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{{Short description|Mathematical model for sequential decision making under uncertainty}}
'''Markov decision process''' ('''MDP'''), also called a [[Stochastic dynamic programming|stochastic dynamic program]] or stochastic control problem, is a model for [[sequential decision making]] when [[Outcome (probability)|outcomes]] are uncertain.<ref>{{Cite book |last=Puterman |first=Martin L. |title=Markov decision processes: discrete stochastic dynamic programming |date=1994 |publisher=Wiley |isbn=978-0-471-61977-2 |series=Wiley series in probability and mathematical statistics. Applied probability and statistics section |___location=New York}}</ref>
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=== Learning automata ===
{{main|Learning automata}}
Another application of MDP process in [[machine learning]] theory is called learning automata. This is also one type of reinforcement learning if the environment is stochastic. The first detail '''learning automata''' paper is surveyed by [[Kumpati S. Narendra|Narendra]] and Thathachar (1974), which were originally described explicitly as [[finite
In learning automata theory, '''a stochastic automaton''' consists of:
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