I personally don't like this, because it makes a place more exclusive with legal moats, not genuine prestige. If you're a professor, this also makes your work less known, not more. IMO the only beneficiaries are either those who paid a lot to be there, lecturers who don't want to adapt, and university admins.

RL excels at learning control problems. It is mathematically guaranteed to provide an optimal solution for the state and controls you provide it, given enough runtime. For some problems (playing computer games), that runtime is surprisingly short.

There is a reason self-driving cars use RL, and don't use GPTs.

For the house value problem, you can quantify how far the prediction is from the true value, there are lots of regression models with proven methods of adjusting the model parameters (e.g. gradient descent), and the feature space comprises mostly monotone, weakly interacting features like quality of neighborhood schools and square footage. It's a "traditional" problem and can be solved as well as possible by the traditional methods we know and love. RL is unnecessary, might require more data than you have, and might produce an inferior result.

In contrast, for a sequential decision problem like playing go, the binary won-lost signal doesn't tell us much about how well or poorly the game was played, it's not clear how to improve the strategy, and there are a large number of moves at each turn with no evident ranking. In this setting RL is a difficult but possible approach.

RL is nice in that it is handles messy cases where you don't have per example labels.

How do you build a learned chess playing bot? Essentially the state of the art is to find a clever way of turning the problem of playing chess into a sequence of supervised learning problems.

I don't really see why you would want to use it for binary classification or continuous predictive modeling. It's why it excels in game play and operational control. You need to make decisions now that constrain possible decision in the future, but you cannot know the outcome until that future comes and you cannot attribute causality to the outcome even when you learn what it is. This isn't "hot dog/not a hot dog" that generally has an unambiguously correct answer and the classification itself is directly either correct or incorrect. In RL, a decision made early in a game probably leads causally to a particular outcome somewhere down the line, but the exact extent to which any single action contributes is unknown and probably unknowable in many cases.