DDA-4230: Reinforcement Learning

Course Introduction

This course provides a basic introduction to reinforcement learning algorithms and their applications. Topics include:


  1. Assignments (written and coding homework) (30 points).
  2. Midterm exam (20 points).
  3. Final project (50 points).

For the detailed scoring scheme, please check the project introduction below.

Course Arrangement

  1. Lectures.
    • Time: Monday and Wednesday, 3:30 PM - 4:50 PM.
    • Classroom: Bldg 204, Teaching A Building.
  2. Tutorials.
    • Time: Monday, 8:00 PM -8:50 PM
    • Classroom: Bldg 204, Teaching A Building.
  3. Office Hours.
    • Guiliang Liu (Instructor): Monday, 5:00 PM - 6:00 PM, Bldg 204, Teaching A Building.
    • Xu Sheng (TA): Wednesday 7:00 PM -8:00 PM, Room 326, Daoyuan Building.

Important Notes


Some news will be added to here at the student′s request.


  1. Late Policy. A late submission should receive a 10% penalty for each date after the due. Note that the penalty can accumulate until it reaches 100% (late for 10 days). If you need special care (e.g., for surgery and other health problem), DO NOT wait until the last moment, and please let me know in advance (see my contact below).
  2. Late Drop. A late drop from the course is not encouraged. Under special circumstances, students may apply for a late drop, but there is no guarantee that the request can be approved by the school office.
  3. Honesty in Academic Work. The Chinese University of Hong Kong, Shenzhen places very high importance on honesty in academic work submitted by students, and adopts a policy of zero tolerance on academic dishonesty. While academic dishonesty is the overall name, there are several sub-categories can be found at here.

Course syllabus and Timetable

Topics covered will include the following (The instructor will consistently upload slides and the timeline might be changed at the needs from students)):

  1. Week 1 (Sept. 6th) Lecture 0: [Slides].
  2. Week 1 (Sept. 9th) Lecture 1: Markov decision process [Slides] [Notes].
  3. Week 2 (Sept. 11th) Lecture 2: Optimality of MDPs [Slides] [Notes].
  4. Week 2 (Sept. 13th) Lecture 3: Stochastic multi-armed bandits [Slides] [Notes].
  5. Week 3 (Sept. 18th) Lecture 4: Greedy algorithms [Slides] [Notes].
  6. Week 3 (Sept. 20th) Lecture 5: Explore-then-commit algorithms [Slides] [Notes].
  7. Week 3 (Sept. 20th) Lecture 6: UCB algorithms [Slides] [Notes].
  8. Week 4 (Sept. 25th) Lecture 7: Thompson sampling [Slides] [Notes].
  9. Week 4 (Sept. 25th) Lecture 8: Hardness of Bandits [Slides] [Notes].
  10. Week 4 (Sept. 27th) Lecture 9: Discrete MDPs [Slides] [Notes].
Acknowledgement: The teaching materials use resource from [Previous Course].


  1. [Assignment 1].(Due 23:59 PM Oct. 9, 2023.)