Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


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

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Hyeong Soo Chang 作者
Springer
譯者
2007-03-05 出版日期
189 頁數
USD 89.95 價格
Hardcover
Communications and Control Engineering 叢書系列
9781846286896 圖書編碼

Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering) 在線電子書 圖書標籤: 機器學習   


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Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering) 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering) 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



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Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering) 在線電子書 著者簡介


Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering) 在線電子書 著者簡介


Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering) 在線電子書 圖書描述

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: a multi-stage adaptive sampling; a evolutionary policy iteration; a evolutionary random policy search; and a 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 introducedwith 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.

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