In reinforcement learning, handling state constraints in the environment is often more difficult than enforcing action constraints.

“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”

“Having effectively state constraints or something in the environment.”