Online Course Catalog

This course will discuss the theory and practice of reinforcement learning (RL) with applications to control, robotics, and multi-agent systems. The goal is for students to understand: (1) key theoretical concepts, (2) basic algorithms and their implementation, and (3) when and how RL can be used for research applications. Topics include Markov decision processes (MDPs), value-based methods, policy methods, function approximation, and multi-agent reinforcement learning (MARL).

4 hours

BS degree in engineering or science from an accredited college in the United States or an approved institution of higher learning abroad.

• Aerospace Engineering