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- W127620117 abstract "This chapter discusses Markov decision problem and provides an overview of the Markov decision model. A system is observed at equally-spaced epochs numbered 0, 1, 2,… so on. At each epoch, the system is observed to occupy one of N states that are numbered 1 through N. Each state i has associated with it a finite nonempty decision set D1. Whenever state i is observed, some decision k in D1 must be selected. This model is stationary in that nothing depends on n. The chapter discusses the model under several performance criteria, each of which involves the limiting behavior of the income earned at the first n epochs, as n approaches infinity. Throughout in the model, it is assumed that each transition matrix Pδ has one ergodic chain that must be acyclic. Transient states are allowed and they can vary with the policy. The chapter discusses stationary policies, the income generating characteristics of a particular policy, and the question of computing an optimal stationary policy. It discusses nonstationary policies and certain operating characteristics of the optimal (nonstationary) policy for a very long planning horizon. In the discounted model, a stationary non-randomized policy comes arbitrarily close to being optimal for the finite-horizon problem, provided the horizon is long enough." @default.
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- W127620117 date "1973-01-01" @default.
- W127620117 modified "2023-10-18" @default.
- W127620117 title "A Markov Decision Problem" @default.
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- W127620117 doi "https://doi.org/10.1016/b978-0-12-358350-5.50005-1" @default.
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