Relative Value Iteration
Relative Value Iteration is a dynamic programming algorithm within the class of average reward reinforcement learning methods that iteratively updates value differences relative to a reference state to solve infinite-horizon Markov decision processes under the average reward criterion.
17
Publications
662
Citations
39
Authors
22
Institutions
Publications per year
2017–2026
7
Parents
17
1–5 of 17
39
2
h-index
Universidad Autónoma Agraria Antonio Narro, Mexico
2 publications · 23 citations
2
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2
h-index
2
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2
h-index
1–5 of 39
22

Shanghai Jiao Tong University
Shanghai, China
2 h-index
6 publications
390 citations
The Ohio State University
Columbus, United States
2 h-index
3 publications
34 citations
Massachusetts Institute of Technology
Cambridge, United States
2 h-index
4 publications
666 citations

Indian Institute of Technology Bombay
Mumbai, India
2 h-index
5 publications
106 citations
University of Southern California
Los Angeles, United States
2 h-index
4 publications
52 citations
University of Arizona
Tucson, United States
2 h-index
2 publications
23 citations

Universidad Autónoma Agraria Antonio Narro
Saltillo, Mexico
2 h-index
2 publications
23 citations

James Cook University
Townsville, Australia
1 h-index
1 publications
28 citations
1–8 of 22
2 publications · 145 citations
2
h-index
JAP · 2 publications · 23 citations
2
h-index
2 publications · 254 citations
2
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Conference · 1 publications · 41 citations
1
h-index
1 publications · 26 citations
1
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1–5