Reinforcement Learning (Fall 2025)

Reinforcement Learning (Fall 2025)



Course Information

Overview

Reinforcement learning (RL) is a powerful learning paradigm through which machines learn to make (sequential) decisions. It has been playing a pivotal role in advancing artificial intelligence, with notable successes including mastering the game of Go and enhancing large language models.

This course focuses on the design principles of RL algorithms. Similar to statistical learning, a central challenge in RL is to generalize learned capabilities to unseen environments. However, RL also faces additional challenges such as exploration-exploitation tradeoff, credit assignment, and distribution mismatch between behavior and target policies. Throughout the course, we will delve into various solutions to these challenges and provide theoretical justifications.

Prerequisites

Probability, linear algebra, calculus, machine learning, python programming.

Grading

Late policy for assignments: 10 free late days can be used across all assignments. Each additional late day will result in a 10% deduction in the semester’s assignment grade. No assignment can be submitted more than 7 days after its deadline.

Schedule

Date Topics Materials Assignments
8/27      
8/29      
9/1 Labor Day    
9/3      
9/5      
9/8      
9/10      
9/12      
9/15      
9/17      
9/19      
9/22      
9/24      
9/26      
9/29      
10/1      
10/3      
10/6      
10/8      
10/10      
10/13 Reading Day    
10/15      
10/17      
10/20      
10/22      
10/24      
10/27      
10/29      
10/31      
11/3      
11/5      
11/7      
11/10      
11/12      
11/14      
11/17      
11/19      
11/21      
11/24 Thanksgiving Recess    
11/26 Thanksgiving Recess    
11/28 Thanksgiving Recess    
12/1      
12/3      
12/5      
12/8      

Resources