ORIE 4570

Reinforcement Learning with Operations Research Applications

Course Description

Course information provided by the Courses of Study 2024-2025. Courses of Study 2024-2025 is scheduled to publish mid-June.

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 and ORIE 3500.

• 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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