CS 6789

Foundations of Reinforcement Learning

Course Description

Course information provided by the Courses of Study 2021-2022.

State-of-art intelligent systems often need the ability to make sequential decisions in an unknown, uncertain, possibly hostile environment, by actively interacting with the environment to collect relevant data. Reinforcement Learning is a general framework that can capture the interactive learning setting. This graduate level course focuses on theoretical and algorithmic foundations of Reinforcement Learning. The topics of the course will include: basics of Markov Decision Process (MDP); Sample efficient learning in discrete MDPs; Sample efficient learning in large-scale MDPs; Off-policy policy optimization; Policy gradient methods; Imitation learning & Learning from demonstrations; Contextual Bandits. Throughout the course, we will go over algorithms, prove performance guarantees, and also discuss relevant applications. This is an advanced and theory-heavy course: there is no programming assignment and students are required to work on a theory-focused course project.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: CS 4780, BTRY 3080 or ECON 3130, or MATH 4710, ORIE 3300, MATH 2940. For undergraduates: permission of instructor with minimum grade A in CS 4780.

View Enrollment Information