Summary

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.

Expected Learning

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.

Syllabus

• 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

Required Knowledge

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.