Average Reward Linear Programming
Average Reward Linear Programming is a mathematical optimization framework within the domain of Markov decision processes that characterizes the long-run expected reward per unit time through linear programming formulations, distinguishing itself by enabling the computation of optimal stationary policies under ergodicity assumptions.
17
Publications
727
Citations
34
Authors
30
Institutions
Publications per year
2017–2026
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Stony Brook University
Stony Brook, United States
2 h-index
2 publications
118 citations
Leiden University
Leiden, The Netherlands
2 h-index
2 publications
88 citations
University of California, Berkeley
Berkeley, United States
1 h-index
2 publications
72 citations
City College of New York
New York, United States
1 h-index
2 publications
60 citations
City University of New York
New York, United States
1 h-index
2 publications
60 citations
University of Southern California
Los Angeles, United States
1 h-index
2 publications
56 citations

Indian Institute of Technology Bombay
Mumbai, India
1 h-index
2 publications
34 citations

Universidad Autónoma Agraria Antonio Narro
Saltillo, Mexico
1 h-index
1 publications
22 citations
1–8 of 30
2 publications · 54 citations
2
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2 publications · 146 citations
2
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2 publications · 89 citations
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OJVT · 1 publications · 72 citations
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Repository · 1 publications · 98 citations
1
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