| 书目名称 | Simulation-based Algorithms for Markov Decision Processes |
| 编辑 | Hyeong Soo Chang,Jiaqiao Hu,Steven I. Marcus |
| 视频video | |
| 概述 | Provides practical modeling methods for many real-world problems with high dimensionality or complextity which have not hitherto been treatable with Markov decision processes.Rigorous theoretical deri |
| 丛书名称 | Communications and Control Engineering |
| 图书封面 |  |
| 描述 | .Often, real-world problems modeled by Markov decision processes (MDPs) are difficult to solve in practise because of the curse of dimensionality. In others, explicit specification of the MDP model parameters is not feasible, but simulation samples are available. For these settings, various sampling and population-based numerical algorithms for computing an optimal solution in terms of a policy and/or value function have been developed recently. ..Here, this state-of-the-art research is brought together in a way that makes it accessible to researchers of varying interests and backgrounds. Many specific algorithms, illustrative numerical examples and rigorous theoretical convergence results are provided. The algorithms differ from the successful computational methods for solving MDPs based on neuro-dynamic programming or reinforcement learning. The algorithms can be combined with approximate dynamic programming methods that reduce the size of the state space and ameliorate the effects of dimensionality.. |
| 出版日期 | Book 20071st edition |
| 关键词 | Control; Control Theory; Decision; Dynamic Programming; Evolutionary Policy Iteration; Markov Processes; M |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-84628-690-2 |
| isbn_softcover | 978-1-84996-643-6 |
| isbn_ebook | 978-1-84628-690-2Series ISSN 0178-5354 Series E-ISSN 2197-7119 |
| issn_series | 0178-5354 |
| copyright | Springer-Verlag London 2007 |
发表于 2025-3-21 18:50:18
