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Hierarchical Relative Entropy Policy Search - Daniel, Christian / Neumann, Gerhard
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Daniel, Christian / Neumann, Gerhard:

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[EAN: 9783639475999], Neubuch, Publisher/Verlag: AV Akademikerverlag | An Information Theoretic Learning Algorithm in Multimodal Solution Spaces for Real Robots | Many real-world problems are inherently hierarchically structured. The use of this structure in an agent's policy may well be the key to improved scalability and higher performance on motor skill tasks. However, such hierarchical structures cannot be exploited by current policy search algorithms. We concentrate on a basic, but highly relevant hierarchy - the `mixed option' policy. Here, a gating network first decides which of the options to execute and, subsequently, the option-policy determines the action. Using a hierarchical setup for our learning method allows us to learn not only one solution to a problem but many. We base our algorithm on a recently proposed information theoretic policy search method, which addresses the exploitation-exploration trade-off by limiting the loss of information between policy updates. | Format: Paperback | Language/Sprache: english | 68 pp, [PU: VDM Verlag Dr. Müller, Saarbrücken]

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Hierarchical Relative Entropy Policy Search - Christian Daniel#Gerhard Neumann
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Christian Daniel#Gerhard Neumann:

Hierarchical Relative Entropy Policy Search - neues Buch

2015, ISBN: 9783639475999

ID: 613837706

Many real-world problems are inherently hierarchically structured. The use of this structure in an agent´s policy may well be the key to improved scalability and higher performance on motor skill tasks. However, such hierarchical structures cannot be exploited by current policy search algorithms. We concentrate on a basic, but highly relevant hierarchy - the `mixed option´ policy. Here, a gating network first decides which of the options to execute and, subsequently, the option-policy determines the action. Using a hierarchical setup for our learning method allows us to learn not only one solution to a problem but many. We base our algorithm on a recently proposed information theoretic policy search method, which addresses the exploitation-exploration trade-off by limiting the loss of information between policy updates. An Information Theoretic Learning Algorithm in Multimodal Solution Spaces for Real Robots Bücher > Fremdsprachige Bücher > Englische Bücher Taschenbuch 30.07.2015 Buch (fremdspr.), AV Akademikerverlag, .201

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Hierarchical Relative Entropy Policy Search - Christian Daniel#Gerhard Neumann
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Hierarchical Relative Entropy Policy Search - neues Buch

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An Information Theoretic Learning Algorithm in Multimodal Solution Spaces for Real Robots Many real-world problems are inherently hierarchically structured. The use of this structure in an agent's policy may well be the key to improved scalability and higher performance on motor skill tasks. However, such hierarchical structures cannot be exploited by current policy search algorithms. We concentrate on a basic, but highly relevant hierarchy - the `mixed option' policy. Here, a gating network first decides which of the options to execute and, subsequently, the option-policy determines the action. Using a hierarchical setup for our learning method allows us to learn not only one solution to a problem but many. We base our algorithm on a recently proposed information theoretic policy search method, which addresses the exploitation-exploration trade-off by limiting the loss of information between policy updates. Bücher / Fremdsprachige Bücher / Englische Bücher 978-3-639-47599-9, AV Akademikerverlag

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Hierarchical Relative Entropy Policy Search - Christian Daniel#Gerhard Neumann
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Christian Daniel#Gerhard Neumann:
Hierarchical Relative Entropy Policy Search - neues Buch

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ID: 743664935

Many real-world problems are inherently hierarchically structured. The use of this structure in an agent´s policy may well be the key to improved scalability and higher performance on motor skill tasks. However, such hierarchical structures cannot be exploited by current policy search algorithms. We concentrate on a basic, but highly relevant hierarchy - the `mixed option´ policy. Here, a gating network first decides which of the options to execute and, subsequently, the option-policy determines the action. Using a hierarchical setup for our learning method allows us to learn not only one solution to a problem but many. We base our algorithm on a recently proposed information theoretic policy search method, which addresses the exploitation-exploration trade-off by limiting the loss of information between policy updates. An Information Theoretic Learning Algorithm in Multimodal Solution Spaces for Real Robots Buch (fremdspr.) Bücher>Fremdsprachige Bücher>Englische Bücher, AV Akademikerverlag

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Hierarchical Relative Entropy Policy Search - Christian Daniel
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Christian Daniel:
Hierarchical Relative Entropy Policy Search - Taschenbuch

2015, ISBN: 3639475992

ID: 11878815233

[EAN: 9783639475999], Neubuch, [PU: AV Akademikerverlag Jul 2015], This item is printed on demand - Print on Demand Neuware - Many real-world problems are inherently hierarchically structured. The use of this structure in an agent's policy may well be the key to improved scalability and higher performance on motor skill tasks. However, such hierarchical structures cannot be exploited by current policy search algorithms. We concentrate on a basic, but highly relevant hierarchy - the `mixed option' policy. Here, a gating network first decides which of the options to execute and, subsequently, the option-policy determines the action. Using a hierarchical setup for our learning method allows us to learn not only one solution to a problem but many. We base our algorithm on a recently proposed information theoretic policy search method, which addresses the exploitation-exploration trade-off by limiting the loss of information between policy updates. 68 pp. Englisch

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Hierarchical Relative Entropy Policy Search: An Information Theoretic Learning Algorithm in Multimodal Solution Spaces for Real Robots
Autor:

Christian Daniel, Gerhard Neumann

Titel:

Hierarchical Relative Entropy Policy Search: An Information Theoretic Learning Algorithm in Multimodal Solution Spaces for Real Robots

ISBN-Nummer:

9783639475999

Detailangaben zum Buch - Hierarchical Relative Entropy Policy Search: An Information Theoretic Learning Algorithm in Multimodal Solution Spaces for Real Robots


EAN (ISBN-13): 9783639475999
ISBN (ISBN-10): 3639475992
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2014
Herausgeber: Av Akademikerverlag

Buch in der Datenbank seit 27.07.2014 21:28:31
Buch zuletzt gefunden am 21.12.2016 13:16:36
ISBN/EAN: 9783639475999

ISBN - alternative Schreibweisen:
3-639-47599-2, 978-3-639-47599-9

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