Modern Trends in Controlled Stochastic Processes: (Paperback)

Alexey Piunovskiy

ISBN 10: 3030769305 / ISBN 13: 9783030769307

Edité par Springer Nature Switzerland AG, Cham, 2022

A propos de cet article

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.

N° de réf. du vendeur 9783030769307

Détails bibliographiques

• Titre: Modern Trends in Controlled Stochastic Processes: (Paperback)
• Éditeur: Springer Nature Switzerland AG, Cham
• Date de parution: 2022
• Langue: anglais
• ISBN à 10 chiffres: 3030769305
• ISBN à 13 chiffres: 9783030769307
• Reliure: Paperback
• État de l'article: new

A propos de ce titre

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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