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- W1546722720 abstract "AbstractIn this chapter we indicate that when it is scaled properly, an absorbing Markov decision process (MDP) model with an expected total undiscounted cost can be approximated by two of its deterministic and continuous analogues, namely, the standard fluid model and the refined fluid model, at least when the scaling parameter (n = 1,2,ldots ) grows large. We obtain the level of accuracy of such fluid approximations by showing that the absolute difference between the objective function of the scaled MDP model and the one of the (standard and refined) fluid model goes to zero as fast as (frac{1} {n}.) Under some extra conditions, we obtain that given a particular type of policy solving the refined fluid model, it can be translated into a policy, which is nearly optimal for the scaled MDP model.KeywordsFluid ApproximationMarkov Decision Process (MDP)Standard fluid ModelTotal Undiscounted CostDeterministic Stationary PolicyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
- W1546722720 created "2016-06-24" @default.
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- W1546722720 date "2012-01-01" @default.
- W1546722720 modified "2023-09-24" @default.
- W1546722720 title "Fluid Approximations to Markov Decision Processes with Local Transitions" @default.
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- W1546722720 doi "https://doi.org/10.1007/978-0-8176-8337-5_13" @default.
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