CS 6789

CS 6789

Course information provided by the Courses of Study 2020-2021. Courses of Study 2021-2022 is scheduled to publish by July 1.

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

Enrollment Information
Syllabi: none
  •   Regular Academic Session. 

  • 4 Credits Stdnt Opt

Enrollment Information
Syllabi: none
  •   Regular Academic Session. 

  • 4 Credits Stdnt Opt

  • 18930CS 6789  LEC 030

  • Instruction Mode: In Person
    Co-Taught in NYC. Enrollment limited to Cornell Tech PhD Students Only.