Open course
I have been teaching a course on reinforcement learning for five years. Along with my teaching, I developed a textbook and an open course. The development process was very time-consuming. I sincerely hope they are helpful to those who are interested in reinforcement learning!
Lecture Videos
You can check out our YouTube channel. You can also click the following links to be directed to specific lecture videos.
L1: Basic Concepts (P2-Reward,return, Markov decision process)
L4: Value Iteration and Policy Iteration (P1-Value iteration)
L4: Value Iteration and Policy Iteration (P2-Policy iteration)
L4: Value Iteration and Policy Iteration (P3-Truncated policy iteration)
L5: Monte Carlo Learning (P5-MC Epsilon-Greedy-introduction)
L6: Stochastic Approximation and SGD (P1-Motivating example)
L6: Stochastic Approximation and SGD (P2-RM algorithm: introduction)
L6: Stochastic Approximation and SGD (P3-RM algorithm: convergence)
L6: Stochastic Approximation and SGD (P4-SGD algorithm: introduction)
L6: Stochastic Approximation and SGD (P5-SGD algorithm: examples)
L6: Stochastic Approximation and SGD (P6-SGD algorithm: properties)
L6: Stochastic Approximation and SGD (P7-SGD algorithm: comparison)
L7: Temporal-Difference Learning (P2-TD algorithm: introduction)
L7: Temporal-Difference Learning (P3-TD algorithm: convergence)
L7: Temporal-Difference Learning (P5-Expected Sarsa & n-step Sarsa)
L7: Temporal-Difference Learning (P6-Q-learning: introduction)
L7: Temporal-Difference Learning (P7-Q-learning: pseudo code)
L7: Temporal-Difference Learning (P8-Unified viewpoint and summary)
L8: Value Function Approximation (P1-Motivating example – curve fitting)
L8: Value Function Approximation (P3-Optimization algorithm)
L8: Value Function Approximation (P4-illustrative examples and analysis)
L8: Value Function Approximation (P7-DQN – experience replay)
L8: Value Function Approximation (P8-DQN – implementation and example)
L9: Policy Gradient Methods (P5-Gradient-based algorithms & REINFORCE)
The lecture videos of the last chapter (Chapter 10) will be uploaded shortly. Please stay tuned!
Lecture Slides
The slides for the above lecture videos can be found here: https://github.com/MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning/tree/main/Lecture%20slides
Textbook
The GitHub homepage of the textbook is here: https://github.com/MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning
So far, the textbook has received 5,000+ stars on GitHub. Unlike many other books, this book provides a mathematical but friendly introduction to reinforcement learning. Its content structure is also novel. You are welcome to check out!
Lecture Videos (in Chinese)
For Chinese lecture videos, you can check our Bilibili channel or our YouTube channel
So far, the lecture videos have received 1,300,000+ views over the Internet and received very good feedback! You are welcome to check out!