16-745 Optimal Control and Reinforcement Learning

12 units, Carnegie Mellon University

This is a course about how to make robots move through and interact with their environment with speed, efficiency, and robustness. We will survey a broad range of topics from nonlinear dynamics, linear systems theory, classical optimal control, numerical optimization, state estimation, system identification, and reinforcement learning. The goal is to provide students with hands-on experience applying each of these ideas to a variety of robotic systems so that they can use them in their own research.

Prerequisites: none listed

Offered: Spring 2027, Spring 2026

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