Abhijit Gosavi – Simulation Based Optimization. Parametric Optimization Techniques and Reinforcement Learning (Second edition)

Автор: Abhijit Gosavi
Название книги: Simulation Based Optimization. Parametric Optimization Techniques and Reinforcement Learning (Second edition)
Формат: PDF
Жанр: Экономические дисциплины
Страницы: 530
Качество: Изначально компьютерное, E-book

This book introduces to the reader the evolving area of simulation-based optimization, also known as simulation optimization. The book should serve as an accessible introduction to this topic and requires a background only in elementary mathematics. It brings the reader up to date on cutting-edge advances in simulation-optimization methodologies, including dynamic controls, also called Reinforcement Learning (RL) or Approximate Dynamic Programming (ADP), and static optimization techniques, e.g., Simultaneous Perturbation, Nested Partitions, Backtracking Adaptive Search, Response Surfaces, and Meta-Heuristics. Special features of this book include:

Stochastic Control Optimization:
An Accessible Introduction to Reinforcement Learning Techniques for Solving Markov Decision Processes (MDPs), with Step-by-Step Descriptions of Numerous Algorithms, e.g., Q-Learning, SARSA, R-SMART, Actor-Critics, Q-P-Learning, and Classical Approximate Policy Iteration
A Detailed Discussion on Dynamic Programing for Solving MDPs and Semi-MDPs (SMDPs), Including Steps for Value Iteration and Policy Iteration
An Introduction to Function Approximation with Reinforcement Learning
An In-Depth Treatment of Reinforcement Learning Methods for SMDPs, Average Reward Problems, Finite Horizon Problems, and Two Time Scales
Computer Programs (available online)
A Gentle Introduction to Convergence Analysis of Simulation Optimization Methods via Banach Fixed Point Theory and Ordinary Differential Equations (ODEs)
Stochastic Static Optimization:
A Step-by-Step Description of Stochastic Adaptive Search Algorithms, e.g., Simultaneous Perturbation, Nested Partitions, Backtracking Adaptive Search, Stochastic Ruler, and Meta-Heuristics, e.g., Simulated Annealing, Tabu Search, and Genetic Algorithms
A Clear and Simple Introduction to the Methodology of Neural Networks
The book ends with a chapter on case studies that explain how these methods can be applied in real-world settings; an online repository of computer programs that can be downloaded from a website is also available.

The book was written for students and researchers in the fields of engineering (industrial, electrical, and computer), computer science, operations research, management science, and applied mathematics. An attractive feature of this book is its accessibility to readers new to this topic.