| 書目名稱 | Reinforcement Learning | | 副標題 | Theory and Python Im | | 編輯 | Zhiqing Xiao | | 視頻video | http://file.papertrans.cn/826/825929/825929.mp4 | | 概述 | Introduces not only algorithms and mathematical theory behind them, but also implementation details and usage examples.Covers both classical and modern RL algorithms, including algorithms for large mo | | 圖書封面 |  | | 描述 | .Reinforcement Learning: Theory and Python Implementation. is a tutorial book on reinforcement learning, with explanations of both theory and applications. Starting from a uniform mathematical framework, this book derives the theory of modern reinforcement learning systematically and introduces all mainstream reinforcement learning algorithms such as PPO, SAC, and MuZero. It also covers key technologies of GPT training such as RLHF, IRL, and PbRL. Every chapter is accompanied by high-quality implementations, and all implementations of deep reinforcement learning algorithms are with both TensorFlow and PyTorch. Codes can be found on GitHub along with their results and are runnable on a conventional laptop with either Windows, macOS, or Linux...This book is intended for readers who want to learn reinforcement learning systematically and apply reinforcement learning to practical applications. It is also ideal to academical researchers who seek theoretical foundation or algorithm enhancement in their cutting-edge AI research.. | | 出版日期 | Book 2024 | | 關(guān)鍵詞 | Reinforcement Learning; Deep Reinforcement Learning; Machine Learning; Artificial Intelligence; Python I | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-19-4933-3 | | isbn_softcover | 978-981-19-4935-7 | | isbn_ebook | 978-981-19-4933-3 | | copyright | Beijing Huazhang Graphics & Information Co., Ltd, China Machine Press 2024 |
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