[astrophysician]

I agree -- as ML becomes increasingly easy to be applied by non-experts or people without a heavy math/stats background, I've seen an increasing volume of arguments against the data science profession (someone the other day called DS the "gate-keepers") but: there be dragons.

Anyone can use SOTA deep learning models today, but in my experience, it's more important to understand the answer to "what are the shortcomings/consequences of using a particular method to solve this problem?" "what is (or could be) biases in this dataset?", etc. It requires a non-trivial understanding of the underlying methodology and statistics to reliably answer these questions (or at least worry about them).

Can you apply deep reinforcement learning to your problem? Maybe. Should you? Well, it depends, and you should understand the pros and cons, which requires more than just the knowledge of how to make API calls. There are consequences to misusing ML/AI, and they may not even be obvious from offline testing and cross validation.