Multichain Markov Decision Processes
Multichain Markov Decision Processes is a class of Markov decision processes characterized by the presence of multiple recurrent classes under deterministic stationary policies, where the transition structure allows for several closed communicating sets of states and transient states, distinguishing it from unichain processes by enabling non-uniform ergodic behavior across policies.
22
Publications
987
Citations
29
Authors
26
Institutions
Publications per year
2017–2026
1
Variants
Multichain Mdps
Parents
22
Multichain Markov Renewal Programs
Eric V. Denardo, Bennett L. Fox · SIAM Journal on Applied Mathematics · 1968 · 156 citations
1–5 of 22
29
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Rutgers, The State University of New Jersey, United States
2 publications · 53 citations
2
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1–5 of 29
26
IBM Research - Thomas J. Watson Research Center
Yorktown Heights, United States
3 h-index
3 publications
103 citations
University of Pennsylvania
Philadelphia, United States
2 h-index
2 publications
120 citations
Rutgers, The State University of New Jersey
New Brunswick, United States
2 h-index
2 publications
53 citations

University of Rochester
Rochester, United States
2 h-index
3 publications
216 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
2 h-index
3 publications
81 citations
Tsinghua University
Beijing, China
2 h-index
4 publications
94 citations
Stony Brook University
Stony Brook, United States
1 h-index
1 publications
68 citations

Chinese Academy of Sciences
Beijing, China
1 h-index
1 publications
28 citations
1–8 of 26
7 publications · 351 citations
7
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2 publications · 245 citations
2
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2 publications · 52 citations
2
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1 publications · 19 citations
1
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1 publications · 18 citations
1
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1–5