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Markov Decision Processes and Stochastic Positional Games [electronic resource] : Optimal Control on Complex Networks / by Dmitrii Lozovanu, Stefan Wolfgang Pickl.

By: Contributor(s): Material type: TextSeries: International Series in Operations Research & Management Science ; 349Publisher: Cham : Springer International Publishing : Imprint: Springer, 2024Edition: 1st ed. 2024Description: XIX, 396 p. 41 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783031401800
Subject(s):
Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleLOC classification:
  • T57.6-.97
Online resources:
Contents:
Discrete Markov Processes and Numerical Algorithms for Markov Chains -- Markov Decision Processes and Stochastic Control Problems on Networks -- Stochastic Games and Positional Games on Networks.
In: Springer Nature eBookSummary: This book presents recent findings and results concerning the solutions of especially finite state-space Markov decision problems and determining Nash equilibria for related stochastic games with average and total expected discounted reward payoffs. In addition, it focuses on a new class of stochastic games: stochastic positional games that extend and generalize the classic deterministic positional games. It presents new algorithmic results on the suitable implementation of quasi-monotonic programming techniques. Moreover, the book presents applications of positional games within a class of multi-objective discrete control problems and hierarchical control problems on networks. Given its scope, the book will benefit all researchers and graduate students who are interested in Markov theory, control theory, optimization and games.
Item type: List(s) this item appears in: Titluri cărți electronice Springer Nature 2025 (10631 titluri)
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Discrete Markov Processes and Numerical Algorithms for Markov Chains -- Markov Decision Processes and Stochastic Control Problems on Networks -- Stochastic Games and Positional Games on Networks.

This book presents recent findings and results concerning the solutions of especially finite state-space Markov decision problems and determining Nash equilibria for related stochastic games with average and total expected discounted reward payoffs. In addition, it focuses on a new class of stochastic games: stochastic positional games that extend and generalize the classic deterministic positional games. It presents new algorithmic results on the suitable implementation of quasi-monotonic programming techniques. Moreover, the book presents applications of positional games within a class of multi-objective discrete control problems and hierarchical control problems on networks. Given its scope, the book will benefit all researchers and graduate students who are interested in Markov theory, control theory, optimization and games.

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