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 ORIE 3300, ORIE 3500, and ORIE 3510.

Last 4 Terms Offered 2025FA, 2024FA

Learning 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.

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