Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning: 55 (Operations Research/Computer Science Interfaces Series, 55) - Image 1

£102.76

Springer Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning: 55 (Operations Research/Computer Science Interfaces Series, 55)

Name: Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning: 55 (Operations Research/Computer Science Interfaces Series, 55)
Brand: Springer
Price: 111.64 GBP
Availability: InStock

42 black & white illustrations, 15 black

Managers' Guides to Computing

Price History & Forecast

Last 43 days • 43 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 43 days • 5 price levels

Days at Price

Current Price

Price Analysis

Most common price: £108 (10 days, 23.3%)

Price range: £103 - £112

Price levels: 5 different prices over 43 days

Description

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.

Product Specifications

Brand: Springer
Model: 42 black & white illustrations, 15 black
Format: hardcover
ASIN: 1489974903
Category: Books > Subjects > Business, Finance & Law > E-Commerce > Managers' Guides to Computing
Domain: Amazon UK
Release Date: 30 October 2014
Listed Since: 31 May 2014

Barcode

No barcode data available

Springer Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning: 55 (Operations Research/Computer Science Interfaces Series, 55)

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning: 55 (Operations Research/Computer Science Interfaces Series, 55)

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning: 25 (Operations Research/Computer Science Interfaces Series)

Reinforcement Learning for Sequential Decision and Optimal Control

Reinforcement Learning for Decision & Optimal Control - Springer

High-Performance Simulation-Based Optimization: 833 (Studies in Computational Intelligence, 833)

Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions

Reinforcement Learning and Dynamic Programming Using Function Approximators: 39 (Automation and Control Engineering)

Reinforcement Learning: Optimal Feedback Control with Industrial Applications (Advances in Industrial Control)

Springer Intelligent Computational Optimization in Engineering

Simulation-Based Algorithms for Markov Decision Processes (Communications and Control Engineering)

Springer Reinforcement Learning for Optimal Feedback Control

Springer - Reinforcement Learning for Optimal Feedback Control

Computational Intelligence in Expensive Optimization Problems: 2 (Adaptation, Learning, and Optimization, 2)

Decision Science in Action: Theory and Applications of Modern Decision Analytic Optimisation (Asset Analytics)

Optimization for Learning and Control

Stochastic Recursive Algorithms for Optimization: Simultaneous Perturbation Methods: 434 (Lecture Notes in Control and Information Sciences, 434)

Computing Tools for Modeling, Optimization and Simulation: Interfaces in Computer Science and Operations Research: 12 (Operations Research/Computer Science Interfaces Series, 12)

Computing Tools for Modeling, Optimization and Simulation: Interfaces in Computer Science and Operations Research: 12 (Operations Research/Computer Science Interfaces Series, 12)

Optimization for Machine Learning (Neural Information Processing series)

Advances in Metaheuristic Algorithms for Optimal Design of Structures

Convex Analysis and Global Optimization: 110 (Springer Optimization and Its Applications, 110)

Computational Intelligence in Expensive Optimization Problems: 2 (Adaptation, Learning, and Optimization, 2)

Machine Learning: A Bayesian and Optimization Perspective

Advances in Metaheuristic Algorithms for Optimal Design of Structures