PDF⋙ Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series) by Abhijit Gosavi
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series) by Abhijit Gosavi
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series) by Abhijit Gosavi PDF, ePub eBook D0wnl0ad
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)
- 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 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.
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