Markov Decision Processes with Their Applications
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| Qiying Hu, Wuyi Yue | ||||
| Advances in Mechanics and Mathematics 14 | ||||
| Springer US | ||||
| 2008 | ||||
| English | ||||
| 305 pages | ||||
| 1.74 MB | ||||
[tab] [content title="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. [/content] [content title="Content"] [/content] [content title="About the author"]Qi-Ying Hu VP:Formulations, Omega Therapeutics Inc.Wuyi Yue received the B. Eng. degree in Electronic Engineering from Tsinghua University, China and the M. Eng. and Dr. Eng. degrees in Applied Mathematics and Physics from Kyoto University, Japan. She was a Researcher and a Chief Researcher of ASTEM RI, an Associate Professor of Wakayama University, an Associate Professor and a Professor at the Department of Applied Mathematics, a Professor at Department of Information Science and Systems Engineering, Konan University, Japan. [/content] [/tab]
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