Titlebook: Explainable and Interpretable Reinforcement Learning for Robotics; Aaron M. Roth,Dinesh Manocha,Elham Tabassi Book 2024 The Editor(s) (if

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978-3-031-47520-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Explainable and Interpretable Reinforcement Learning for Robotics978-3-031-47518-4Series ISSN 1939-4608 Series E-ISSN 1939-4616

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Inertia

David C. Paine,Burag Yaglioglu,Joseph Berry help facilitate discussion of and research into this interdisciplinary field. We consider classification techniques used in past surveys and papers and attempt to unify terminology, describing 12 attributes that can be used to classify explainable/interpretable techniques: (i) Model-Agnostic or Mod

Hemoptysis

Chandra R. Bhat,Frank S. Koppelman or Explainable Robotics, and any previous attempts at taxonomizing these subfields. Our own system involves twelve attributes which are grouped into three types of attributes. Hard Attributes are attributes that can generally be objectively defined. Hard Attributes include (i) MAMS (is the method m

停止偿付

Katharina Manassis,Pamela Wilansky-Traynorg to some of the attributes in our classification system, summarized in Tables ., ., . and .. The methods in each category are described and discussed. The categories are Decision Tree, Single Decision Tree, Single Altered Decision Tree, Multiple or Combined Decision Trees, Saliency Maps, Post-Hoc S