MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning

Project Overview

This repository hosts the LaTeX source code for the slide deck of a book titled Mathematical Foundations of Reinforcement Learning, authored by S. Zhao and published by Springer. The slides are created with LaTeX/Beamer and are available to professors for course preparation upon request.

Purpose

The book aims to provide a mathematical but friendly introduction to reinforcement learning (RL). It focuses on fundamental concepts, basic problems, and classic algorithms, explaining not just how algorithms work but why they are designed that way and why they are effective.

Content & Structure

The book contains ten chapters divided into two parts:

• Part 1: Basic tools
• Part 2: Algorithms

Chapters are highly correlated, so earlier chapters should be studied first. The book uses a grid world example throughout to illustrate concepts and algorithms, making it easy to follow.

Key Features

• Mathematical but friendly: Mathematics is presented at a controlled depth, with optional gray-box materials for deeper exploration.
• Illustrative examples: All examples use a simple grid world task.
• Core idea separation: Algorithms are introduced by separating the core idea from distracting complications.
• Coherent organization: Each chapter builds on the previous one.

Target Audience

Designed for senior undergraduates, graduate students, researchers, and practitioners interested in RL. No prior RL background is required, but readers should have some knowledge of probability theory and linear algebra (basics are included in the appendix).

Author

S. Zhao – a professor who has been teaching a graduate-level RL course since 2019. The book originated as lecture notes for that course. More info is available on his homepage and research group website.

Supplementary Resources

• Lecture videos: The author provides free lecture videos (Chinese and English) on YouTube and Bilibili, which have received over 2.1 million views. They complement the book.
• Third-party code and notes: Many readers have contributed Python, MATLAB, R, and C++ implementations of the book's algorithms, as well as study notes in English and Chinese. These are linked in the README, but the author has not verified them.
• Grid-world environment code: The author provides code for the grid-world environment (Python and MATLAB) so readers can test their own algorithms.

Notable Updates

The README tracks the book's progress:

• July 2025: Typo corrections and an errata list for the printed edition.
• June 2025: Reached 10,000+ GitHub stars.
• Oct 2024: Book cover finalized; published by Springer and Tsinghua University Press.
• Aug 2024: 3,000+ stars; more third-party code added.
• Apr 2024: Grid-world environment code released.
• Mar 2024: 2,000 stars; minor updates.
• Sep 2023: 1,000+ stars.
• Aug 2023: Major update – second version of the draft.

Citation

@book{zhao2025RLBook,
  title={Mathematical Foundations of Reinforcement Learning},
  author={S. Zhao},
  year={2025},
  publisher={Springer Press}
}

All information above is derived solely from the provided README.