Trying to dig into Optimal Control Theory a bit, after realizing that - in many ways - OCT and (certain aspects of) Machine Learning are just opposite sides of the same coin. Reinforcment Learning in particular shares a lot of concepts with OCT.
His slides, references and FB livestreamed video, are here:
Both fields are attempting to solve the same problem: choose the optimal action to take at the current time for a given process. Control theorists normally start out with a model, or a family of potential models that describe the behavior of the process and work from there to determine the optimal action. This is very much an area of applied mathematics and academics take rigorous approaches, but, in industry, many engineers just use a PID or LQR controller and call it a day regardless how applicable they are to the actual system theoretically.
Meanwhile, the reinforcement learning folk typically work on problems where the models are too complicated to work with computationally or often even write down, so a more tractable approach is to learn a model and control policy from data. There's plenty of people who analyze properties of learning algorithms, etc., within this framework, and others who don't really care beyond whether or not the system works.
This is the main distinction I've been exposed to, between Optimal Control and Reinforcement Learning. I've heard it summarized as "Optimal Control uses models, Reinforcement Learning tries very hard to stay away from using models". That's probably simplifying things a little bit too much, but it seems like a reasonable starting point to see where the two fields diverge.
http://www.ieeecss.org/CSM/library/1992/april1992/w01-Reinfo...