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Titlebook: Learning to Play; Reinforcement Learni Aske Plaat Textbook 2020 Springer Nature Switzerland AG 2020 Deep Learning.Machine Learning.Reinforc

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發(fā)表于 2025-3-21 17:38:04 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Learning to Play
副標題Reinforcement Learni
編輯Aske Plaat
視頻videohttp://file.papertrans.cn/584/583005/583005.mp4
概述Author takes as inspiration breakthroughs in game playing, and using two-agent games to explain the full power of deep reinforcement learning.Suitable for advanced undergraduate and graduate courses i
圖書封面Titlebook: Learning to Play; Reinforcement Learni Aske Plaat Textbook 2020 Springer Nature Switzerland AG 2020 Deep Learning.Machine Learning.Reinforc
描述In this textbook the author takes as inspiration recent breakthroughs in game playing to explain how and why deep reinforcement learning works. In particular he shows why two-person games of tactics and strategy fascinate scientists, programmers, and game enthusiasts and unite them in a common goal: to create artificial intelligence (AI).?.After an introduction to the core concepts, environment, and communities of intelligence and games, the book is organized into chapters on reinforcement learning, heuristic planning, adaptive sampling, function approximation, and self-play. The author takes a hands-on approach throughout, with Python code examples and exercises that help the reader understand how AI learns to play. He also supports the main text with detailed pointers to online machine learning frameworks, technical details for AlphaGo, notes on how to play and program Go and chess, and a comprehensive bibliography..The content is class-tested and suitable for advanced undergraduate and graduate courses on artificial intelligence and games. It‘s also appropriate for self-study by professionals engaged with applications of machine learning and with games development. Finally it‘s
出版日期Textbook 2020
關(guān)鍵詞Deep Learning; Machine Learning; Reinforcement Learning; Artificial Intelligence; Computational Intellig
版次1
doihttps://doi.org/10.1007/978-3-030-59238-7
isbn_softcover978-3-030-59240-0
isbn_ebook978-3-030-59238-7
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Aske PlaatAuthor takes as inspiration breakthroughs in game playing, and using two-agent games to explain the full power of deep reinforcement learning.Suitable for advanced undergraduate and graduate courses i
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Reinforcement Learning,The field of reinforcement learning studies the behavior of agents that learn through interaction with their environment. Reinforcement learning is a general paradigm, with links to trial-and-error methods and behavioral conditioning studies. In this chapter we will introduce basic concepts and algorithms that will be used in the restof the book.
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發(fā)表于 2025-3-22 21:47:51 | 只看該作者
Heuristic Planning,Combinatorial games have been used in AI to study reasoning and decision making since the early days of AI. An important challenge in decision making is how tosearch large state spaces efficiently.
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