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Business & Management - Operations Research & Decision Theory | Simulation-Based Optimization - Parametric Optimization Techniques and Reinforcement Learning

Simulation-Based Optimization

Parametric Optimization Techniques and Reinforcement Learning

Gosavi, Abhijit

2nd ed. 2015, XXVI, 508 p. 42 illus.

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  • Brings the field completely up to date
  • All computer code brought up to date
  • New material not covered in first edition includes nested partitions, simultaneous perturbation, backtracking adaptive search and the stochastic ruler method

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.

Content Level » Research

Keywords » Computational Operations Research - Operations Research - Optimization - Reinforcement Learning - Simulation

Related subjects » Applications - Operations Research & Decision Theory - Theoretical Computer Science

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