What does a joint probability density function have to do with Stochastic Optimal Control and Reinforcement Learning? I stumbled upon a job offer from a company that was looking for someone who was good with Reinforcement Learning (applied to finance) and something in their offer caught my eye. It goes something like this:


  We want you to be able to study the price dynamic (of a stock I suppose) and its evolution in order to extract a Joint PDF that will be used in the Optimal Stochastic Control of a Loss Function (or gain)


The thing is I understand what each of these things mean and how they are used separately (from my background in Control theory &amp; dynamical systems) and I worked with fitting Joint PDFs and Copulas before, but I don't understand how a Joint PDF would help with the "Optimal Stochastic Control of a Loss Function" ? Thanks.
Extracting a joint PDF just means that you create a model that models the behavior of several variables combined instead of in isolation. 

If these variables aren't independent and your loss functions is influenced by all of them, you obviously have to learn this joint PDF to minimize your loss. 

So I don't see this statement as particularly mysterious.