MDPs are useful for studying a wide range of optimization problems solved via dynamic programming and reinforcement learning. High Quality Content by WIKIPEDIA articles! 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 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.
Markov Decision Process
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Книга по Требованию
Should be press liable or not
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