| 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 | Rigorous theoretical derivation of sampling and population-based algorithms enables the reader to expand on the work presented in the certainty that new results will have a sound foundation.New chapte |
| 丛书名称 | Communications and Control Engineering |
| 图书封面 |  |
| 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. 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 mate |
| 出版日期 | Book 2013Latest edition |
| 关键词 | Controlled Markov Chains; Markov Decision Processes; Simulation-based Algorithms; Stochastic Dynamic Pr |
| 版次 | 2 |
| doi | https://doi.org/10.1007/978-1-4471-5022-0 |
| isbn_softcover | 978-1-4471-5990-2 |
| isbn_ebook | 978-1-4471-5022-0Series ISSN 0178-5354 Series E-ISSN 2197-7119 |
| issn_series | 0178-5354 |
| copyright | Springer-Verlag London 2013 |
发表于 2025-3-21 18:21:05
