ORIE 5570

ORIE 5570

Course information provided by the Courses of Study 2024-2025.

The ongoing information revolution and the advent of the big data era make quantitative methods in the business context indispensable. This course introduces reinforcement learning, decision-making under uncertainty, and related algorithms through the lens of OR applications. Examples will be drawn from real-world problems in operations, revenue management, queuing, finance, transportation, healthcare, and other areas of interest. The course will cover modeling and applications, basic theory, and algorithms.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: ORIE 3300, ORIE 3500, or equivalent.

Outcomes
  • Be able to formalize dynamic decision problems under uncertainty as Markov decision processes.
  • Learn about finite-horizon and infinite-horizon MDPs.
  • Know how to solve MDPs exactly via dynamic programming as well as know how to solve MDPs approximately via reinforcement learning.
  • Learn to read the technical literature in operations research, machine learning, and control literature.
  • Gain hands-on experience in implementing and applying various exact and approximate algorithms.

View Enrollment Information

Syllabi:
  •   Regular Academic Session.  Combined with: ORIE 4570

  • 3 Credits Graded

  • 19630 ORIE 5570   LEC 001

  • Instruction Mode: In Person