{"product_id":"9781489974907","title":"Operations Research\/Computer Science Interfaces Series: Parametric Optimization Techniques and Reinforcement Learning","description":"\u003ch1\u003eOperations Research\/Computer Science Interfaces Series: Parametric Optimization Techniques and Reinforcement Learning\u003c\/h1\u003e \u003ch2\u003eGosavi, Abhijit\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003eSimulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning\u003c\/i\u003e\u003c\/b\u003e introduce 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 \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2014-10-30\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9781489974907\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-1-4899-7491-4\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 508\u003c\/p\u003e ","brand":"Springer","offers":[{"title":"Default Title","offer_id":45986327593100,"sku":"9781489974907","price":179.1,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781489974907.jpg?v=1775704498","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9781489974907","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}