The real trouble with bandits is that people don't bother to look into what the real potential benefit is as far as the target you're optimizing. Despite theoretically loving bandit techniques, I've convinced multiple teams not to go that path because the real advantage of using them is a slightly more optimal mix of people in experiment than if you ran them manually.
At some scales it can make sense, but for the vast majority of products/companies the statistical benefits don't outweigh the added complexity.
Bandits work best for relatively simple optimization choices at very large scale. If you care at all about the information gained in an experiment, it's fairly unlikely bandits are a good choice for you (or any reinforcement learning algorithm at that point).
At some scales it can make sense, but for the vast majority of products/companies the statistical benefits don't outweigh the added complexity.
Bandits work best for relatively simple optimization choices at very large scale. If you care at all about the information gained in an experiment, it's fairly unlikely bandits are a good choice for you (or any reinforcement learning algorithm at that point).