Some constrained reinforcement-learning methods solve local approximations and train a model to imitate the resulting locally optimal solution.

“Uh and that's fundamental because like raw like estimate Lagrangians from data like constraints from data is really difficult because uh you have this this variance in there. And then there's like a final family, which for example a safe M_P_O_ paper published recently in there, b uh that is like C_V_P_O_ uh like this or like what is the other one like full cops is another one, which basically try to do like k effectively local approximations and try to solve the problem”

“home locally optimally and then they train the model to effectively just emulate this locally optimal solution.”