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Fair point, but there are no shortcuts. Either you will use domain knowledge and you will build a model-based approach (LP, IP, MINLP, CP/SAT, DP), or you will
by whatever1 3y ago
Fair point, but there are no shortcuts. Either you will use domain knowledge and you will build a model-based approach (LP, IP, MINLP, CP/SAT, DP), or you will have to find terabytes of good quality samples to feed the RL beast, hoping it will approximate a good enough policy / value function.
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.
- taeric 3y agoAbsolutely! I said in another post that I assumed you'd use LP if your decision space was modeled in such a way that that can work, but RL if you instead have more of a simulator and a cost function. Even that, I grant, is over simplified. Mainly trying to find a way to use charity for that opening post. At face value, I don't know what they were getting at.