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