TY  - BOOK
AU  - Gosavi,Abhijit
ED  - SpringerLink (Online service)
TI  - Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning 
T2  - Operations Research/Computer Science Interfaces Series,
SN  - 9781489974914
AV  - HD30.23 
U1  - 658.40301 23
PY  - 2015///
CY  - Boston, MA
PB  - Springer US, Imprint: Springer
KW  - Economics
KW  - Computer simulation
KW  - Operations research
KW  - Economics/Management Science
KW  - Operation Research/Decision Theory
KW  - Operations Research, Management Science
KW  - Simulation and Modeling
N1  - Background -- Simulation basics -- Simulation optimization: an overview -- Response surfaces and neural nets -- Parametric optimization -- Dynamic programming -- Reinforcement learning -- Stochastic search for controls -- Convergence: background material -- Convergence: parametric optimization -- Convergence: control optimization -- Case studies
N2  - Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces 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
UR  - http://dx.doi.org/10.1007/978-1-4899-7491-4
ER  -