CMSC389F at University of Maryland

Reinforcement Learning

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

Instructor Kevin Chen

kev (at)

Instructor Zack Khan

zack123 (at)

Week 1 Overview

Introduction to Reinforcement Learning

Week 2 Overview

Reinforcement Learning Framework and Markov Decision Processes

Week 3 Overview

Markov Decision Processes With Gridworld

Week 4 Overview

Discounting and Cumulative Reward

Week 5 Overview

Value Functions

Week 6 Overview

DP Methods: Value and Policy Iteration

Week 7 Overview

Model Free Methods: Monte Carlo

Week 8 Overview

Temporal Difference Learning

Week 9 Overview

Temporal Difference Learning

Week 10 Overview

Q Learning

Problem Sets

It is highly-recommended that you complete the problem-sets. Late submissions are deducted 10% the following day, and any later submissions are not accepted. See Syllabus for more information.