Exact methods, e.g., models that utilize an MIP approach with a specified objective and constraints, have advantages like the following:
- Using off the shelf solvers
- Optimality gap provability
- Modelling approaches for domain problems is well understood
Reinforcement Learning (RL) presents the opportunity to train a model and then have it "predict" a solution.
Why would you use this method?
What is the effectiveness of this as a methodology?
It may be possible to get "better" solutions in less compute time given a pre-trained model.
Is any one else working on this type of approach? Or have references?
(Specifically for solving network flow and assignment problems on graphs.)