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 5 Overview

## Introduction to OpenAI Gym

- Note X: Introduction to OpenAI Gym (Draft)
- Note X: Brute Force Policy Search (Draft)
- Problem Set 05 (Draft)

## Notes

- Note 1: Reinforcement Learning
- Note 2: Markov Chains and Markov Reward Processes
- Note 3: Markov Decision Processes
- Note 4: Value and Policy Iteration

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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.

- Problem Set 01: Reinforcement Learning Basics ( TeX)
- Problem Set 02: Python and Colaboratory
- Problem Set 03: Decisions and MDPs
- Problem Set 04: Discounting

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## Lecture Slides

Slides generally follow the notes on a weekly basis. See Syllabus for more information.

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