| 書目名稱 | From Shortest Paths to Reinforcement Learning |
| 副標(biāo)題 | A MATLAB-Based Tutor |
| 編輯 | Paolo Brandimarte |
| 視頻video | http://file.papertrans.cn/349/348925/348925.mp4 |
| 概述 | Covers both, classical numerical analysis approaches and more recent learning strategies based on Monte Carlo simulation.Includes well-documented MATLAB code snapshots to illustrate algorithms and app |
| 叢書名稱 | EURO Advanced Tutorials on Operational Research |
| 圖書封面 |  |
| 描述 | Dynamic programming (DP) has a relevant history as a powerful and flexible optimization principle, but has a bad reputation as a computationally impractical tool. This book fills a gap between the statement of DP principles and their actual software implementation. Using MATLAB throughout, this tutorial gently gets the reader acquainted with DP and its potential applications, offering the possibility of actual experimentation and hands-on experience. The book assumes?basic familiarity with probability and optimization, and is suitable to both practitioners and graduate students in engineering, applied mathematics, management, finance and economics. |
| 出版日期 | Textbook 2021 |
| 關(guān)鍵詞 | Dynamic programming; Reinforcement learning; Machine learning; Stochastic optimization; Dynamic optimiza |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-61867-4 |
| isbn_softcover | 978-3-030-61869-8 |
| isbn_ebook | 978-3-030-61867-4Series ISSN 2364-687X Series E-ISSN 2364-6888 |
| issn_series | 2364-687X |
| copyright | Springer Nature Switzerland AG 2021 |
|Archiver|手機版|小黑屋|
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