The more assumptions you relax, the more general the algorithms become, for example going from immediate reward to delayed reward means going from supervised to reinforcement learning.

The trade-off is the more general algorithms needs many times exponentially more data and compute to come to a similarly good solution.

That's why reinforcement learning has seen so practical few applications relative to supervised learning. There's no free lunch.

That said, as a ML practitioner I would love it if I could just apply a single master algorithm to all problems, but that is likely many years away.