Probabilistic Inference for Determining Options in Reinforcement Learning

Daniel, C.; van Hoof, H.; Peters, J; Neumann, G.

doi:10.1007/s10994-016-5580-x

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Probabilistic Inference for Determining Options in Reinforcement Learning

Daniel, C., van Hoof, H., Peters, J., & Neumann, G. (2016). Probabilistic Inference for Determining Options in Reinforcement Learning. Machine Learning, Special Issue, 104(2), 337-357. doi:10.1007/s10994-016-5580-x.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-002D-19A7-8 Version Permalink: https://hdl.handle.net/21.11116/0000-0004-74FE-C

Genre: Journal Article

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Creators:
Daniel, C.¹, Author
van Hoof, H.¹, Author
Peters, J^{2, 3}, Author
Neumann, G.¹, Author

Affiliations:
1External Organizations, ou_persistent22
2Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647
3Dept. Autonomous Motion, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497646

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Free keywords: Abt. Schaal; Abt. Schölkopf

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Language(s): eng - English

Dates: Published Online: 2016-09

Publication Status: Published online

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Identifiers: BibTex Citekey: DanHooPetNeu16
DOI: 10.1007/s10994-016-5580-x

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Title: Machine Learning, Special Issue

Source Genre: Journal

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Publ. Info: Springer Link

Pages: - Volume / Issue: 104 (2) Sequence Number: - Start / End Page: 337 - 357 Identifier: ISSN: 0885-6125
ISSN: 1573-0565