Markov decision processes and stochastic positional games: optimal control on complex networks

By:

Lozovanu, Dmitrii

Material type: Text

TextSeries: International Series in Operations Research & Management SciencePublication details: Springer Switzerland 2024Description: xix, 396 pISBN:

9783031401794

Subject(s):

DDC classification:

519.542 LOZ

Summary: 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. (https://link.springer.com/book/10.1007/978-3-031-40180-0)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.542 LOZ (Browse shelf(Opens below))	1	Available		007662

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Previous	519.542 GAR Bayesian optimization	519.542 LIO Bayesian inference and computation in reliability and survival analysis	519.542 LON Statistics for making decisions	519.542 LOZ Markov decision processes and stochastic positional games: optimal control on complex networks	519.542 MA Bayesian models of perception and action: an introduction	519.542 POW Sequential decision analytics and modeling: modeling with python	519.542 WAT Mathematical theory of bayesian statistics	Next

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.

(https://link.springer.com/book/10.1007/978-3-031-40180-0)

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