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Reinforcement Learning

Free Course

Offered at Georgia Tech as CS 8803

Related Nanodegree Program

Deep Reinforcement Learning

In collaboration with
  • Georgia Institute of Technology

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.

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.

Additionally, you will be programming extensively in Java during this course. If you are not familiar with Java, we recommend you review Udacity's Object Oriented Programming in Java 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.

Learn with the best.

  • Charles Isbell
    Charles Isbell


  • Michael Littman
    Michael Littman


  • Chris Pryby
    Chris Pryby