Continual and Reinforcement Learning for Edge AI: Framework, Foundation, and Algorithm Design
暫譯: 邊緣人工智慧的持續與強化學習：框架、基礎與演算法設計
Wang, Hang, Lin, Sen, Zhang, Junshan
- 出版商: Springer
- 出版日期: 2025-05-21
- 售價: $1,840
- 貴賓價: 9.5 折 $1,748
- 語言: 英文
- 頁數: 265
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3031843622
- ISBN-13: 9783031843624
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相關分類:
Reinforcement、DeepLearning、Algorithms-data-structures
海外代購書籍(需單獨結帳)
相關主題
商品描述
This book provides a comprehensive introduction to continual and reinforcement learning for edge AI, which investigates how to build an AI agent that can continuously solve new learning tasks and enhance the AI at resource-limited edge devices. The authors introduce readers to practical frameworks and in-depth algorithmic foundations. The book surveys the recent advances in the area, coming from both academic researchers and industry professionals. The authors also present their own research findings on continual and reinforcement learning for edge AI. The book also covers the practical applications of the topic and identifies exciting future research opportunities.
商品描述(中文翻譯)
本書提供了邊緣人工智慧（edge AI）中持續學習（continual learning）和強化學習（reinforcement learning）的全面介紹，探討如何建立一個能夠持續解決新學習任務並在資源有限的邊緣設備上增強人工智慧的代理。作者向讀者介紹了實用的框架和深入的算法基礎。本書調查了該領域的最新進展，這些進展來自學術研究者和業界專業人士。作者還展示了他們在邊緣人工智慧的持續學習和強化學習方面的研究成果。本書還涵蓋了該主題的實際應用，並指出了令人興奮的未來研究機會。
作者簡介
Hang Wang is a Ph.D. candidate in the Department of Electrical and Computer Engineering at the University of California, Davis. He received his B.E. from the University of Science and Technology of China (USTC). His research aims to establish a fundamental understanding of reinforcement learning, multi-agent systems, and human-AI interaction, as well as practical applications such asautonomous driving and edge computing. His contributions have been published in NeurIPS, AAMAS. His recent work on Warm-start Reinforcement Learning also garnered attention and acclaim via an oral presentation at ICML.
Sen Lin, Ph.D., is an Assistant Professor in the Department of Computer Science at University of Houston. He received his Ph.D. degree from Arizona State University, M.S. from HKUST and B.E. from Zhejiang University. His research interests broadly fall in the intersection of machine learning and wireless networking. Currently, his research focuses on developing algorithms and theories in continual learning, meta-learning, reinforcement learning, adversarial machine learning and bilevel optimization, with applications in multiple domains, e.g., edge computing, security, network control.
Junshan Zhang, Ph.D. is a Professor in the ECE Department at the University of California, Davis. He received his Ph.D. from the School of ECE at Purdue University. His research interests fall in the general field of information networks and data science, including edge intelligence, reinforcement learning, continual learning, network optimization and control, and game theory, with applications in connected and automated vehicles, 5G and beyond, wireless networks, IoT data privacy/security, and smart grid.
作者簡介(中文翻譯)
Hang Wang 是加州大學戴維斯分校電機與計算機工程系的博士候選人。他在中國科學技術大學（USTC）獲得了工程學士學位。他的研究旨在建立對強化學習、多智能體系統和人機互動的基本理解，以及自動駕駛和邊緣計算等實際應用。他的貢獻已發表於 NeurIPS 和 AAMAS。他最近在 ICML 上的口頭報告中，針對 Warm-start Reinforcement Learning 的研究也引起了關注和讚譽。
Sen Lin 博士是休士頓大學計算機科學系的助理教授。他在亞利桑那州立大學獲得博士學位，並在香港科技大學獲得碩士學位，浙江大學獲得工程學士學位。他的研究興趣廣泛涵蓋機器學習與無線網絡的交集。目前，他的研究專注於持續學習、元學習、強化學習、對抗性機器學習和雙層優化中的算法和理論開發，並應用於多個領域，例如邊緣計算、安全性和網絡控制。
Junshan Zhang 博士是加州大學戴維斯分校電機與計算機工程系的教授。他在普渡大學的電機與計算機工程學院獲得博士學位。他的研究興趣涵蓋信息網絡和數據科學的一般領域，包括邊緣智能、強化學習、持續學習、網絡優化與控制以及博弈論，應用於連接和自動化車輛、5G 及更高版本、無線網絡、物聯網數據隱私/安全和智能電網。
