Earlier quoted context omitted.
If you need robustness you can explicitly model an uncertainty set for your problem coefficients, and solve to optimality the robust optimization problem. That way you know exactly your degree of “insurance” to uncertainty. You also know that this is the absolute best you can do in view of this uncertainty set.
Right, I am asking if RL may be a somewhat easy way to get that in there? Since a lot of robustness is best known after a series of losses. That is, if you know what your total exposure to risk is, then LP is still ultimately the answer. If you don't, you need some way to monte carlo different scenarios to get an idea. And at that point, I feel you have backed yourself into RL?
The main appeal of RL is for cases where an explicit model cannot be derived. Think of very complex chemical reactions or other system dynamics. There indeed, you can go with RL and cross your fingers.
If your process is dangerous, we must also have a supervisor system, to ensure that the RL actions will not lead to catastrophic situations (from violating constraints). Like Teslas targeting firetrucks.