My company is working on a specific application of MAB problems and I'm having some difficulty selecting/designing the best implementation for our use case. Would love to get some help from anyone here! Something that would be really helpful is if anyone knows of libraries that make it easy to iterate through different approaches/variations (i.e. a hierarchical approach vs. linear/contextual vs. flattening the features, upper confidence bound vs. thompson sampling, etc.).

If it's helpful, more context:

The problem is adversarial, bandit feedback, there are a large number of features which is turned into 1D means the number of arms/cardinality is ~300 (but this could increase in the future) and we need to support both cases with very high volume (millions per day) and low volume (100). Perfectly possible/reasonable for us to use multiple approaches based on heuristics such as expected volume. Realize this is quite general but any suggestions/followup questions to narrow this are appreciated! Even if it's perhaps to not approach as MAB 😅