Many comments here actually state that RL is really data hungry. That is true, and indeed one of the difficulties to overcome. However there is a lot of work in

Many comments here actually state that RL is really data hungry. That is true, and indeed one of the difficulties to overcome. However there is a lot of work in the field with making use of transfer learning / meta learning to tackle this and I have seen successful implementations in the manufacturing industry. The bigger blocker right now is the simulation part itself (and there is also a lot work on that!) Training a DRL for real world physical processes ob real world processes has the Problem, that a failure is extremely expensive. You don’t want to see a manufacturing process moving too fast (because it’s still learning / optimizing) and then breaking a 500k router spindle. Simulation is certainly one of the trajectories to deal with this though. The third big block is then really the cost: for many manufacturing processes there are pretty good parameters found over multiple years. The trade off of the costs of a new DRL system - training it, potential failures, deploying it and training the users - to the gains need to be big enough to justify the use of DRL financially. Again standardization will help with this, but it requires significant R&D costs upfront only few are willing to pay. In the end, in my opinion we will see the application more often, but it takes time and effort to improve and make it cost effective.