{"total":10,"rows":[{"id":"2103.14629","title":"Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution","created":"2021-03-26","cited_by_count":14,"area":null},{"id":"2112.00115","title":"Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning","created":"2021-11-30","cited_by_count":8,"area":null},{"id":"2203.00368","title":"Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization","created":"2022-03-01","cited_by_count":3,"area":null},{"id":"2307.16769","title":"2-Level Reinforcement Learning for Ships on Inland Waterways: Path Planning and Following","created":"2023-07-25","cited_by_count":2,"area":null},{"id":"2307.01917","title":"Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers","created":"2023-07-04","cited_by_count":1,"area":null},{"id":"2312.01855","title":"Modular Control Architecture for Safe Marine Navigation: Reinforcement Learning and Predictive Safety Filters","created":"2023-12-04","cited_by_count":1,"area":null},{"id":"2203.00369","title":"Approximating a deep reinforcement learning docking agent using linear model trees","created":"2022-03-01","cited_by_count":0,"area":null},{"id":"2307.06688","title":"Aeolus Ocean -- A simulation environment for the autonomous COLREG-compliant navigation of Unmanned Surface Vehicles using Deep Reinforcement Learning and Maritime Object Detection","created":"2023-07-13","cited_by_count":0,"area":null},{"id":"2603.04057","title":"Sim2Sea: Sim-to-Real Policy Transfer for Maritime Vessel Navigation in Congested Waters","created":"2026-03-04","cited_by_count":0,"area":null},{"id":"2609.12938","title":"A Large-Scale AIS Dataset from Finnish Water","created":"2026-09-11","cited_by_count":0,"area":null}],"next_cursor":null}