I spent three semesters in college learning RL only to be massively disappointed in the end after discovering that the latest and greatest RL techniques can’t even beat a simple heuristic in Tetris.
In my limited understanding, MAB problems are simpler than those tackled by Deep Reinforcement Learning (DRL), because typically there is no state involved in bandit problems. However, I have no idea about their scale in practical applications, and would love to know more about said business problem.
Nonetheless, I would personally recommend even just learning the basics and fundamentals of RL. Beyond supervised, unsupervised, and the most-recent and well-deservedly hyped semi-supervised learning (generative AI, LLMs, and so on), reinforcement learning indeed models the learning problem in a very elegant way: an agent interacting with an environment and getting feedback. Which is, arguably, a very intuitive and natural way of modeling it. You could consider backward error correction / propagation as an implicit reward signal, but that would be a very limited view.
On a positive note, RL has very practical sucessful applications today - even if in niche fields. For example, LLM fine-tuning techniques like RLHF successfully apply RL to modern AI systems, companies like Covariant are working on large robotics models which definitely use RL, and generally as a research field I believe (but I may be proven wrong!) there is so much more to explore. For example, check Nvidia Eureka that combines LLM to RL [2]: pretty cool stuff IMHO!
Far from attempting to convince you on the strength and capabilities of DRL, just recommending folks to not discard it right away and at least give it a chance to learn the basics, even just for an intellectual exercise :) Thanks again!