Markov Decision Process: Andrey Markov, Partially observable Markov decision process, Markov property, Optimal control theory, Tuple, Stochastic, Discrete time - Softcover
Synopsis
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Markov decision processes (MDPs), named after Andrey Markov, provide a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying a wide range of optimization problems solved via dynamic programming and reinforcement learning. MDPs were known at least as early as the 1950s (cf. Bellman 1957). Much research in the area was spawned due to Ronald A. Howard''s book, Dynamic Programming and Markov Processes, in 1960. Today they are used in a variety of areas, including robotics, automated control, economics and manufacturing.
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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Markov decision processes (MDPs), named after Andrey Markov, provide a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying a wide range of optimization problems solved via dynamic programming and reinforcement learning. MDPs were known at least as early as the 1950s (cf. Bellman 1957). Much research in the area was spawned due to Ronald A. Howard''s book, Dynamic Programming and Markov Processes, in 1960. Today they are used in a variety of areas, including robotics, automated control, economics and manufacturing.
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Markov Decision Process
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Markov Decision Process
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Please note that the content of this book primarily consists of articlesavailable from Wikipedia or other free sources online. Markov decisionprocesses (MDPs), named after Andrey Markov, provide a mathematicalframework for modeling decision-making in situations where outcomes arepartly random and partly under the control of a decision maker. MDPs areuseful for studying a wide range of optimization problems solved viadynamic programming and reinforcement learning. MDPs were known at leastas early as the 1950s (cf. Bellman 1957). Much research in the area wasspawned due to Ronald A. Howard's book, Dynamic Programming and MarkovProcesses, in 1960. Today they are used in a variety of areas, includingrobotics, automated control, economics and manufacturing.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Englisch. Seller Inventory # 9786131750595
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Markov Decision Process : Andrey Markov, Partially observable Markov decision process, Markov property, Optimal control theory, Tuple, Stochastic, Discrete time
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