Description: Simulation-Based Algorithms for Markov Decision Processes Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Author(s): Hyeong Soo Chang, Jiaqiao Hu, Michael C. Fu, Steven I. Marcus Format: Paperback Publisher: Springer London Ltd, United Kingdom Imprint: Springer London Ltd ISBN-13: 9781447159902, 978-1447159902 Synopsis Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable. In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available ([url] for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search. This substantially enlarged new edition reflects the latest developments in novel algorithms and their underpinning theories, and presents an updated account of the topics that have emerged since the publication of the first edition. Includes: innovative material on MDPs, both in constrained settings and with uncertain transition properties; game-theoretic method for solving MDPs; theories for developing roll-out based algorithms; and details of approximation stochastic annealing, a population-based on-line simulation-based algorithm. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling, and control, and simulation but will be a valuable source of tuition and reference for students of control and operations research.
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Book Title: Simulation-Based Algorithms for Markov Decision Processes
Subject Area: Data Analysis, Mechanical Engineering
Item Height: 235 mm
Item Width: 155 mm
Series: Communications and Control Engineering
Author: Steven I. Marcus, Hyeong Soo Chang, Michael C. Fu, Jiaqiao Hu
Publication Name: Simulation-Based Algorithms for Markov Decision Processes
Format: Paperback
Language: English
Publisher: Springer London LTD
Subject: Computer Science, Mathematics, Management
Publication Year: 2015
Type: Textbook
Item Weight: 519 g
Number of Pages: 229 Pages
