With all its hype in RL, I am yet to see significant real life problems solved with it. I am afraid with all the funding going into it, and nothing to show for except being able to play complex games, this might contribute to the mistrust in proper utilization of research funds. Also the reproducibility problem in RL is many times worse than in ML.
I agree with you that it's early days for RL. I think some companies are using it in their advertising platforms, but it's not really my field.
That said, I strongly disagree about what constitutes the proper utilization of research funds. IMO, society should invest in basic research without the expectation of solutions to significant real-world problems.
Still, I'd be really surprised if I don't see advances from the field of reinforcement learning used in a ton of applications during my lifetime.
So I can see RL augmenting traditional optimal control in regimes outside of previously modeled spaces.
For instance, a machine would operate via optimal control in regimes that are known and characterized by a model, but if it ever gets into a new unmodeled situation, it can use RL to figure stuff out and find a way to proceed suboptimally (subject to safety constraints, etc.).
An illustrative example is Roomba. Roomba is probably based on some form of RL, and it does a decent job. But suppose we have a map of the room that Roomba can use -- this would let it plot the optimal path. However suppose the map of the room is incomplete. Roomba can still operate near optimally within the mapped area, but will have to learn the environment outside the map. Or if the layout of the room has changed since the map was created (new furniture), Roomba's RL can kick in.
I'd bet that sample efficiency is a factor in translating they most hyped bits of RL into solving IRL problems. So many business problems translate to "Learn which of these things to do, as quickly and cheaply as possible."
