Learning & Control Resources Recommendation

This post is a collection of resources that I have found useful for learning about optimal control and reinforcement learning. I will update this post as I find more resources.

Basics

Some youtubers that I found useful to kickstart learning control:

Brain Douglas: His control lectures could be a good starting point for learning the basics of control.

Steve Brunton: UoW Prof. Steve Brunton has a series of lectures on control and dynamical systems as well as how it can be combined with machine learning. Highly recommend his control bootcamp series.

Intermediate

Some introductory courses that could help you dive deeper into control:

CMU Convex Optimization: optimization is everywhere in control. This course is a good starting point for learning about optimization, which I found more practical than the optimization course in Stanford Offered by Prof. Boyd. The course also comes with scribe, slides, quiz, and video lectures, which are very helpful for self-study.

CMU Optimal Control: A course offered by Prof. Zac, who is a leading expert in the field of optimal control. The content is more basic, coming with concrete examples and exercises. Comes with a course notebook, slides, and video lectures. I have a course notebook here and homework solutions.

Berkeley CS285: most classic RL course and I still find it very useful.

MIT Underactuated Robotics: A book by Russ Tedrake that covers the basics of optimal control and reinforcement learning in the context of robotics. Online course available. The notebook is pretty good and could be a good tools for learning drake.

MIT Robotic Manipulation: A book by Russ Tedrake that covers the basics of robotic manipulation. Mainly focus on solving for chain of rigid bodies and contact dynamics. The resource is also pretty good.