{"product_id":"simulation-based-optimization-parametric-optimization-techniques-and-reinforcement-learning-9781489977311","title":"Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning","description":"\u003cp\u003e • Author(s): Abhijit Gosavi\u003cbr\u003e • Publisher: Springer\u003cbr\u003e • Publisher Imprint: Springer\u003cbr\u003e • BISAC: Probability \u0026amp; Statistics - General\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFrom the Back Cover\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003eSimulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning\u003c\/i\u003e\u003c\/b\u003e introduces the evolving area of static and dynamic simulation-based optimization. Covered in detail are \u003ci\u003emodel-free\u003c\/i\u003e optimization techniques - especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eKey features of this revised and improved Second Edition include: \u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e- Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search, and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search, and meta-heuristics (simulated annealing, tabu search, and genetic algorithms)\u003c\/p\u003e\u003cp\u003e- Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics\u003c\/p\u003e\u003cp\u003e- An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: \u003ci\u003eQ\u003c\/i\u003e-\u003ci\u003eLearning\u003c\/i\u003e, \u003ci\u003eSARSA\u003c\/i\u003e, and \u003ci\u003eR-SMART \u003c\/i\u003ealgorithms, and policy search, via \u003ci\u003eAPI\u003c\/i\u003e, \u003ci\u003eQ\u003c\/i\u003e-\u003ci\u003eP\u003c\/i\u003e-\u003ci\u003eLearning\u003c\/i\u003e, actor-critics, and learning automata\u003c\/p\u003e\u003cp\u003e- A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online), and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations\u003c\/p\u003e\u003cp\u003eThemed around three areas in separate sets of chapters - \u003cb\u003eStatic Simulation Optimization, Reinforcement Learning, \u003c\/b\u003eand\u003cb\u003e Convergence Analysis\u003c\/b\u003e\u003ci\u003e \u003c\/i\u003e- this book is written for researchers and students in the fields of engineering (industrial, systems, electrical, and computer), operations research, computer science, and applied mathematics.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Paperback","offer_id":45274001211543,"sku":"9781489977311","price":8369.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9781489977311.webp?v=1769238376","url":"https:\/\/atlanticbooks.com\/products\/simulation-based-optimization-parametric-optimization-techniques-and-reinforcement-learning-9781489977311","provider":"Atlantic Books","version":"1.0","type":"link"}