Local-utopia Policy Selection for Multi-objective Reinforcement Learning

Parisi, Simone; Blank, Alexander; Viernickel, Tobias; Peters, Jan

doi:10.1109/SSCI.2016.7849369

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Local-utopia Policy Selection for Multi-objective Reinforcement Learning

Parisi, S., Blank, A., Viernickel, T., & Peters, J. (2016). Local-utopia Policy Selection for Multi-objective Reinforcement Learning. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI 2016) (pp. 32-38). Piscataway, NJ: IEEE. doi:10.1109/SSCI.2016.7849369.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000A-9F72-3 Version Permalink: https://hdl.handle.net/21.11116/0000-000A-9F73-2

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Creators:
Parisi, Simone¹, Author
Blank, Alexander¹, Author
Viernickel, Tobias¹, Author
Peters, Jan^{1, 2}, 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: 2017-02-13Date issued: 2016

Publication Status: Issued

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Identifiers: DOI: 10.1109/SSCI.2016.7849369
BibTex Citekey: ParBlaViePet16

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Title: IEEE Symposium Series on Computational Intelligence (SSCI 2016)

Place of Event: Athens

Start-/End Date: 2016-12-06 - 2016-12-09

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Title: 2016 IEEE Symposium Series on Computational Intelligence (SSCI 2016)

Source Genre: Proceedings

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Publ. Info: Piscataway, NJ : IEEE

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 32 - 38 Identifier: ISBN: 978-1-5090-4240-1
ISBN: 978-1-5090-4241-8
URI: https://ieeexplore.ieee.org/xpl/conhome/7840087/proceeding