Markov decision processes: discrete stochastic dynamic programming by Martin L. Puterman
- Markov decision processes: discrete stochastic dynamic programming
- Martin L. Puterman
- Page: 666
- Format: pdf, ePub, mobi, fb2
- ISBN: 9780471619772
- Publisher: Wiley-Interscience
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<p>An up-to-date, unified and rigorous treatment of theoretical, computational and applied research on Markov decision process models. Concentrates on infinite-horizon discrete-time models. Discusses arbitrary state spaces, finite-horizon and continuous-time discrete-state models. Also covers modified policy iteration, multichain models with average reward criterion and sensitive optimality. Features a wealth of figures which illustrate examples and an extensive bibliography.</p> <p> From the Publisher</p> <p> An up-to-date, unified and rigorous treatment of theoretical, computational and applied research on Markov decision process models. Concentrates on infinite-horizon discrete-time models. Discusses arbitrary state spaces, finite-horizon and continuous-time discrete-state models. Also covers modified policy iteration, multichain models with average reward criterion and sensitive optimality. Features a wealth of figures which illustrate examples and an extensive bibliography. </p>
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Markov Decision Processes: Discrete Stochastic Dynamic
An up-to-date, unified and rigorous treatment of theoretical, computational and applied research on Markov decision process models. Concentrates on
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We can develop dynamic programming equations min x1 f1(x1) + We usually seek Markov decision rules dt : S → As. □ Decisions can be Discrete time steps. □ Two-stage Random variable Wt, usually Wiener process.
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