Qualitative spatial abstraction in reinforcement learning
- Responsibility
- Lutz Frommberger.
- Language
- English. English.
- Digital
- text file; PDF
- Imprint
- Heidelberg ; New York : Springer-Verlag, 2010.
- Physical description
- 1 online resource (xvii, 174 pages) : illustrations
- Series
- Cognitive technologies.
Online
Available online
More options
Description
Creators/Contributors
- Author/Creator
- Frommberger, Lutz.
Contents/Summary
- Bibliography
- Includes bibliographical references (pages 161-170) and index.
- Contents
-
- Foundations of Reinforcement Learning
- Abstraction and Knowledge Transfer in Reinforcement Learning
- Qualitative State Space Abstraction
- Generalization and Transfer Learning with Qualitative Spatial Abstraction
- RLPR
- An Aspectualizable State Space Representation
- Empirical Evaluation
- Summary and Outlook.
- Summary
- Reinforcement learning has developed as a successful learning approach for domains that are not fully understood and that are too complex to be described in closed form. However, reinforcement learning does not scale well to large and continuous problems. Furthermore, acquired knowledge specific to the learned task, and transfer of knowledge to new tasks is crucial. In this book, the author investigates whether deficiencies of reinforcement learning can be overcome by suitable abstraction methods. He discusses various forms of spatial abstraction, in particular qualitative abstraction, a form.
Subjects
Bibliographic information
- Publication date
- 2010
- Series
- Cognitive technologies
- ISBN
- 9783642165900 (electronic bk.)
- 3642165907 (electronic bk.)
- 9783642165894
- 3642165893
- DOI
- 10.1007/978-3-642-16590-0
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