[tikhonj]

Reinforcement learning is a solid fit for a number of traditional operations research problems. (In fact, that's pretty much what motivated research into RL in the first place, as I understand it.)

One concrete example I heard about was using reinforcement learning to price airline tickets. Behind the scenes, airlines break up the tickets on a single plane into a large number of distinctly priced types. The question of how much of each type of ticket to offer at what price and how to change this over time (as the actual flight is coming closer and closer) is a massive optimization problem that's too large to solve exactly. Reinforcement learning coupled with simulation can find good solutions if you set up the feature space correctly. (In this case, I remember that the only feature that ultimately mattered was either total profit or total revenue for the mix of tickets being offered.)

One thing to note here is that this is using "normal" reinforcement learning, not "deep" reinforcement learning. You can get away with having a simple functional approximation of the state instead of reaching for a neural network. This seems true for most operations research problems where you would reach for reinforcement learning—figuring out a way to model the state by hand works well enough and has the important benefit of being easier to understand and interpret. The "deep" part becomes useful when your state space is so large and complex that other techniques become infeasible.