In reinforcement learning, action constraints are relatively simple to enforce, such as by projecting actions onto a reasonable set.

“Classic example for like reinforcement, you just you do action masking or kind of you you scale your actions to be in the range which is reasonable. The the question is usually that there are things outside the model which actually inform the constraints. So for example in reinforcement learning actually having action constraints is pretty simple. Like effectively just project onto some set. The difficult thing is uh kind of”