| Springer Nature B.V.

73,480원  | 20171215  | 9781461508069

Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter was written by a leading expert in the re­ spective area. The papers cover major research areas and methodologies, and discuss open questions and future research directions. The papers can be read independently, with the basic notation and concepts ofSection 1.2.

Boucherie, Richard (EDT)  | Springer

551,230원  | 20170316  | 9783319477640

This book presents classical Markov Decision Processes (MDP) for real-life applications and optimization. MDP allows users to develop and formally support approximate and simple decision rules, and this book showcases state-of-the-art applications in which MDP was key to the solution approach.

 | Springer Nature B.V.

73,480원  | 20170315  | 9783319477657

This book presents classical Markov Decision Processes (MDP) for real-life applications and optimization. MDP allows users to develop and formally support approximate and simple decision rules, and this book showcases state-of-the-art applications in which MDP was key to the solution approach. ?The book is divided into six parts.

 | Routledge

423,220원  | 19990330  | 9780849303821

This book covers Markov Decisions Processes with constraints -- analyzing the many developments in the areas of MDPs within the last decade.

Puterman, Martin L.  | Wiley

0원  | 19940101  | 9780471619772

 | Springer

264,580원  | 20250114  | 9783031401824

This book presents recent findings and results concerning the solutions of especially finite state-space Markov decision problems and determining Nash equilibria for related stochastic games with average and total expected discounted reward payoffs.

Guo, Xianping, Hernandez-Lerma, Onesimo  | Springer

202,100원  | 20210101  | 9783642025464

Continuous-time Markov decision processes (MDPs), also known as controlled Markov chains, are used for modeling decision-making problems that arise in operations research (for instance, inventory, manufacturing, and queueing systems), computer science, communications engineering, control of populations (such as fisheries and epidemics), and management science, among many other fields.

 | Springer

69,800원  | 20120703  | 9783031004315

Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics.

 | Morgan & Claypool

73,500원  | 20120630  | 9781608458868

Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning.

Feinberg, Eugene A. (EDT)/ Shwartz, Adam (EDT)  | Kluwer

698,230원  | 20020101  | 9780792374596

1.1 AN OVERVIEW OF MARKOV DECISION PROCESSES The theory of Markov Decision Processes-also known under several other names including sequential stochastic optimization, discrete-time stochastic control, and stochastic dynamic programming-studiessequential optimization ofdiscrete time stochastic systems.

Puterman, Martin L.  | Wiley-Interscience

157,240원  | 20050301  | 9780471727828

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This text is unique in bringing together so many results hitherto found only in part in other texts and papers. . . . The text is fairly self-contained, inclusive of some basic mathematical results needed, and provides a rich diet of examples, applications, and exercises. The bibliographical material at the end of each chapter is excellent, not only from a historical perspective, but because it is valuable for researchers in acquiring a good perspective of the MDP research potential." -Zentralblatt fur Mathematik ". . . it is of great value to advanced-level students, researchers, and professional practitioners of this field to have now a complete volume (with more than 600 pages) devoted to this topic. . . . Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up-to-date, unified, and rigorous treatment of theoretical and computational aspects of discrete-time Markov decision processes." -Journal of the American Statistical Association

Chang, Hyeong Soo/ Fu, Michael, C./ Hu, Jiaqiao/ M  | Springer

0원  | 20070301  | 9781846286896

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. It is well-known that many real-world problems modeled by MDPs have huge state and/or action spaces, leading to the notorious curse of dimensionality that makes practical solution of the resulting models intractable. In other cases, the system of interest is complex enough that it is not feasible to specify some of the MDP model parameters explicitly, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based numerical algorithms have been developed recently to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include: multi-stage adaptive sampling; evolutionary policy iteration; evolutionary random policy search; and model reference adaptive search. Simulation-based Algorithms for Markov Decision Processes brings this state-of-the-art research together for the first time and presents it in a manner that makes it accessible to researchers with varying interests and backgrounds. In addition to providing numerous specific algorithms, the exposition includes both illustrative numerical examples and rigorous theoretical convergence results. The algorithms developed and analyzed differ from the successful computational methods for solving MDPs based on neuro-dynamic programming or reinforcement learning and will complement work in those areas. Furthermore, the authors show how to combine the various algorithms introduced with approximate dynamic programming methods that reduce the size of the state space and ameliorate the effects of dimensionality. 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 instruction and reference for students of control and operations research.

Vikram Krishnamurthy  | Cambridge University Press

209,470원  | 20160321  | 9781107134607

This book covers formulation, algorithms, and structural results of partially observed Markov decision processes, whilst linking theory to real-world applications in controlled sensing. Computations are kept to a minimum, enabling students and researchers in engineering, operations research, and economics to understand the methods and determine the structure of their optimal solution.

Howard, Ronald A.  | Dover

45,120원  | 20070611  | 9780486458724

This book is an integrated work published in two volumes. the first volume treats the basic Markov process and its variants; the second, semi-Markov and decision processes. It equips readers to formulate, analyze, and evaluate simple and advanced Markov models of systems, ranging from genetics to space engineering to marketing. 1971 edition.

Theodore J. Sheskin  | CRC Press

356,400원  | 20210101  | 9781420051117

Presents an introduction to finite Markov chains and Markov decision processes, with applications in engineering and management. This book introduces discrete-time, finite-state Markov chains, and Markov decision processes. It describes both algorithms and applications, enabling students to understand the logical basis for the algorithms.