A Novel Dynamically Adjusted Entropy Algorithm for Collision Avoidance in Autonomous Ships Based on Deep Reinforcement Learning

Decision-making for collision avoidance in complex maritime environments is a critical technology in the field of autonomous ship navigation. However, existing collision avoidance decision algorithms still suffer from unstable strategy exploration and poor compliance with regulations. To address the...

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Autores principales:	Guoquan Chen, Zike Huang, Weijun Wang, Shenhua Yang
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	MDPI AG 2024-09-01
Colección:	Journal of Marine Science and Engineering
Materias:	unmanned ship deep reinforcement learning dynamically adjusted entropy collision avoidance COLREGs
Acceso en línea:	https://www.mdpi.com/2077-1312/12/9/1562
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