Name: Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (en Inglés)
Price: 148840.00 CLP
Availability: InStock
Author: Gosavi, Abhijit
ISBN: 9781489977311

portada Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (en Inglés)

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (en Inglés)

Formato

Libro Físico

Autor

Abhijit Gosavi

Editorial

Springer

Idioma

Inglés

N° páginas

508

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 15.6 x 2.7 cm

Peso

0.74 kg.

ISBN13

9781489977311

N° edición

0002

Categorías

Medicina: cuestiones generales
Probabilidad y estadística
Ingeniería de control automático

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (en Inglés)

Abhijit Gosavi (Autor) · Springer · Tapa Blanda

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Reseña del libro "Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (en Inglés)"

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 EquationsThemed 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.