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Markov Decision Processes and Stochastic Positional Games

- Optimal Control on Complex Networks

About Markov Decision Processes and Stochastic Positional Games

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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  • Language:
  • English
  • ISBN:
  • 9783031401794
  • Binding:
  • Hardback
  • Pages:
  • 383
  • Published:
  • January 12, 2024
  • Dimensions:
  • 156x234x24 mm.
  • Weight:
  • 757 g.
Delivery: 2-4 weeks
Expected delivery: December 22, 2024
Extended return policy to January 30, 2025

Description of Markov Decision Processes and Stochastic Positional Games

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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