Simulation-Based Optimization Parametric Optimization Techniques and Reinforcement Learning

<p><b><i>Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning</i></b> introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are <i>model-free</i> 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.</p><p><b>Key features of this revised and improved Second Edition include:</b></p><p>· 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)</p><p>· 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</p><p>· An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: <i>Q</i>-<i>Learning</i>, <i>SARSA</i>, and <i>R-SMART </i>algorithms, and policy search, via <i>API</i>, <i>Q</i>-<i>P</i>-<i>Learning</i>, actor-critics, and learning automata</p><p>· 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</p><p>Themed around three areas in separate sets of chapters – <b>Static Simulation Optimization, Reinforcement Learning </b>and<b> Convergence Analysis</b><i> </i>– 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.</p>