Models for Data-Efficient Reinforcement Learning on Real-World Applications

Dörr, Andreas

doi:10.18419/opus-11908

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Models for Data-Efficient Reinforcement Learning on Real-World Applications

Dörr, A. (2021). Models for Data-Efficient Reinforcement Learning on Real-World Applications. PhD Thesis, Universität Stuttgart, Stuttgart.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0009-D4CC-2 Version Permalink: https://hdl.handle.net/21.11116/0000-0009-D4CD-1

Genre: Thesis

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Creators:
Dörr, Andreas^{1, 2}, Author

Affiliations:
1Max Planck Research Group Intelligent Control Systems, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_2579691
2Institut für Parallele und Verteilte Systeme, Universität Stuttgart, Stuttgart, ou_persistent22

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Free keywords: Forschungsgruppe Trimpe

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Language(s): eng - English

Dates: Accepted: 2021-10-07

Publication Status: Accepted / In Press

Pages: VI, 147

Publishing info: Stuttgart : Universität Stuttgart

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Identifiers: BibTex Citekey: doerr2021data-efficient-RL
DOI: 10.18419/opus-11908
URN: urn:nbn:de:bsz:93-opus-ds-119257
URI: http://elib.uni-stuttgart.de/handle/11682/11925

Degree: PhD

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