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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| Tác giả chính: | |
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| Tác giả của công ty: | |
| Định dạng: | Điện tử eBook |
| Ngôn ngữ: | English |
| Được phát hành: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2010.
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| Phiên bản: | 1st ed. 2010. |
| Loạt: | Cognitive Technologies,
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| Những chủ đề: | |
| Truy cập trực tuyến: | https://doi.org/10.1007/978-3-642-16590-0 |
| Các nhãn: |
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Mục lục:
- 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.