Fall 2018 Course Descriptions
COMP 150-04 Reinforcement Learning
"Reinforcement learning problems involve learning what to do --- how
to map situations to actions --- so as to maximize a numerical reward
signal." - Sutton and Barto ("Reinforcement Learning: An
Introduction", course textbook)
This course will focus on agents that much learn, plan, and act in
complex, non-deterministic environments. We will cover the main theory
and approaches of Reinforcement Learning (RL), along with common
software libraries and packages used to implement and test RL
algorithms. The course is a graduate seminar with assigned readings
and discussions. The content of the course will be guided in part by
the interests of the students. It will cover at least the first
several chapters of the course textbook. Beyond that, we will move to
more advanced and recent readings from the field (e.g., transfer
learning and deep RL) with an aim towards focusing on the practical
successes and challenges relating to reinforcement learning.
There will be a programming component to the course in the form of a
few short assignments and a final projects.
Prerequisite: tudents are expected to be proficient programmers in at least one of the following languages: C++, Java, or Python. Prior coursework (or experience) in Artificial Intelligence and/or Machine Learning is highly recommended, but not required.