LibGuides: CSC457: Module 1

Module 1 Required Resources

Mathematical Foundations of Reinforcement Learning
Zhao, S. (2024). Mathematical foundations of reinforcement learning (1st ed.). Springer.

Read Chapter 1: Basic Concepts
Read Chapter 2: State Values and Bellman Equation
These chapters introduce foundational reinforcement learning concepts. Chapter 1 explores basic constructs like states, actions, policies, and rewards, while Chapter 2 dives into state values and the Bellman equation—essential for understanding value-based decision making in RL systems.

View all video lectures in the order listed below.
These lectures are presented by Shiyu Zhao, author of the book Mathematical Foundations of Reinforcement Learning. The slides used in this module are based on content from this book.

Reinforcement Learning
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
This widely-used textbook by Sutton and Barto introduces the core concepts and algorithms of reinforcement learning. The slides and lectures in this module are based on this book.

Module 1 Optional Resources

Reinforcement Learning – Lecture Notes
Auddy, S. (n.d.). Reinforcement learning – Lecture notes. Retrieved from https://sayantanauddy.github.io/assets/pdf/rl-notes.pdf
These concise notes by Sayantan Auddy provide a streamlined overview of reinforcement learning topics, ideal for quick reference or review alongside more in-depth texts.