Introduction to reinforcement learning (RL).
Previous semesters: S23, S21, F19.
Also see CS 542 for a more theoretical version of the course.
All slides and notes will be found on this website.
| Date | Lecture | Comments |
|---|---|---|
| 01/16 | Introduction | slides |
| 01/18 | MDP formulation | slides |
| 01/23 | Value function | note, meet over zoom |
| 01/25 | Value function (cont.) | |
| 01/30 | Optimality | |
| 02/01 | Value iteration | |
| 02/06 | VI (cont.) | |
| 02/08 | Policy iteration | |
| 02/13 | PI (cont.) | HW1 due before class |
| 02/15 | Learning settings | slides |
| 02/20 | Value prediction | slides, reading: Sec 3.1 of Szepesvári |
| 02/22 | TD | annotated slides (updated: 02/27) |
| 02/27 | TD lambda | HW2 due before class |
| 02/29 | Function approximation | slides, annotated, reading: Sec 3.2 of Szepesvári |
| 03/05 | Control | slides, reading: Sutton & Barto, Chap 10 |
| 03/07 | Off-policy learning | annotated slides, project proposal due EOW |
| 03/19 | Importance sampling | reference, slides |
| 03/26 | Policy Gradient | slides (updated: 03/28), HW3 due |
| 03/28 | PG | |
| 04/02 | State abstraction | slides |
| 04/04 | Abstraction | ref |
| 04/09 | In-class Office Hour | HW4 due, Practice exam released |
| 04/11 | In-class Office Hour | Final exam in the week after |
| 04/15 | Exam | Start 1:30pm, 1404 SC, Monday |
| 04/18 | Exploration | slides |
| 04/23 | Partial observability | slides |
| 04/25 | Bayesian RL | slides |
| 04/30 | Imitation learning | slides, 4 credit project due 05/05 EOD |
Prerequisites
Linear algebra, probability & statistics, and basic calculus. Experience with machine learning (e.g., CS 446) highly recommended.
Time & Location
Tue & Thu, 2-3:15pm. 1306 Everitt Lab.
Lecture recording
Please see this channel on Mediaspace. You can subscribe to it to be automatically notified of new recordings.
Canvas
Canvas will be the main platform for handling homework assignments and discussions. All announcements (including Assignments when they are created) will be made through Canvas, so make sure you turn on system notification emails. For students who registered later than Jan 10: please contact the TAs to be added to Canvas.
TAs & Office Hours
The-Anh Vu-Le and Rohan Deb. See Canvas announcement for OH.
Textbook
We will not follow a specific textbook, but readings may be assigned based on the following textbooks whose pdfs are freely available online.
Coursework & Grading
For 3 credit students: Your grade will consist of 2 components:
For 4 credit students: You will need to additionally work on a final project (20%; the points of other components will be reduced proportionally). You can either work on your own or work in a team of size 2. The project should be about reproducing the theoretical analysis or the empirical experiments of a published paper on RL; you do not need to reproduce the full paper and can be selective about which part you work on. You are expected to discuss with me the choice of topic in the middle of the semester. For those who want to work on theory, please refer to the CS542 site for the guidelines (though you are expected to spend less effort than the CS542 project) and the list of seed papers.
Academic Integrity
Jeff Erickson has a good page on this. TL;DR from him: “Be honest. Cite your sources. We mean it. If you need help, please ask.”
Late Policy
Late homework will not be accepted. Instead, your lowest homework score will be dropped. Additional late-exceptions will only be granted in a case-by-case manner when compelling reasons are presented (e.g., documented emergencies).
Disability
Please let me know as soon as possible if you need accommodations for disability.
Tentative List of Topics