Markov Decision Processes with Their Applications
Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time MDPs,...
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oai:112.137.131.14:VNU_123-265232020-06-29T09:17:11Z Markov Decision Processes with Their Applications Hu, Qiying Yue, Wuyi Mathematics and Statistics ; Markov processes; Statistical decision 519.233 Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practical problems. These models include partially observable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple objectives, constraints or imprecise parameters. 2017-04-12T02:23:20Z 2017-04-12T02:23:20Z 2008 Book 9780387369501 http://repository.vnu.edu.vn/handle/VNU_123/26523 en 305 p. application/pdf Springer |
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Vietnam National University, Hanoi |
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VNU Library & Information Center |
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Vietnam |
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VNU Digital Repository |
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English |
| topic |
Mathematics and Statistics ; Markov processes; Statistical decision 519.233 |
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Mathematics and Statistics ; Markov processes; Statistical decision 519.233 Hu, Qiying Yue, Wuyi Markov Decision Processes with Their Applications |
| description |
Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practical problems. These models include partially observable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple objectives, constraints or imprecise parameters. |
| format |
Book |
| author |
Hu, Qiying Yue, Wuyi |
| author_facet |
Hu, Qiying Yue, Wuyi |
| author_sort |
Hu, Qiying |
| title |
Markov Decision Processes with Their Applications |
| title_short |
Markov Decision Processes with Their Applications |
| title_full |
Markov Decision Processes with Their Applications |
| title_fullStr |
Markov Decision Processes with Their Applications |
| title_full_unstemmed |
Markov Decision Processes with Their Applications |
| title_sort |
markov decision processes with their applications |
| publisher |
Springer |
| publishDate |
2017 |
| url |
http://repository.vnu.edu.vn/handle/VNU_123/26523 |
| _version_ |
1680965790104289280 |