The course starts on Thursday, January 4. The lectures are also broadcast and recorded via zoom, but questions are only taken in person. The recordings are available in MyCourses.

General Information

When: Tuesday and Thursday, 4:05-5:25pm

Where: Trottier 0100

What: The goal of this class is to provide an introduction to reinforcement learning, a very active sub-field of machine learning. Reinforcement learning is concerned with building computer agents that learn how to predict and act in a stochastic, dynamic environment, based on past experience. Applications of reinforcement learning range from classical control problems, such as power plant optimization or dynamical system control, to game playing, inventory control, and many other fields. Notably, reinforcement learning has also produced very compelling models of animal and human learning. During this course, we will study theoretical properties and practical applications of reinforcement leanring. We will start by following the second edition of the classic textbook by Sutton & Barto (available online), but we will supplement it with papers and other materials.


Doina Precup
School of Computer Science
Office Hours: See MyCourses

Isabeau Prémont-Schwarz
School of Computer Science
Office Hours: See MyCourses

Teaching assistants

Mark Bai

Ali Karami

Ziyan (Ray) Luo

Gandharv Patil

Shuyuan Zhang

Office Hours: See MyCourses


Required textbook: Lecture notes and other relevant materials are linked to the schedule web page.

MyCourses will be used for bulletin board, access to Ed, assignment submission and grading.