Regularizing Reinforcement Learning with State Abstraction

Akrour, R.; Veiga, F.; Peters, J; Neuman, G.

doi:10.1109/IROS.2018.8594201

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Regularizing Reinforcement Learning with State Abstraction

Akrour, R., Veiga, F., Peters, J., & Neuman, G. (2018). Regularizing Reinforcement Learning with State Abstraction. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018) (pp. 534-539). Piscataway, NJ: IEEE. doi:10.1109/IROS.2018.8594201.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0003-7E70-2 Version Permalink: https://hdl.handle.net/21.11116/0000-000C-DBED-3

Genre: Conference Paper

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Creators:
Akrour, R.¹, Author
Veiga, F.¹, Author
Peters, J^{1, 2}, Author
Neuman, G.¹, Author

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

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Free keywords: Abt. Schölkopf

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Language(s): eng - English

Dates: Published Online: 2019-01-07Date issued: 2018

Publication Status: Issued

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Identifiers: BibTex Citekey: AkrVeiPetNeu18
DOI: 10.1109/IROS.2018.8594201

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Title: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)

Place of Event: Madrid, Spain

Start-/End Date: 2018-10-01 - 2018-10-05

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Title: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)

Source Genre: Proceedings

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Publ. Info: Piscataway, NJ : IEEE

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 534 - 539 Identifier: ISBN: 978-1-5386-8094-0
ISBN: 978-1-5386-8095-7
ISBN: 978-1-5386-8093-3
ISSN: 2153-0866
ISSN: 2153-0858
DOI: 10.1109/IROS39529.2018