Markov model: Difference between revisions

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{{main|Markov decision process}}

A [[Markov decision process]] is a Markov chain in which state transitions depend on the current state and an action vector that is applied to the system. Typically, a Markov decision process is used to compute a policy of actions that will maximize some utility with respect to expected rewards. It is closely related to [[Reinforcementreinforcement learning]], and can be solved with [[value iteration]] and related methods.

==Partially observable Markov decision process==