CMSC389F

CMSC389F at University of Maryland

Reinforcement Learning

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

Instructor Kevin Chen

kev (at) umd.edu

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

Instructor Zack Khan

zack123 (at) umd.edu

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

Notes

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.

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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.

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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
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