Designing autonomous decision making systems is one of the longstanding goals of Artificial Intelligence. Such decision making systems, if realized, can have a big impact in machine learning for robotics, game playing, control, health care to name a few. This course introduces Reinforcement Learning as a general framework to design such autonomous decision making systems. By the end of this course, you will have a solid knowledge of the core challenges in designing RL systems and how to approach them.

This course will be offered in English. However, the students in this course can submit in English or French any written work that is to be graded.

Quebec university students from outside Polytechnique Montreal can register for the course via Inter-University Transfer Authorization.

Please note that I will also be teaching Machine Learning (ML) (INF8245E) this Fall. You can take both the courses (ML and RL) in parallel.

If you are a student at Poly, UdeM, HEC, McGill, or Mila, then you can request to audit this course by filling this Google Form.

General Information

When?
Mondays 12:45 pm to 3:45 pm (starting from 29 Aug)

Where?
M-1420
There will not be remote option this year.

About Labs
The official slots for lab sessions for this course are either Tuesdays 8:30 am to 11:30 am biweekly or Wednesdays 8:30 am to 11:30 am biweekly depending on your group. However, we will not do regular labs during these slots. We will have few online tutorials during these timeslots. You can use the rest of the lab time to work on the practical assignments by yourself. Additionally, we will have one-hour office hours every day from Monday to Friday where students can ask TAs their doubts about the practical assignments.

People

Instructor

TAs

  • Hadi Nekoei (Lead TA)
  • Gopeshh Subbaraj
  • Harley Wiltzer
  • Michel Ma

Office Hours

Name Day Time Location
Sarath Monday 3.45 PM to 4.45 PM M-3406
Harley Tuesday 4.00 PM to 5.00 PM Link in Moodle
Michel Wednesday 3.00 PM to 4.00 PM Link in Moodle
Gopeshh Thursday 2.00 PM to 3.00 PM Link in Moodle
Hadi Friday 11.00 AM to 12.00 PM Link in Moodle