Course Websites

AE 598 RL - Reinforcement Learning

Last offered Spring 2023

Official Description

Subject offerings of new and developing areas of knowledge in aerospace engineering intended to augment existing formal courses. Topics and prerequisites vary for each section. See Class Schedule or departmental course information for both. Course Information: May be repeated in the same or separate terms if topics vary to a maximum of 12 hours.

Section Description

Theory and practice of reinforcement learning (RL) algorithms with applications to control, robotics, and multi-agent systems. The goal is for students to understand: (1) RL algorithms and their implementation, (2) key theoretical concepts, and (3) when and how RL can be used for research applications. Topics include MDPs, value-based methods, policy methods, function approximation, and multi-agent reinforcement learning. Pre-reqs: CS 446 or equivalent; STAT 400 or equivalent; proficiency with Python.

Related Faculty

TitleSectionCRNTypeHoursTimesDaysLocationInstructor
Reinforcement LearningRL49350LCD11000 - 1150 T R  410B1 Engineering Hall Huy Trong Tran