3 credits
Fall 2026 Lecture Upper Division
The ability to autonomously acquire skills is a hallmark of an intelligent agent. This can be achieved through a machine learning paradigm known as reinforcement learning, in which an agent learns by repeatedly interacting with an environment. This course will explore topics related to reinforcement learning, including deep reinforcement learning, model-based and model-free learning, intrinsically motivated learning, applications, and open challenges. This course is structured to introduce methods and research topics in reinforcement learning. It consists of lectures, homework assignments, a midterm exam, and a course project. You will be expected to work in small groups to formulate and carry out a short-term research project related to reinforcement learning.
Learning Outcomes1Understand how agents can acquire skills through reinforcement learning.
2Know the strengths and weaknesses of various classes of reinforcement learning algorithms and understand how they can be applied to real-world problems.
3Implement and evaluate various reinforcement learning algorithms.
4Articulate and present project findings clearly.
Prerequisites
Restrictions
made by Purdue students.