Markov Decision Process: Andrey Markov, Partially observable Markov decision process, Markov property, Optimal control theory, Tuple, Stochastic, Discrete time - Softcover

 
9786131750595: Markov Decision Process: Andrey Markov, Partially observable Markov decision process, Markov property, Optimal control theory, Tuple, Stochastic, Discrete time

Inhaltsangabe

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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Reseña del editor

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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