{"product_id":"9781489974907","title":"Simulation-Based Optimization","description":"\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\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePublication Year: \u003c\/strong\u003e2015\u003cbr\u003e\u003cstrong\u003eImprint: \u003c\/strong\u003eSpringer US\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003eFormat: H\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003eWeight (Gram): 9221\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003c\/p\u003e","brand":"Abhijit Gosavi","offers":[{"title":"Default Title","offer_id":41277408116914,"sku":"9781489974907","price":202.79,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0502\/5382\/4178\/products\/1489974903.01_SCLZZZZZZZ.jpg?v=1636197380","url":"https:\/\/readabook.store\/products\/9781489974907","provider":"READABOOK BY ALKEM","version":"1.0","type":"link"}