Deep Reinforcement Learning: Fundamentals, Research and Applications
暫譯: 深度強化學習：基礎、研究與應用

Dong, Hao, Ding, Zihan, Zhang, Shanghang

Deep Reinforcement Learning: Fundamentals, Research and Applications

相關主題

商品描述

Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance.

Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations.

The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.

商品描述(中文翻譯)

深度強化學習（Deep Reinforcement Learning, DRL）是強化學習（Reinforcement Learning, RL）和深度學習的結合。它能夠解決許多過去對機器來說無法達成的複雜決策任務，並且著名地促成了 AlphaGo 的成功。此外，它在醫療保健、機器人技術、智慧電網和金融等領域開啟了許多新的應用。

本書分為三個主要部分，提供了對 DRL 的全面且自成一體的介紹。第一部分介紹了深度學習、強化學習（RL）的基礎知識以及廣泛使用的深度強化學習方法，並討論其實現。第二部分涵蓋了選定的 DRL 研究主題，這對於希望專注於 DRL 研究的人士非常有用。為了幫助讀者深入理解 DRL 並迅速將技術應用於實踐，第三部分介紹了大量應用案例，例如智能交通系統和學習跑步，並提供詳細的解釋。

本書適合計算機科學的本科生和研究生，他們希望從零開始學習 DRL，實踐其實現，並探索研究主題。它同樣吸引那些沒有強大機器學習背景的工程師和從業者，他們希望快速理解 DRL 的運作方式並在其應用中使用這些技術。

作者簡介

Dr. Hao Dong is currently an Assistant Professor at Peking University. He received his Ph.D. in Computing from Imperial College London in 2019, supervised by Prof. Yike Guo. Hao's research chiefly involves Deep Learning and Computer Vision, with the goal of reducing the amount of data required for learning intelligent systems. He is passionate about popularizing artificial intelligence technologies and established TensorLayer, a deep learning and reinforcement learning library for scientists and engineers, which won the Best Open Source Software Award at ACM Multimedia 2017.
Zihan Ding received his M.Sc. degree in Machine Learning with distinction from the Department of Computing, Imperial College London, supervised by Dr. Edward Johns. He holds double Bachelor degrees from the University of Science and Technology of China: in Photoelectric Information Science and Engineering (Physics) and in Computer Science and Technology. His research interests include deep reinforcement learning, robotics, computer vision, quantum computation and machine learning. He has published papers in ICRA, AAAI, NIPS, IJCAI, and Physical Review. He also contributed to the open-source projects TensorLayer RLzoo, TensorLet and Arena.
Dr. Shanghang Zhang is a postdoctoral research fellow in the Berkeley AI Research (BAIR) Lab, the Department of Electrical Engineering and Computer Sciences, UC Berkeley, USA. She received her Ph.D. from Carnegie Mellon University in 2018. Her research interests cover deep learning, computer vision, and reinforcement learning, as reflected in her numerous publications in top-tier journals and conference proceedings, including NeurIPS, CVPR, ICCV, and AAAI. Her research mainly focuses on machine learning with limited training data, including low-shot learning, domain adaptation, and meta-learning, which enables the learning system to automatically adapt to real-world variations and new environments. She was one of the "2018 Rising Stars in EECS" (a highly selective program launched at MIT in 2012, which has since been hosted at UC Berkeley, Carnegie Mellon, and Stanford annually). She has also been selected for the Adobe Academic Collaboration Fund, Qualcomm Innovation Fellowship (QInF) Finalist Award, and Chiang Chen Overseas Graduate Fellowship.

作者簡介(中文翻譯)

侯東博士目前是北京大學的助理教授。他於2019年在倫敦帝國學院獲得計算機博士學位，指導教授為郭怡可教授。侯博士的研究主要涉及深度學習和計算機視覺，目標是減少學習智能系統所需的數據量。他熱衷於普及人工智慧技術，並創立了TensorLayer，這是一個為科學家和工程師設計的深度學習和強化學習庫，該庫在2017年ACM多媒體會議上獲得最佳開源軟體獎。

丁子涵在倫敦帝國學院計算機系獲得優異的機器學習碩士學位，指導教授為愛德華·約翰斯博士。他擁有中國科學技術大學的雙學士學位：光電信息科學與工程（物理）及計算機科學與技術。他的研究興趣包括深度強化學習、機器人技術、計算機視覺、量子計算和機器學習。他在ICRA、AAAI、NIPS、IJCAI和《物理評論》上發表了多篇論文，並參與了開源項目TensorLayer RLzoo、TensorLet和Arena的貢獻。

張尚航博士是美國加州大學伯克利分校電機工程與計算機科學系伯克利人工智慧研究（BAIR）實驗室的博士後研究員。她於2018年在卡內基梅隆大學獲得博士學位。她的研究興趣涵蓋深度學習、計算機視覺和強化學習，並在多個頂級期刊和會議論文集中發表了大量論文，包括NeurIPS、CVPR、ICCV和AAAI。她的研究主要集中在有限訓練數據下的機器學習，包括低樣本學習、領域適應和元學習，使學習系統能夠自動適應現實世界的變化和新環境。她曾被評選為「2018年電機工程與計算機科學新星」（這是一個自2012年在麻省理工學院啟動的高度選拔計劃，至今每年在加州大學伯克利分校、卡內基梅隆大學和史丹佛大學舉辦）。她還獲得了Adobe學術合作基金、Qualcomm創新獎學金（QInF）決賽入圍獎和蔣震海外研究生獎學金。

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