Qualitative Spatial Abstraction in Reinforcement Learning
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...
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| מחבר תאגידי: | |
| פורמט: | אלקטרוני ספר אלקטרוני |
| שפה: | English |
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Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2010.
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| מהדורה: | 1st ed. 2010. |
| סדרה: | Cognitive Technologies,
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| גישה מקוונת: | https://doi.org/10.1007/978-3-642-16590-0 |
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תוכן הענינים:
- 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.