CMSC389F at University of Maryland

Reinforcement Learning

Lectures: F 12:00-12:50 p.m., 3118 Csic

Instructor Kevin Chen

kev (at)

Office Hours: Tu/Th 2-3 pm. 3118 Csic

Instructor Zack Khan

zack123 (at)

Office Hours: Tu/Thr 2-3 pm. 3118 Csic

Week 1 Overview

Introduction to Reinforcement Learning

Week 2 Overview

OpenAI Gym & Basic Techniques

  • Note 4: Setting up OpenAI Gym
  • Note 5: The Pong Example, in Practice
  • Problem Set 02

Week 3 Overview

Markov Decision Processes

  • Note 6 : Agents, Environments, and Rewards
  • Note 7 : Markov Decision Processes
  • Problem Set 03


There is no textbook for this class. Instead, there is a set of fairly comprehensive lecture notes. Make sure you revisit the notes after lecture. Each note may be covered in one or more lectures. See Syllabus for more information.


Problem Sets

All problem-sets are graded for completion and it is highly-recommended that you do them. Late submissions are deducted 10% the following day, and any later submissions are not accepted. See Syllabus for more information.


Lecture Slides

Slides generally follow the notes on a weekly basis. Lecture videos will be provided. See Syllabus for more information.

  • Lecture 01: Introduction to Reinforcement Learning
  • Lecture 02: OpenAI Gym & Basic Techniques
  • Lecture 03: Markov Decision Processes