Reinforcement Learning
Theory and Python Implementation
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Beschreibung
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.
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.
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 models such as PPO, RLHF, PbRL, and IRL Provides coding examples in all chapters, and all deep RL implementations have both TensorFlow and PyTorch versions
Autor*in
Zhiqing Xiao
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“The book is an excellent resource for anyone looking to explore the world of reinforcement learning (RL). This book combines theoretical depth with practical implementation, making it a standout choice for students, researchers, and industry professionals alike. ... The book is a comprehensive guide that balances theoretical rigor with practical usability.” (Catalin Stoean, zbMATH 1562.68008, 2025)
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