Articles liés à Deep Reinforcement Learning: Fundamentals, Research...
Synopsis
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 detailedexplanations.
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
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 includedeep 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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Acheter neuf
Afficher cet article
EUR 162,51
EUR 9,70 expédition depuis Allemagne vers France
Destinations, frais et délaisAutres éditions populaires du même titre
Résultats de recherche pour Deep Reinforcement Learning: Fundamentals, Research...
Image fournie par le vendeur
Deep Reinforcement Learning
Edité par
Springer, Berlin|Springer Nature Singapore|Springer, 2021
ISBN 10 : 9811540977
ISBN 13 : 9789811540974
Neuf
Kartoniert / Broschiert
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. N° de réf. du vendeur 477964095
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Deep Reinforcement Learning
Edité par
Springer Nature Singapore, Springer Nature Singapore Jun 2021, 2021
ISBN 10 : 9811540977
ISBN 13 : 9789811540974
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 detailedexplanations.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. 544 pp. Englisch. N° de réf. du vendeur 9789811540974
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Deep Reinforcement Learning : Fundamentals, Research and Applications
Edité par
Springer Nature Singapore, Springer Nature Singapore, 2021
ISBN 10 : 9811540977
ISBN 13 : 9789811540974
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 detailedexplanations.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. N° de réf. du vendeur 9789811540974
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Deep Reinforcement Learning
Edité par
Springer Nature Singapore, Springer Nature Singapore Jun 2021, 2021
ISBN 10 : 9811540977
ISBN 13 : 9789811540974
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware -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 detailedexplanations.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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 544 pp. Englisch. N° de réf. du vendeur 9789811540974
Quantité disponible : 2 disponible(s)
Image d'archives
Deep Reinforcement Learning: Fundamentals, Research and Applications
Vendeur : Books Puddle, New York, NY, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. 1st ed. 2020 edition NO-PA16APR2015-KAP. N° de réf. du vendeur 26389006131
Quantité disponible : 4 disponible(s)
Image d'archives
Deep Reinforcement Learning: Fundamentals, Research and Applications
impression à la demandeVendeur : Majestic Books, Hounslow, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. Print on Demand. N° de réf. du vendeur 391675116
Quantité disponible : 4 disponible(s)
Image d'archives
Deep Reinforcement Learning: Fundamentals, Research and Applications
impression à la demandeVendeur : Biblios, Frankfurt am main, HESSE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. PRINT ON DEMAND. N° de réf. du vendeur 18389006137
Quantité disponible : 4 disponible(s)