{"product_id":"9781489974907","title":"Simulation-Based Optimization : Parametric Optimization Techniques and Reinforcement Learning (Operations Research\/Computer Science Interfaces Series) \u003cVol. 55\u003e (2nd)","description":"\u003cp\u003eSimulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free 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\n\n\u003cp\u003eKey features of this revised and improved Second Edition include:\u003c\/p\u003e\n\n\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\n\n\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\n\n\u003cp\u003e· An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata\u003c\/p\u003e\n\n\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\n\n\u003cp\u003eThemed around three areas in separate sets of chapters - Static Simulation Optimization, Reinforcement Learning and Convergence Analysis - 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":"Default Title","offer_id":52559903228215,"sku":"00000_00000_00000_00000","price":1078.18,"currency_code":"MYR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0962\/3548\/7543\/files\/9781489974907-1.jpg?v=1783210132","url":"https:\/\/kinokuniya.com.my\/products\/9781489974907","provider":"Books Kinokuniya Malaysia","version":"1.0","type":"link"}