CS 6789 - Foundations of Reinforcement Learning

Spring. 4 credits. Student option grading.

Prerequisite: CS 3780/CS 5780 or equivalent, BTRY 3080 or ECON 3130 or MATH 4710 and ORIE 3300 and MATH 2940. For undergraduates: permission of instructor with minimum grade A in CS 3780.

W. Sun.

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.