Batch Policy Optimization
Batch Policy Optimization is a reinforcement learning method that improves decision-making policies by optimizing over a fixed dataset of previously collected interactions, distinguishing itself from online approaches through its reliance on offline, batch-processed experience rather than real-time environment engagement.
7
Publications
510
Citations
24
Authors
13
Institutions
Publications per year
2017–2026
2
Variants
Offline Policy Optimization
Parents
7
Bi-criteria algorithm for scheduling jobs on cluster platforms
Pierre-François Dutot, Lionel Eyraud, Grégory Mounié et al. · 2004 · 38 citations
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National Yang Ming Chiao Tung University
Hsinchu, Taiwan
1 h-index
4 publications
72 citations
Stanford University
Stanford, United States
1 h-index
1 publications
12 citations
Tongji University
Shanghai, China
1 h-index
2 publications
30 citations
Columbia University
New York, United States
1 h-index
1 publications
401 citations
Google (United States)
Mountain View, United States
1 h-index
1 publications
12 citations

University of Alberta
Edmonton, Canada
1 h-index
1 publications
12 citations
The University of Texas at Austin
Austin, United States
1 h-index
1 publications
12 citations

University of Manchester
Manchester, United Kingdom
1 h-index
1 publications
12 citations
1–8 of 13
1 publications · 18 citations
1
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1 publications · 401 citations
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1 publications · 15 citations
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Conference · 1 publications · 12 citations
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Repository · 1 publications · 10 citations
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