Download PDF Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series)
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Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series)
Download PDF Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series)
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Review
The following reviews may have been corrected for minor grammatical errors:"One of the great books. I have found every detail I needed and he has done (an) excellent job. I will definitely recommend the book." -- A reader from Google Books
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From the Author
The main motivation for writing this book was to provide an accessible account of methods based on Reinforcement Learning (closely related to what is now also called Approximate Dynamic Programming) and Meta-Heuristics (closely related to what is now also called Stochastic Adaptive Search) for optimization in discrete-event systems via simulation. Reinforcement Learning (RL) is typically used for solving Markov decision problems (MDPs), which are dynamic optimization problems where the underlying discrete-event stochastic system is driven by Markov chains, while Meta-Heuristics are used for solving static optimization problems where the underlying system is any discrete-event stochastic system (not necessarily driven by Markov chains). This book provides a selected collection of topics, mostly focused on model-free techniques, which are useful when one does not have access to the structure of the objective function (in static optimization) or the transition probability function (in dynamic optimization).   My goal was neither to overwhelm the reader with mathematical details nor was it to cover every topic. Rather, the goal was to provide the reader with an overview of the fundamental concepts and at the same time provide the details required for solving real-world stochastic optimization problems via simulation-based techniques. Some of the main topics covered are: Reinforcement learning techniques for discounted and average reward MDPsDetailed recipes for Reinforcement Learning algorithms such as Q-Learning, SARSA, R-SMART, and Actor CriticsStatic optimization techniques rooted in meta-heuristics (simulated annealing, genetic algorithms, and tabu search) and stochastic adaptive search (nested partitions, stochastic ruler, and backtracking adaptive search) for discrete solution spaces and simultaneous perturbation for continuous solution spacesNeural network algorithms useful for function approximation in response surface methods for static optimization and in reinforcement learning for MDPs with large state-action spacesA detailed background on dynamic programming (value and policy iteration)A special coverage of semi-MDPs (SMDPs), average reward problems, finite horizon MDPs, and two time scales in RLA gentle introduction to convergence analysis of simulation optimization methods via Banach fixed point theory and Ordinary Differential Equations Preview
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Product details
Series: Operations Research/Computer Science Interfaces Series (Book 55)
Hardcover: 508 pages
Publisher: Springer; 2nd ed. 2015 edition (October 30, 2014)
Language: English
ISBN-10: 9781489974907
ISBN-13: 978-1489974907
ASIN: 1489974903
Product Dimensions:
6.1 x 1.2 x 9.2 inches
Shipping Weight: 2.1 pounds (View shipping rates and policies)
Average Customer Review:
5.0 out of 5 stars
3 customer reviews
Amazon Best Sellers Rank:
#394,714 in Books (See Top 100 in Books)
I recently used this to teach a graduate course taken by industrial engineering and mechanical engineering students. The students loved it. It was comprehensive, yet easy to understand the theory behind topics such as neural networks and reinforcement learning, 2 commonly used data and decision analytic techniques.
Very comprehensive book ! The language used is very simple and one best thing about the book is that the flow of topics, which worked very well for me.
I used this book in my most recent graduate level course in Engineering.The material was described beautifully and in a straight-forward way. The author makes good use of references which tie ideas together. The chapters build on each other fluidly.I highly recommend this book. You will learn, without any doubt, from reading this book.
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