| 書目名稱 | Learning for Decision and Control in Stochastic Networks | | 編輯 | Longbo Huang | | 視頻video | http://file.papertrans.cn/583/582925/582925.mp4 | | 概述 | Introduces Learning-Augmented Network Optimization based on a general stochastic network optimization model.Covers key theoretical tools for network research, as well as popular learning-based methods | | 叢書名稱 | Synthesis Lectures on Learning, Networks, and Algorithms | | 圖書封面 |  | | 描述 | This book introduces the Learning-Augmented Network Optimization (LANO) paradigm, which interconnects network optimization with the emerging AI theory and algorithms and has been receiving a growing attention in network research. The authors present the topic based on a general stochastic network optimization model, and review several important theoretical tools that are widely adopted in network research, including convex optimization, the drift method, and mean-field analysis. The book then covers several popular learning-based methods, i.e., learning-augmented drift, multi-armed bandit and reinforcement learning, along with applications in networks where the techniques have been successfully applied. The authors also provide a discussion on potential future directions and challenges. | | 出版日期 | Book 2023 | | 關(guān)鍵詞 | Network Optimization; Learning; Drift Method; Convex Optimization; Mean-Field; Online Learning; Reinforcem | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-31597-8 | | isbn_softcover | 978-3-031-31599-2 | | isbn_ebook | 978-3-031-31597-8Series ISSN 2690-4306 Series E-ISSN 2690-4314 | | issn_series | 2690-4306 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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