Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization

Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization

Deep neural networks (DNNs) can be useful within the marine robotics field, but their utility value is restricted by their black-box nature. Explainable artificial intelligence methods attempt to understand how such black-boxes make their decisions. In this work, linear model trees (LMTs) are used t...

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Autores principales: Vilde B. Gjærum, Inga Strümke, Ole Andreas Alsos, Anastasios M. Lekkas
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
deep reinforcement learning
autonomous surface vessel
explainable artificial intelligence
linear model trees
Naval architecture. Shipbuilding. Marine engineering
VM1-989
Oceanography
GC1-1581
Acceso en línea:https://doaj.org/article/1cef3c3316ff413abd15bb5f091ca66f
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https://doaj.org/article/1cef3c3316ff413abd15bb5f091ca66f

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