I think RL is a niche by definition, but that's not a bad thing. If the problem you want to solve is about agents operating in interactive environments and maximizing some kind of utility function along the way - surely RL is your workhorse here.

Over the course of the last years we have seen successful applications of RL outside that narrow field of problems, where a problem that is seemingly not about agents and environments can still be formulated as an MDP and then solved with an RL approach. Because of these examples there seems to be a looming sentiment that RL is somehow "instead of" supervised, and questions like "which is better RL or supervised" arise.

My take on this would he that both are applicable in their appropriate spaces of problem formulations. Some problems are made to be solved with SL, some other ones with RL. And while it is feasible to twist an SL problem into RL framework, or even vice versa, it does not imply that one or the other is the ultimate tool.

Same way as one wouldn't use RL to multiply two numbers (except for academic interest), one should not use RL if it is not the right framework for the problem at hand. But for some other problems RL will definitely be (and already is, like in Go, Chess, Startcraft) the future.