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  Projections for Approximate Policy Iteration Algorithms

Akrour, R., Pajarinen, J., Neumann, G., & Peters, J. (2019). Projections for Approximate Policy Iteration Algorithms. In K. Chaudhuri, & R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning (pp. 181-190). PMLR. Retrieved from http://proceedings.mlr.press/v97/akrour19a.html.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0007-89F4-B Version Permalink: https://hdl.handle.net/21.11116/0000-0007-89F5-A
Genre: Conference Paper

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 Creators:
Akrour, Riad1, Author
Pajarinen, Joni1, Author
Neumann, Gerhard1, Author
Peters, Jan2, 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
 Abstract: -

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Language(s): eng - English
 Dates: 2019
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: AkrPajPetNeu19
URI: http://proceedings.mlr.press/v97/akrour19a.html
 Degree: -

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Title: 36th International Conference on Machine Learning (ICML)
Place of Event: Long Beach, CA
Start-/End Date: 2019-06-09 - 2019-06-15

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Title: Proceedings of the 36th International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Chaudhuri, K.1, Editor
Salakhutdinov, R.1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: PMLR
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 181 - 190 Identifier: URI: http://proceedings.mlr.press/v97/

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Title: Proceedings of Machine Learning Research (PMLR)
Source Genre: Series
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Affiliations:
Publ. Info: PMLR
Pages: - Volume / Issue: 97 Sequence Number: - Start / End Page: - Identifier: ISSN: 2640-3498