000			01831nam a22001697a 4500
020			_a9789811540974
082			_a006.31 _bDON/D
100			_aDong Hao ed.
100			_aDing Zihan ed.
100			_aZhang Shanghang ed.
245			_aDeep Reinforcement Learning _b: Fundamentals, Research and Applications
260			_aSwitzerland _bSpringer _c2020
300			_axxvii, 514p.
520			_aDeep 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.
650			_aMachine Learning
942			_cBK
999			_c1997 _d1997