Course Description

To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This class will provide a solid introduction to the field of reinforcement learning and students will learn about the core challenges and approaches, including generalization and exploration. Through a combination of lectures, and written and coding assignments, students will become well versed in key ideas and techniques for RL. Assignments will include the basics of reinforcement learning as well as deep reinforcement learning-- an extremely promising new area that combines deep learning techniques with reinforcemetn learning. In addition, students will advance their understanding and the field of RL through an open ended project.

Learning Objectives

By the end of the class students should be able to

Class Time and Location

Spring quarter (April - June, 2017)
Lecture: Monday, Wednesday 1:30-2:50
Location: Cubberley Auditorium

Office Hours

See calendar


See the Grading page.

Final Project Details

See the Project Page for more details on the course project.

Useful Reference Texts