Comment by hruk
I don't know, all of these are pretty surmountable. We've done dynamic pricing with contextual multi-armed bandits, in which each context gets a single decision per time block and gross profit is summed up at the end of each block and used to reward the agent.
That being said, I agree that MABs are poor for experimentation (they produce biased estimates that depend on somewhat hard-to-quantify properties of your policy). But they're not for experimentation! They're for optimizing a target metric.
Surmountable, yes, but in practice it is often just too much hassle. If you are doing tons of these tests you can probably afford to invest in the infrastructure for this, but otherwise AB is just so much easier to deploy that it does not really matter to you that you will have a slightly ineffective algo out there for a few days. The interpretation of the results is also easier as you don't have to worry about time sensitivity of the collected data.