| 书目名称 | Learning to Play | | 副标题 | Reinforcement Learni | | 编辑 | Aske Plaat | | 视频video | | | 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 | | 图书封面 |  | | 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 | | 关键词 | Deep Learning; Machine Learning; Reinforcement Learning; Artificial Intelligence; Computational Intellig | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-59238-7 | | isbn_softcover | 978-3-030-59240-0 | | isbn_ebook | 978-3-030-59238-7 | | copyright | Springer Nature Switzerland AG 2020 |
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发表于 2025-3-21 17:38:04
