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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| Natura: | Elettronico eBook |
| Lingua: | English |
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Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2010.
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| Edizione: | 1st ed. 2010. |
| Serie: | Cognitive Technologies,
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| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/978-3-642-16590-0 |
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Sommario:
- 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.