes the Risk-Adaptive Implicit Quantile Network, a brain-inspired framework integrating distributional reinforcement learning with dynamic risk regulation. Unmanned Surface Vehicles; Risk-aware autonomous navigation; Brain-inspired framework; Distributional reinforcement learning; Uncertain ocean environments. Prof. CAO Yong Bai Zhenxin Qin Mengnan Zhang Liang Zhao Fang Wang Jeom-Kee Paik PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank