Simulation-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. Key features of this revised and improved Second Edition
include: * 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) *
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 * 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
* 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 Themed 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.
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