CMSC389F at University of Maryland
Reinforcement Learning
Lectures: F 12:00-12:50 p.m., 3118 Csic
Instructor Kevin Chen
kev (at) umd.edu
Instructor Zack Khan
zack123 (at) umd.edu
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 6 Overview
DP Methods: Value and Policy Iteration
Week 9 Overview
Temporal Difference 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.
- Problem Set 01: Reinforcement Learning Basics ( TeX)
- Problem Set 02: Python and Colaboratory
- Problem Set 03: Decisions and MDPs
- Problem Set 04: Discounting
- Problem Set 05: Value Functions
- Problem Set 06:Value and Policy Iteration
- Problem Set 07: Open AI Gym and Random Policy
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Lecture Slides
Slides generally follow the notes on a weekly basis. See Syllabus for more information.
- Slides 01: Reinforcement Learning
- Slides 02: Markov Decision Processes
- Slides 06: Value and Policy Iteration
- Slides 07: Monte Carlo
- Slides 08: TD Learning
- Slides 09: TD Lambda + Q Learning Intro
- Slides 10: Q Learning
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