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.

Two areas that stand out to me are all non trivial forms of A/B testing and adaptive (educational) assessment.

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.