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  Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks

Rakhsha, A., Radanovic, G., Devidze, R., Zhu, X., & Singla, A. (2021). Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks. Journal of Machine Learning Research, 22: 210, pp. 1-45.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0009-F7AA-1 Version Permalink: https://hdl.handle.net/21.11116/0000-000E-7F4B-1
Genre: Journal Article

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©2021 Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, and Adish Singla. License: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are provided at http://jmlr.org/papers/v22/20-1329.html.
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 Creators:
Rakhsha, Amin1, Author           
Radanovic, Goran2, Author           
Devidze, Rati1, Author           
Zhu, Xiaojin3, Author
Singla, Adish1, Author                 
Affiliations:
1Group A. Singla, Max Planck Institute for Software Systems, Max Planck Society, ou_2541698              
2Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society, ou_2105291              
3External Organizations, ou_persistent22              

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Language(s): eng - English
 Dates: 2021
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Raksha2021
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Title: Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Brookline, MA : Microtome Publishing
Pages: - Volume / Issue: 22 Sequence Number: 210 Start / End Page: 1 - 45 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020