The first class takes place Monday, January 6, 2020.

General Information

Where: Trottier (ENGTR) 0100

When: Monday and Wednesday, 8:35-9:55am

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 programs 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 powerplant 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 follow 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: McConnell Engineering building, room 111N (left from elevators)
Office Hours: Monday and Wednesday, 10:00-11:00am Phone: (514) 398-6443

Teaching assistants

Nishanth Anand
Office hours: TBD

Veronica Chelu
Office hours: TBD

Khimya Khetarpal
Office hours: TBD


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

MyCourses will be used only for bulletin board, discussion groups and assignment submission and grading.