Reinforcement Learning

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

Auteur

Zhiqing Xiao obtained doctoral degree from Tsinghua University in 2016 and has more than 15 years in academic research and industrial practices on data-analytics and AI. He is the author of two AI bestsellers in Chinese: Reinforcement Learning and Application of Neural Network and PyTorch and published many academic papers. He also contributed to recent versions of the open-source software Gym.

Texte du rabat

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.

Contenu

Chapter 1. Introduction of Reinforcement Learning (RL).- Chapter 2. MDP: Markov Decision Process.- Chapter 3. Model-based Numerical Iteration.- Chapter 4. MC: Monte Carlo Learning.- Chapter 5. TD: Temporal Difference Learning.- Chapter 6. Function Approximation.- Chapter 7. PG: Policy Gradient.- Chapter 8. AC: ActorCritic.- Chapter 9. DPG: Deterministic Policy Gradient.- Chapter 10. Maximum-Entropy RL.- Chapter 11. Policy-based Gradient-Free Algorithms.- Chapter 12. Distributional RL.- Chapter 13. Minimize Regret.- Chapter 14. Tree Search.- Chapter 15. More AgentEnvironment Interfaces.- Chapter 16. Learn from Feedback and Imitation Learning.