CMPS 4660/6660: Reinforcement Learning - Fall 2020

Instructor: Zizhan Zheng (, Stanley Thomas 307B
Class Time & Place: TR 12:25PM-01:35PM,  Dinwiddie Hall 103
Office Hours: Wed 10-11AM and by appointment

Course Description (Syllabus)

Reinforcement learning (RL) has found successful applications in various domains, including recommender systems, health care, energy, finance, robotics, transportation, and computer systems. Many people believe that RL is a step toward Artificial General Intelligence (AGI). This course introduces both the classic results and state-of-the-art research in RL at the graduate level. We will cover both the theoretical foundation of RL and its applications through case studies. Topics to be covered include:

Course Materials

Class Meeting Plan

We will meet both in person and online (about 40% of lectures will be online). A detailed class schedule can be found on the course webpage. To compensate for the shortened class time, extra reading and discussion material will be assigned on Canvas.

Homework Assignments  

There will be both written problem assignments and labs (programming assignments). Graduate students will be given extra questions that require advanced algorithmic/analytic techniques. Specific instructions will be given in each assignment. All the assignments will be posted on the course webpage.

Midterm Exam

The midterm will be closed-book and closed-notes, but you will be allowed to bring a cheat sheet to each exam (one letter page single-sided).  A different set of questions will be given to undergraduate and graduate students, respectively.

Final Project

Students will work in groups on a final project. Each group should include up to two members. The project should center on a well-defined problem related to reinforcement learning and (ideally) your specific research area. You will develop the project through close interactions with the instructor and your peers and write a paper that has all the sections of a typical research paper including some preliminary results.

A couple of milestone presentations will be scheduled during the semester and the final presentation will be in the final exam week (Nov. 30 – Dec. 5). The final paper is due after the final presentation. A tentative schedule for the final project can be found on the course website.

Late Policy

Each student has a total of 6 grace days that may be applied to the homework assignments. No more than 2 grace days may be used on any single assignment. Any assignment submitted more than 2 days past the deadline (or the date the student no longer has late day credit) will get zero credit. No late days are allowed for the final presentation and report.

Attendance Policy

Faculty and students must comply with University policies on COVID-19 testing and isolation, which are located here[]. Faculty and students must wear face coverings in all common areas, including classrooms, and follow social distancing rules. Failure to comply is a violation of the Code of Student Conduct and students will be subject to University discipline, which can include suspension or permanent dismissal.

If a student cannot attend class for any reason, the student is responsible for communicating with their instructor to make up any work they may miss. Faculty will provide online options for class participation, outlined in this document, and unless a student is seriously ill, they are expected to use this option. The University Health Center will provide documentation verifying a student is ill, as well as verification that a student may return to class. With the approval of the Newcomb-Tulane College dean, an instructor may have a student who has excessive absences involuntarily withdrawn from a course with a WF grade after written warning at any time during the semester.

Grading Policy

The weighted average will determine your letter grade roughly as follows:
A  >= 90%; B  >= 80%; C  >= 70%; D  >= 60%; F  < 60%
+/- grades will be given for borderline cases.

All grades will be posted on Canvas.

Class Schedule & Handouts

Acknowledgment: many slides are adapted from Richard Sutton's RL slides, David Silver's RL course, and Berkeley CS 285.

Lecture Date Topic Lecture Topic Reading Assignments
1 Aug 20 (R) Introduction
Logistics; Intro to RL[pdf] SB 1.1-1.5
Probability review
Linear algebra review
Forming groups
(due Sep 1)
Aug 25 (T)
Markov decision processes and dynamic programming
Markov Reward Processes; Episodic and continuing tasks SB 3.1-3.5, DB 4.1
Aug 27 (R)
Finite MDP
SB 3.1-3.5, CS 2.1-2.2, DB 4.2
Sep 1 (T) Bellman equations SB 3.6, CS 2.3, DB 4.3 Homework 1 (due Sep 10)
Sep 3 (R)
Bellman optimality equation [pdf] SB 3.6, CS 2.4
Sep 8 (T)
Contractions and fixed point theorem; DP for prediction  CS 2.4, A.1-A.2
SB 4.1-4.2

Sep 10 (R)
Value iteration SB 4.3-4.7 Homework 2 (due Sep 22)

Sep 15 (T)
Class cancelled
Sep 17 (R)
Policy iteration SB 4.3-4.7
Sep 22 (T)

Model-free prediction
and control

  LP approach for MDP, POMDP [pdf];
Monte Carlo prediction
SB 17.3, 5.1-5.2, CS 3.1
Sep 24 (R)
Student presentations: project proposal
Lab 1 (due Oct 6)
Sep 29 (T)
Stochastic approximation, TD(0) SB 6.1-6.3, CS 3.1
Oct 1 (R)
TD(0) SB 6.1-6.3, CS 3.1
Oct 6 (T)
n-step TD, TD(λ)  SB 7.1, 12.1-12.2, CS 3.1 Homework 3 (due Oct 13)
Oct 8 (R)
TD(λ) [pdf]
Monte Carlo control
SB 5.3-5.7
Oct 11 (U) Sarsa; Midterm review [pdf] SB 6.4
Oct 13 (T) Q-learning [pdf] SB 6.5-6.7
Oct 15 (R)
Midterm: Thursday, Oct 15
Oct 20 (T) Approximation solution methods
On-policy prediction SB 9.1-9.2, CS 3.2
Oct 22 (R)
 On-policy prediction SB 9.3-9.4, CS 3.2
Oct 27 (T)
 On-policy control;
Off-policy methods
SB 9.5, 9.8, 11.1-11.3

Oct 29 (R)
Class cancelled
Nov 3 (T)
Batch methods, DQN [pdf] SB 16.5, 13
Lab 2
Nov 5 (R)
Student presentations: project update
Nov 7 (S)
Policy gradients SB 13
Nov 10 (T)
Policy gradients SB 13

Nov 12 (R)
Mini-lectures Arie, Eli, and Sri: Deep Q-Networks
Farzad and Tianyi: Multi-armed bandits for wireless network
Nov 17 (T)


DDPG [pdf], model-based RL Lillicrap, et al., “Continuous control with deep reinforcement learning”, ICLR, 2016;
SB 8

Nov 19 (R)
Mini-lectures Ningxiao and Xiaolin: Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations
Henger: Convergence of Q-learning
Nov 24 (T)
Dyna, Rollout, Monte Carlo tree search [pdf] SB 8, 16.6

Final presentations: Wednesday, Dec 2, 4:00-6:00pm
Final report: Friday, Dec 4, 11:59pm

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