ORIE 4570

Reinforcement Learning with Operations Research Applications

Course Description

Course information provided by the 2025-2026 Catalog.

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.

Prerequisites REF-FA25/Corequisites REF-FA25 ORIE 3300 and ORIE 3500. Corequisites: None.

Last 4 terms offered (None)

Outcomes REF-FA25

• 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.

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