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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主要な著者:	Guoquan Chen, Zike Huang, Weijun Wang, Shenhua Yang
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2024-09-01
シリーズ:	Journal of Marine Science and Engineering
主題:	unmanned ship deep reinforcement learning dynamically adjusted entropy collision avoidance COLREGs
オンライン･アクセス:	https://www.mdpi.com/2077-1312/12/9/1562
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