Explainable interpretable reinforcement learning par roth aaron (15 résultats)
Explainable and Interpretable Reinforcement Learning for Robotics (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Roth, Aaron M.; Manocha, Dinesh; Sriram, Ram D.; Tabassi, Elham
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Explainable and Interpretable Reinforcement Learning for Robotics (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Roth, Aaron M.; Manocha, Dinesh; Sriram, Ram D.; Tabassi, Elham
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Vendeur : Books Puddle, New York, NY, Etats-UnisBooks Puddle
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Etat : New. 2024th edition NO-PA16APR2015-KAP.
Langue : anglais
Edité par Springer International Publishing, Springer Nature Switzerland, 2025
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Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded… the real-world adoption of RL algorithms for robotics and related areas. These include difficulties in preventing safety constraints from being violated and the issues faced by systems operators who desire explainable policies and actions. Robotics applications present a unique set of considerations and result in a number of opportunities related to their physical, real-world sensory input and interactions.The authors consider classification techniques used in past surveys and papers and attempt to unify terminology across the field. The book provides an in-depth exploration of 12 attributes that can be used to classify explainable/interpretable techniques. These include whether the RL method is model-agnostic or model-specific, self-explainable or post-hoc, as well as additional analysis of the attributes of scope, when-produced, format, knowledge limits, explanation accuracy, audience, predictability, legibility, readability, and reactivity. The book is organized around a discussion of these methods broken down into 42 categories and subcategories, where each category can be classified according to some of the attributes. The authors close by identifying gaps in the current research and highlighting areas for future investigation.
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Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded the re…al-world adoption of RL algorithms for robotics and related areas. These include difficulties in preventing safety constraints from being violated and the issues faced by systems operators who desire explainable policies and actions. Robotics applications present a unique set of considerations and result in a number of opportunities related to their physical, real-world sensory input and interactions.The authors consider classification techniques used in past surveys and papers and attempt to unify terminology across the field. The book provides an in-depth exploration of 12 attributes that can be used to classify explainable/interpretable techniques. These include whether the RL method is model-agnostic or model-specific, self-explainable or post-hoc, as well as additional analysis of the attributes of scope, when-produced, format, knowledge limits, explanation accuracy, audience, predictability, legibility, readability, and reactivity. The book is organized around a discussion of these methods broken down into 42 categories and subcategories, where each category can be classified according to some of the attributes. The authors close by identifying gaps in the current research and highlighting areas for future investigation.
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Taschenbuch. Etat : Neu. Explainable and Interpretable Reinforcement Learning for Robotics | Aaron M. Roth (u. a.) | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xv | Englisch | 2025 | Springer | EAN 9783031475207 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17,…69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Langue : anglais
Edité par Springer International Publishing, Springer Nature Switzerland Mär 2025, 2025
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AllemagneBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challen…ges have impeded the real-world adoption of RL algorithms for robotics and related areas. These include difficulties in preventing safety constraints from being violated and the issues faced by systems operators who desire explainable policies and actions. Robotics applications present a unique set of considerations and result in a number of opportunities related to their physical, real-world sensory input and interactions.The authors consider classification techniques used in past surveys and papers and attempt to unify terminology across the field. The book provides an in-depth exploration of 12 attributes that can be used to classify explainable/interpretable techniques. These include whether the RL method is model-agnostic or model-specific, self-explainable or post-hoc, as well as additional analysis of the attributes of scope, when-produced, format, knowledge limits, explanation accuracy, audience, predictability, legibility, readability, and reactivity. The book is organized around a discussion of these methods broken down into 42 categories and subcategories, where each category can be classified according to some of the attributes. The authors close by identifying gaps in the current research and highlighting areas for future investigation. 132 pp. Englisch.
Langue : anglais
Edité par Springer International Publishing Apr 2024, 2024
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AllemagneBuchWeltWeit Ludwig Meier e.K.
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EUR 23,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 2 disponible(s)
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges hav…e impeded the real-world adoption of RL algorithms for robotics and related areas. These include difficulties in preventing safety constraints from being violated and the issues faced by systems operators who desire explainable policies and actions. Robotics applications present a unique set of considerations and result in a number of opportunities related to their physical, real-world sensory input and interactions.The authors consider classification techniques used in past surveys and papers and attempt to unify terminology across the field. The book provides an in-depth exploration of 12 attributes that can be used to classify explainable/interpretable techniques. These include whether the RL method is model-agnostic or model-specific, self-explainable or post-hoc, as well as additional analysis of the attributes of scope, when-produced, format, knowledge limits, explanation accuracy, audience, predictability, legibility, readability, and reactivity. The book is organized around a discussion of these methods broken down into 42 categories and subcategories, where each category can be classified according to some of the attributes. The authors close by identifying gaps in the current research and highlighting areas for future investigation. 132 pp. Englisch.
Explainable and Interpretable Reinforcement Learning for Robotics (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Roth, Aaron M.; Manocha, Dinesh; Sriram, Ram D.; Tabassi, Elham
- Couverture rigide
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Vendeur : Majestic Books, Hounslow, Royaume-UniMajestic Books
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Etat : New. Print on Demand.
Explainable and Interpretable Reinforcement Learning for Robotics (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Roth, Aaron M.; Manocha, Dinesh; Sriram, Ram D.; Tabassi, Elham
- Couverture rigide
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Vendeur : Biblios, frankfurt am main, HESSE, AllemagneBiblios
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides readers with a categorization system to discuss explainable and interpretable RL techniquesExplores RL methodology specific to robotics applicationsExplains how interpretable RL algorithms can enhance trust, incr…ease adoption, redu.
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagnebuchversandmimpf2000
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges…have impeded the real-world adoption of RL algorithms for robotics and related areas. These include difficulties in preventing safety constraints from being violated and the issues faced by systems operators who desire explainable policies and actions. Robotics applications present a unique set of considerations and result in a number of opportunities related to their physical, real-world sensory input and interactions.The authors consider classification techniques used in past surveys and papers and attempt to unify terminology across the field. The book provides an in-depth exploration of 12 attributes that can be used to classify explainable/interpretable techniques. These include whether the RL method is model-agnostic or model-specific, self-explainable or post-hoc, as well as additional analysis of the attributes of scope, when-produced, format, knowledge limits, explanation accuracy, audience, predictability, legibility, readability, and reactivity. The book is organized around a discussion of these methods broken down into 42 categories and subcategories, where each category can be classified according to some of the attributes. The authors close by identifying gaps in the current research and highlighting areas for future investigation.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 132 pp. Englisch.
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EUR 60,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 1 disponible(s)
Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have im…peded the real-world adoption of RL algorithms for robotics and related areas. These include difficulties in preventing safety constraints from being violated and the issues faced by systems operators who desire explainable policies and actions. Robotics applications present a unique set of considerations and result in a number of opportunities related to their physical, real-world sensory input and interactions.The authors consider classification techniques used in past surveys and papers and attempt to unify terminology across the field. The book provides an in-depth exploration of 12 attributes that can be used to classify explainable/interpretable techniques. These include whether the RL method is model-agnostic or model-specific, self-explainable or post-hoc, as well as additional analysis of the attributes of scope, when-produced, format, knowledge limits, explanation accuracy, audience, predictability, legibility, readability, and reactivity. The book is organized around a discussion of these methods broken down into 42 categories and subcategories, where each category can be classified according to some of the attributes. The authors close by identifying gaps in the current research and highlighting areas for future investigation.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 132 pp. Englisch.
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Buch. Etat : Neu. Explainable and Interpretable Reinforcement Learning for Robotics | Aaron M. Roth (u. a.) | Buch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xv | Englisch | 2024 | Springer | EAN 9783031475177 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelbe…rg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
