Most of the ML approaches boil down to some well-defined algorithms that are at least 10 years and often 30 years old.  Understanding it isn&#x27;t really the issue, it&#x27;s testing it for usage and tweaking it for better performance.  What exactly is to &quot;understand&quot; about reinforcement learning (application, not theory)?  You could say it&#x27;s developing&#x2F;emergent, but not that it&#x27;s &quot;not well understood.&quot;<p>The rest of my comment is centered on what the real problem is: a lack of data.  That will hurt any and every algorithm.  If I have to make a decision&#x2F;prediction and you give me a single or two pieces of input data, whatever process I employ is likely to return something poor.