Modern Trends in Controlled Stochastic Processes: (Paperback)

Alexey Piunovskiy

ISBN 10: 3030769305 / ISBN 13: 9783030769307

Published by Springer Nature Switzerland AG, Cham, 2022

About this Item

Paperback. This book presents state-of-the-art solution methods and applications of stochastic optimal control. It is a collection of extended papers discussed at the traditional Liverpool workshop on controlled stochastic processes with participants from both the east and the west. New problems are formulated, and progresses of ongoing research are reported.Topics covered in this book include theoretical results and numerical methods for Markov and semi-Markov decision processes, optimal stopping of Markov processes, stochastic games, problems with partial information, optimal filtering, robust control, Q-learning, and self-organizing algorithms. Real-life case studies and applications, e.g., queueing systems, forest management, control of water resources, marketing science, and healthcare, are presented. Scientific researchers and postgraduate students interested in stochastic optimal control,- as well as practitioners will find this book appealing and a valuable reference. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9783030769307

Bibliographic Details

• Title: Modern Trends in Controlled Stochastic Processes: (Paperback)
• Publisher: Springer Nature Switzerland AG, Cham
• Publication Date: 2022
• Language: English
• ISBN 10: 3030769305
• ISBN 13: 9783030769307
• Binding: Paperback
• Condition: new

About this title

Synopsis

This book presents state-of-the-art solution methods and applications of stochastic optimal control. It is a collection of extended papers discussed at the traditional Liverpool workshop on controlled stochastic processes with participants from both the east and the west. New problems are formulated, and progresses of ongoing research are reported.

Topics covered in this book include theoretical results and numerical methods for Markov and semi-Markov decision processes, optimal stopping of Markov processes, stochastic games, problems with partial information, optimal filtering, robust control, Q-learning, and self-organizing algorithms. Real-life case studies and applications, e.g., queueing systems, forest management, control of water resources, marketing science, and healthcare, are presented.

 

Scientific researchers and postgraduate students interested in stochastic optimal control,- as well as practitioners will find this book appealing and a valuable reference.

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