Reinforcement learning isn't magic pixie dust that suddenly solves AI. One thing we often do is assume for some compact subproblem that AI will offer massive o

Reinforcement learning isn't magic pixie dust that suddenly solves AI. One thing we often do is assume for some compact subproblem that AI will offer massive optimizations relative to our current human baseline. However it often turns out that engineers have spent enough time to do a good enough job that the relative improvement is either not existant or small when focused on optimizing subcomponents of a system. With your video game reference there are some games that q learning does better than humans and others that does worse. For the factory floor I know data scientists and industrial engineers run optimizations to increase productivity and reduce costs. With their constraints it becomes hard or impossible for any algorithm to find a much more optimal solution especially when their problem is convex where the human generated solution is provably globally optimal.