MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
From the publisher
README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
If you are a professor and preparing a course and would like to use any content from my slides, feel free to reach out by email. I can share the source code with you. The slides were created using Latex/Beamer. Regarding reader feedback and questions in the discussion section, please note that due to a high volume of commitments, there may be significant delays in my response. Your understanding would be greatly appreciated. This book aims to provide a mathematical but friendly introduction to the fundamental concepts, basic problems, and classic algorithms in reinforcement learning. Some essential features of this book are highlighted as follows. [](https://link.springer.com/book/9789819739431) The topics addressed in the book are shown in the figure below. This book contains ten chapters, which can be classified into two parts: the first part is about basic tools, and the second part is about algorithms. The ten chapters are highly correlated.…
Read the full README ↗ · Preview checked 2026-10-27T20:18:46.468Z
Inside the original README — Document outline
- About the Latex source code of my slides
- Why a new book on reinforcement learning?
- Contents
- Readership
- About the author
- Citation
- Lecture videos
- Third-party code and materials
- Update history
Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.
Links from the README
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- ![Book cover
- ![
- L2: Bellman Equation (P1-Motivating examples)
- L3: Bellman Optimality Equation (P1-Motivating example)
- L5: Monte Carlo Learning (P1-Motivating examples)
- L5: Monte Carlo Learning (P3-MC Basic-examples)
- L5: Monte Carlo Learning (P6-MC Epsilon-Greedy-examples)
- L6: Stochastic Approximation and SGD (P1-Motivating example)
- L6: Stochastic Approximation and SGD (P5-SGD algorithm: examples)
- L7: Temporal-Difference Learning (P1-Motivating example)
- L8: Value Function Approximation (P1-Motivating example–curve fitting)
- L8: Value Function Approximation (P4-illustrative examples and analysis)
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What this repository does
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
Repository facts
- Owner
- MathFoundationRL
- Primary language
- MATLAB
- Stars
- 18,380
- Forks
- 1,720
- Open issues + pull requests
- 1
- License
- Not reported — inspect the license file
- Archived
- No
- Default branch
- main
- Created
- 2022-08-07T06:22:20.000Z
- Last push
- 2026-10-27T05:47:21.000Z
Topics and intended use
Owner-supplied topics: artificial-intelligence, book, courses, reinforcement-learning, tutorials
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