Introduction to reinforcement learning (RL). See CS598 for a more theoretical version of the course here
All slides, notes, and deadlines will be found on this website.
| Date | Lecture | Comments |
|---|---|---|
| 01/26 | Introduction to the course | slides |
| 01/28 | Intro, MDP basics | slides, blackboard |
| 02/02 | Value function and Bellman equation | annotated slides (updated: 02/04) |
| 02/04 | Bellman equation | see updated slides above |
| 02/09 | Formulating problems as MDPs | reading |
| 02/11 | Value Iteration | blackboard |
| 02/16 | VI (cont) | blackboard |
| 02/18 | Policy Iteration | blackboard, HW1 due |
| 02/23 | PI, LP | blackboard |
| 02/25 | Learning settings | slides, blackboard |
| 03/02 | MC value prediction | slides (updated: 03/06), reading: Sec 3.1 of Szepesvári |
| 03/04 | TD(0) and TD(lambda) | |
| 03/09 | Function Approximation | slides (updated: 03/11), HW2 due |
| 03/11 | TD w/ FA | |
| 03/16 | Control & Off-policy | slides |
| 03/23 | Importance Sampling | blackboard (updated: 03/25), reference (advanced material in this note is not covered), ref slides (not used in lecture) |
| 03/25 | IS, PG | blackboard (updated: 04/01), HW3 due EOD 03/27 |
| 03/30 | PG | ref slides |
| 04/01 | PG | |
| 04/06 | Abstraction | slides (updated: 04/08) |
| 04/08 | Abstraction | ref notes, Hw4 due 04/12 EOD |
| 04/13 | No instruction day | |
| 04/15 | Take home exam | |
| 04/20 | Exploration | slides |
| 04/22 | Exploration | |
| 04/27 | Partial Observability | slides |
| 04/29 | Bayesian RL | slides |
| 05/04 | Imitation Learning | slides |
| 05/06 | No class (end of semester) | 4 credit report due |
Prerequisites
Linear algebra, probability & statistics, and basic calculus. Experience with machine learning (e.g., CS 446) highly recommended.
Campuswire (tentative)
Please self-enroll here. Code 6078.
Time & Location
Tue & Thu, 2-3:15pm. Zoom link TBA.
TAs & Office Hours
Jinglin Chen and Jiawei Huang. OH TBA.
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 CS598 site for the guidelines (though you are expected to spend less effort than the CS598 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.
Textbook
We will not follow a specific textbook, but readings may be assigned based on the following textbooks whose pdfs are freely available online.
Tentative List of Topics