Simulation Based Algorithms For Markov Decision Processes
DOWNLOAD
Download Simulation Based Algorithms For Markov Decision Processes PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Simulation Based Algorithms For Markov Decision Processes book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Simulation Based Algorithms For Markov Decision Processes
DOWNLOAD
Author : Hyeong Soo Chang
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-01
Simulation Based Algorithms For Markov Decision Processes written by Hyeong Soo Chang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-01 with Business & Economics categories.
Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. This book brings the state-of-the-art research together for the first time. It provides practical modeling methods for many real-world problems with high dimensionality or complexity which have not hitherto been treatable with Markov decision processes.
Simulation Based Algorithms For Markov Decision Processes
DOWNLOAD
Author : Ying He
language : en
Publisher:
Release Date : 2002
Simulation Based Algorithms For Markov Decision Processes written by Ying He and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Algorithms categories.
Simulation Based Optimization Of Markov Decision Processes
DOWNLOAD
Author : Peter Marbach
language : en
Publisher:
Release Date : 1998
Simulation Based Optimization Of Markov Decision Processes written by Peter Marbach and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
Simulation Based Algorithms For Markov Decision Processes
DOWNLOAD
Author : Hyeong Soo Chang
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-02-26
Simulation Based Algorithms For Markov Decision Processes written by Hyeong Soo Chang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-26 with Technology & Engineering categories.
Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable. In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search. This substantially enlarged new edition reflects the latest developments in novel algorithms and their underpinning theories, and presents an updated account of the topics that have emerged since the publication of the first edition. Includes: innovative material on MDPs, both in constrained settings and with uncertain transition properties; game-theoretic method for solving MDPs; theories for developing roll-out based algorithms; and details of approximation stochastic annealing, a population-based on-line simulation-based algorithm. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling, and control, and simulation but will be a valuable source of tuition and reference for students of control and operations research.
Simulation Based Optimization
DOWNLOAD
Author : Abhijit Gosavi
language : en
Publisher:
Release Date : 2014-11-30
Simulation Based Optimization written by Abhijit Gosavi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-30 with categories.
A Stochastic Point Based Algorithm For Partially Observable Markov Decision Processes
DOWNLOAD
Author : Ludovic Tobin
language : en
Publisher:
Release Date : 2008
A Stochastic Point Based Algorithm For Partially Observable Markov Decision Processes written by Ludovic Tobin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Decision making under uncertainty is a popular topic in the field of artificial intelligence. One popular way to attack such problems is by using a sound mathematical model. Notably, Partially Observable Markov Processes (POMDPs) have been the subject of extended researches over the last ten years or so. However, solving a POMDP is a very time-consuming task and for this reason, the model has not been used extensively. Our objective was to continue the tremendous progress that has been made over the last couple of years, with the hope that our work will be a step toward applying POMDPs in large-scale problems. To do so, we combined different ideas in order to produce a new algorithm called SSVI (Stochastic Search Value Iteration). Three major accomplishments were achieved throughout this research work. Firstly, we developed a new offline POMDP algorithm which, on benchmark problems, proved to be more efficient than state of the arts algorithm. The originality of our method comes from the fact that it is a stochastic algorithm, in comparison with the usual determinist algorithms. Secondly, the algorithm we developed can also be applied in a particular type of online environments, in which this algorithm outperforms by a significant margin the competition. Finally, we also applied a basic version of our algorithm in a complex military simulation in the context of the Combat Identification project from DRDC-Valcartier.
Handbook Of Simulation Optimization
DOWNLOAD
Author : Michael C Fu
language : en
Publisher: Springer
Release Date : 2014-11-13
Handbook Of Simulation Optimization written by Michael C Fu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-13 with Business & Economics categories.
The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of the most well-established approaches for optimizing stochastic simulation models and a sampling of recent research advances in theory and methodology. Leading contributors cover such topics as discrete optimization via simulation, ranking and selection, efficient simulation budget allocation, random search methods, response surface methodology, stochastic gradient estimation, stochastic approximation, sample average approximation, stochastic constraints, variance reduction techniques, model-based stochastic search methods and Markov decision processes. This single volume should serve as a reference for those already in the field and as a means for those new to the field for understanding and applying the main approaches. The intended audience includes researchers, practitioners and graduate students in the business/engineering fields of operations research, management science, operations management and stochastic control, as well as in economics/finance and computer science.
Simulation Based Optimization
DOWNLOAD
Author : Abhijit Gosavi
language : en
Publisher: Springer
Release Date : 2014-10-30
Simulation Based Optimization written by Abhijit Gosavi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-30 with Business & Economics categories.
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.
Integrated Simulation Based Methodologies For Planning And Estimation
DOWNLOAD
Author :
language : en
Publisher:
Release Date : 2004
Integrated Simulation Based Methodologies For Planning And Estimation written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Significant progress was made in a number of proposed research areas. The first major task in the proposal involved incorporating simulation-based optimization (and, in particular, ordinal optimization) into dynamic optimization problems. In support of this task, progress was made on new sampling methods for Markov Decision Processes (MDPs), a new time aggregation approach for MDPs, simulation-based methods for weighted cost-to-go MDPs, approaches to proving the exponential convergence rate of ordinal comparisons, approximate receding horizon approaches to MDPs and Markov games, and new classes of stochastic approximation algorithms. In support of the second major task that involved estimation and control algorithms for dynamic hierarchical and graphical models, a variety of algorithms and analytical tools were developed for models on graphs with loops that exploit embedded loop-free structure. These algorithms offer the potential of significantly enhanced solutions to a variety of optimization problems critical to the Air Force. Another major task in the proposal involved risk-sensitive estimation and control. In support of this task, a new filtering scheme for the risk-sensitive state estimation of partially observed Markov chains was introduced and analyzed.
Stochastic Simulation Optimization For Discrete Event Systems Perturbation Analysis Ordinal Optimization And Beyond
DOWNLOAD
Author : Chun-hung Chen
language : en
Publisher: World Scientific
Release Date : 2013-07-03
Stochastic Simulation Optimization For Discrete Event Systems Perturbation Analysis Ordinal Optimization And Beyond written by Chun-hung Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-03 with Technology & Engineering categories.
Discrete event systems (DES) have become pervasive in our daily lives. Examples include (but are not restricted to) manufacturing and supply chains, transportation, healthcare, call centers, and financial engineering. However, due to their complexities that often involve millions or even billions of events with many variables and constraints, modeling these stochastic simulations has long been a “hard nut to crack”. The advance in available computer technology, especially of cluster and cloud computing, has paved the way for the realization of a number of stochastic simulation optimization for complex discrete event systems. This book will introduce two important techniques initially proposed and developed by Professor Y C Ho and his team; namely perturbation analysis and ordinal optimization for stochastic simulation optimization, and present the state-of-the-art technology, and their future research directions.