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Line 22: ==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 [[Reinforcement learning]], and can be solved with value iteration and related ~~methodshggkhb~~methods. ==Partially observable Markov decision process==

Markov model: Difference between revisions