CS 6789: Foundations of Reinforcement Learning
Modern Artificial Intelligent (AI) systems often need the ability to make sequential decisions in an unknown,
uncertain, possibly hostile environment, by actively interacting with the environment to collect relevant data.
Reinforcement Learning (RL) is a general framework that can capture the interactive learning setting and
has been used to design intelligent agents that achieve superhuman level performances on
challenging tasks such as Go, computer games, and robotics manipulation.
This graduate level course focuses on theoretical and algorithmic foundations of Reinforcement Learning. The four main themes of the course are
(1) fundamentals (MDPs, computation, statistics,
generalization) (2) provably efficient exploration (and
high dimensional RL) (3) direct policy optimization
(e.g. policy gradient methods), (4) further topics
(control, offline RL, partial observability, and imitation learning).
After taking this course, students will be able to understand both classic and stateofart provably correct RL algorithms and their analysis. Students will be able to conduct research on RL related topics.

Staff
Instructors: Wen Sun
TAs: Princewill Okoroafor, Dhruv Sreenivas
Lecture time: Tuesday/Thursday 1:002:15pm ET
Office hours: TBD
Location: Upson Hall 216
Contact: cornellcs6789@gmail.com.
Please communicate to the instructors and TA only through this account.
Emails not sent to this list, with regards to the course,
will not be responded to in a timely manner.

Prerequisites
This is an advanced and theoryheavy course: there is no programming assignment and students
are required to work on a theoryfocused course project.
Students need strong grasp on Machine Learning (e.g., CS 4780), Probability and Statistics (e.g., BTRY 3080 or ECON 3130, or MATH 4710), Optimization (e.g., ORIE 3300), and Linear Algebra (e.g., MATH 2940).
The best way to access your background is to check out HW0.
For undergraduate and Meng students enrollment: permission of instructor subject to your performance on HW0.

Grading Policies
Assignments 55% (HW0:10%, HW1HW3: 15% each), Project 40%, Reading 5%, Participation bonus 5%
All homework will be mathematical in nature, focussing on the theory of RL and bandits;
there will not be a programming component.
The entire HW must be submitted in one single typed pdf document (not handwritten).
HW0 is MANDATORY to pass to satisfactory level;
it is to check your knowledge of the prerequisites in probability, statistics, and linear algebra.
Homework Rules:
Homework must be done individually: each student must understand, write, and hand in their own answers. It is
acceptable for students to discuss problems with each other;
it is not acceptable for students to share answers and look at another students written answers.
You must also indicate on each homework with whom you collaborated with and what online resources you used.
Late days: Homeworks must be submitted by the posted due date.
You are allowed up to 6 total LATE DAYs for the homeworks throughout the entire semester (late days do not apply to HW0 and project reports).
These will be automatically deducted if your assignment is late.
For example, any day in which an assignment is late by up to 24 hours,
then one late day will be used. After your late days are used up,
late penalties will be applied: any assignment turned in late will incur a reduction in score by 33% for each late day,
so if an assignment is up to 24 hours late, it incurs a penalty of 33%.
Else if it is up to 48 hours late, it incurs a penalty of 66%.
And any longer, it will receive no credit. We will track all your late days and any deductions will be applied in computing the final grades.
If you are unable to turn in HWs on time, aside from permitted days, then do not enroll in the course.
Participation/extra effort
bonus: We encourage participation including
asking/answering questions in lectures and ED
discussion, and extra effort on reading the book
chapters and lecture notes (e.g., proof reading additional chapters and
sending back comments/feedback).

Reading Assignment
Please sign up for reading materials here.
Reading assignment is done in group (size 2 or 3). Each group will read the assigned chapter in the AJKS book (V3).
You are required to submit a one page report that
summarizes the chapter. In addition to this, the requirement
is that you also carefully read the chapter, checking for errors, typos, and
arguments/explanations that are not clear; please point this
out to the instructors either in a separate page in the report
or via Ed Discussion. The expectation
is that you check all mathematical steps in the
chapter; this gives you an opportunity to obtain a
mastery of the chapter that you choose.

Course Project
Please see the course project page.
It is a course requirement that you be in attendance for
all student presentations. In addition, we ask everyone to block
approximately 2 hours for each of the presentation sessions
(tentatively there will be 3 presentation sessions at the end of the semester)

Diversity in STEM
While many academic disciplines have historically been dominated by one cross section of society,
the study of and participation in STEM disciplines is a joy that the instructors hope that everyone can pursue,
regardless of their socioeconomic background, race, gender, etc.
The instructors encourage students to both be mindful of these issues, and,
in good faith, try to take steps to fix them. You are the next generation here.

Course Notes: RL Theory and Algorithms
The course will be largely based of the working draft of
the book "Reinforcement Learning Theory and
Algorithms", available
here.
We will be updating these notes in AJKS
throughout the course of the term. If you find typos or errors, please let us
know. We would appreciate it!

Schedule (tentative)


Lecture 
Reading 
Slides/HW 
01/24/23 

Fundamentals: Markov Decision Processes 
Ch.1 
Slides, Annotated Slides, HW0 
01/26/23 

Fundamentals: Value Iteration 
Ch.1 
Slides, Annotated Slides 
01/31/23 

Fundamentals: Policy Iteration and LPFormulation 
Ch.1 
Slides, Annotated Slides 
02/02/23 

Fundamentals: Tabular MDP
with a Generative Model 
Ch.2 
Slides, Annotated Slides 
02/07/23 

Exploration: Multiarmed Bandits 



02/09/23 

Exploration: Linear Bandits 

02/14/23 

Exploration: Exploration in tabular MDP 

02/16/23 

No class 

02/21/23 

Exploration: Lower bounds for linear RL 

02/23/23 

Exploration: Exploration in linear MDPs 

02/28/23 

No class (Spring break) 

03/02/23 

Exploration: Representation learning in Lowrank MDPs 

03/07/23 

Exploration: Bilinear class 

03/09/23 

Policy Optimization: Policy gradient and convergence 

03/14/23 

Policy Optimization: Policy gradient global convergence 

03/16/23 

Policy Optimization: Natural Policy Gradient 

03/21/23 

Policy Optimization: NPG global convergence 

03/23/23 

Policy Optimization: NPG with function approximation 

03/28/23 

Offline RL: Fitted Q iteration 

03/30/23 

Offline RL: Partial Coverage and perssimism 

04/04/23 

No class (spring break) 

04/06/23 

No class (spring break) 

04/11/23 

Offline RL: Recent advancements 

04/13/23 

Partial Observability: Learning Hidden Markov Models 

04/18/23 

Partial Observability: Learning Linear Quadratic Gaussians 

04/20/23 

TBD: TBD 

04/25/23 

TBD: TBD 

04/27/23 

TBD 

05/02/23 

Student Presentation 

05/04/23 

Student Presentation 

05/09/23 

Student Presentation 


