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Titlebook: Deep Reinforcement Learning; Fundamentals, Resear Hao Dong,Zihan Ding,Shanghang Zhang Book 2020 Springer Nature Singapore Pte Ltd. 2020 Dee

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書目名稱Deep Reinforcement Learning
副標(biāo)題Fundamentals, Resear
編輯Hao Dong,Zihan Ding,Shanghang Zhang
視頻videohttp://file.papertrans.cn/265/264653/264653.mp4
概述Offers a comprehensive and self-contained introduction to deep reinforcement learning.Covers deep reinforcement learning from scratch to advanced research topics.Provides rich example codes (free acce
圖書封面Titlebook: Deep Reinforcement Learning; Fundamentals, Resear Hao Dong,Zihan Ding,Shanghang Zhang Book 2020 Springer Nature Singapore Pte Ltd. 2020 Dee
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 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
出版日期Book 2020
關(guān)鍵詞Deep reinforcement learning; DRL; Deep Learning; Reinforcement Learning; Machine Learning
版次1
doihttps://doi.org/10.1007/978-981-15-4095-0
isbn_softcover978-981-15-4097-4
isbn_ebook978-981-15-4095-0
copyrightSpringer Nature Singapore Pte Ltd. 2020
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Combine Deep ,-Networks with Actor-Criticral networks to approximate the optimal action-value functions. It receives only the pixels as inputs and achieves human-level performance on Atari games. Actor-critic methods transform the Monte Carlo update of the REINFORCE algorithm into the temporal-difference update for learning the policy para
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Challenges of Reinforcement Learning; (2) stability of training; (3) the catastrophic interference problem; (4) the exploration problems; (5) meta-learning and representation learning for the generality of reinforcement learning methods across tasks; (6) multi-agent reinforcement learning with other agents as part of the environment;
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Imitation Learningtential approaches, which leverages the expert demonstrations in sequential decision-making process. In order to provide the readers a comprehensive understanding about how to effectively extract information from the demonstration data, we introduce the most important categories in imitation learnin
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