About this Course

You should take this course if you have an interest in machine learning and the desire to engage with it from a theoretical perspective. Through a combination of classic papers and more recent work, you will explore automated decision-making from a computer-science perspective. You will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience. At the end of the course, you will replicate a result from a published paper in reinforcement learning.

Course Cost
Free
Timeline
Approx. 4 months
Skill Level
Advanced
Included in Course
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  • Icon course 04 2edd94a12ef9e5f0ebe04f6c9f6ae2c89e5efba5fd0b703c60f65837f8b54430 Interactive Quizzes

  • Icon course 02 2d90171a3a467a7d4613c7c615f15093d7402c66f2cf9a5ab4bcf11a4958aa33 Taught by Industry Pros

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Join the Path to Greatness

This free course is your first step towards a new career with the Machine Learning Engineer Nanodegree Program.

Free Course

Reinforcement Learning

by Georgia Institute of Technology

Enhance your skill set and boost your hirability through innovative, independent learning.

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Course Leads

  • Charles Isbell
    Charles Isbell

    Instructor

  • Michael Littman
    Michael Littman

    Instructor

  • Chris Pryby
    Chris Pryby

    Instructor

What You Will Learn

  • Reinforcement Learning Basics
  • Introduction to BURLAP
  • TD Lambda
  • Convergence of Value and Policy Iteration
  • Reward Shaping
  • Exploration
  • Generalization
  • Partially Observable MDPs
  • Options
  • Topics in Game Theory
  • Further Topics in RL Models

Prerequisites and Requirements

Before taking this course, you should have taken a graduate-level machine-learning course and should have had some exposure to reinforcement learning from a previous course or seminar in computer science (students who have completed CS 7641 will be well prepared for this course).

Additionally, you will be programming extensively in Java during this course. If you are not familiar with Java, we recommend you review Udacity's Intro to Java Programming course materials to get up to speed beforehand.

See the Technology Requirements for using Udacity.

Why Take This Course

This course will prepare you to participate in the reinforcement learning research community. You will also have the opportunity to learn from two of the foremost experts in this field of research, Profs. Charles Isbell and Michael Littman.

What do I get?
  • Instructor videos
  • Learn by doing exercises
  • Taught by industry professionals

Thanks for your interest!

We'll be in touch soon.

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