News
The first class takes place Friday, Jaunary 6, 2017.
General Information
Where: McConnell Engineering, room 103When: Friday, 10:05am-12:55pm.
What: The goal of this class is to provide an introduction to reinforcement learning, a very active part of machine learning. Reinforcement learning is concerned with building programs which learn how to predict and act in a stochastic 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 learnign. During this course, we will study theoretical properties and practical applications of reinforcement leanring. We will follwo the second edition of the classic textbook by Sutton & Barto (available online), and supplement it as needed with papers and other materials.
Instructors
Doina Precup
School of Computer Science
Office: McConnell Engineering building, room 111N (left from elevators)
Office Hours: Friday, 1-1:30pm Meetings at other times by appointment only
Phone: (514) 398-6443
E-mail: dprecup@cs.mcgill.ca
Pierre-Luc Bacon
School of Computer Science
Office: McConnell Engineering building, room 107 (right of elevators)
Office Hours: TBA. Meetings at other times by appointment only
E-mail: pbacon@cs.mcgill.ca
References
Required textbook:- Richard S. Sutton and Andrew G. Barto, "Reinforcement learning: An introduction", Second Edition, MIT Press, in preparation
- Csaba Szepesvari, "Algorithms for Reinforcement Learning", Morgan and Claypool, 2010.
- Dimitri Bertsekas and John Tsitsiklis, "Neuro-dynamic programming", Athena Scientific, 1997.
MyCourses will be used only for bulletin board, discussion groups and assignment submission and grading.