Reinforcement Learning : Theory and Python Implementation / Zhiqing Xiao.

Format:

Book

Author/Creator:

Xiao, Zhiqing, author.

Language:

English

Subjects (All):

Reinforcement learning.

Physical Description:

1 online resource (574 pages)

Edition:

First edition.

Place of Publication:

Springer Nature 2024

Summary:

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.

Contents:

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: Actor–Critic

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 Agent–Environment Interfaces

Chapter 16. Learn from Feedback and Imitation Learning.

Notes:

Description based on print version record.

ISBN:

981-19-4933-6