CS 443 Reinforcement Learning (S23)

Introduction to reinforcement learning (RL).

Previous semesters (as CS 498): S21, F19.
Also see CS 542 for a more theoretical version of the course.

All slides, notes, and deadlines will be found on this website.


Date Lecture Comments
01/17 Introduction slides
01/19 MDP formulation slides
01/24 Value function note

Linear algebra, probability & statistics, and basic calculus. Experience with machine learning (e.g., CS 446) highly recommended.

Lecture recording
Please see this channel on Mediaspace. You can subscribe to it to be automatically notified of new recordings.

Here. All announcements (including Assignments when they are created) will be made through Canvas, so make sure you can receive system notification emails from it.

Time & Location
Tue & Thu, 2-3:15pm. 1306 Everitt Lab.
In special circumstances we will meet over zoom instead (will be announced in advance); see Canvas announcement for the zoom link.

TAs & Office Hours
Audrey Huang and Philip Amortila. OH TBA.

Coursework & Grading
For 3 credit students: Your grade will consist of 2 components:

  • Homework (~70%): There will be roughly 5 homework assignments, including both written and coding assignments.
  • Final/Late Mid Exam (~30%): There will be a final exam (or a mid exam that is relatively late). Date TBA.

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).

Please let me know as soon as possible if you need accommodations for disability.

We will not follow a specific textbook, but readings may be assigned based on the following textbooks whose pdfs are freely available online.

  • Reinforcement Learning: An Introduction, by Rich Sutton and Andrew Barto. (draft available online)
  • Algorithms of Reinforcement Learning, by Csaba Szepesvári. (pdf available online)

Tentative List of Topics

  • MDP basics.
  • Planning: value iteration, policy iteration, and their analyses.
  • Model-based and value-based learning algorithms: certainty-equivalence, Q-learning, TD.
  • Policy gradient.
  • Importance sampling and off-policy evaluation.
  • State abstractions.
  • Partial observability.