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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| 企业作者: | |
| 格式: | 电子 电子书 |
| 语言: | English |
| 出版: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2010.
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| 版: | 1st ed. 2010. |
| 丛编: | Cognitive Technologies,
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| 主题: | |
| 在线阅读: | https://doi.org/10.1007/978-3-642-16590-0 |
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书本目录:
- 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.