I have been teaching a course on reinforcement learning at Westlake University for three years.
This course aims to provide a mathematical but friendly introduction to the fundamental concepts, basic problems, and classical algorithms in reinforcement learning, which is a core discipline in the areas of artificial intelligence.
The topics covered in the course include the Bellman equation, Bellman optimality equation, value iteration/policy iteration algorithms, Monte-Carlo based algorithms, temporal-difference algorithms, function approximation, and policy gradient algorithms.
Along with the teaching, I have been writting a book as the lecture notes for my students.
The book can be found here: https://github.com/MathFoundationRL/Book-Mathmatical-Foundation-of-Reinforcement-Learning
The lecture slides can be found in the same webpage.
The lecture videos are online: https://space.bilibili.com/2044042934
We will keep updating the videos and slides. Please stay tuned.
This website was last updated in Aug 2022
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