Constraint reinforcement learning can be harder because a stationary deterministic policy is not necessarily available.

“Like fundamentally if you look at literature, uh there is the uh paper by Altman who did like this P_H_D_ in like uh constraint re-enforcement learning about like the actual like the mix integer programming uh like uh state. And one of the things that was even known back then is that constraint re-enforcement is a lot harder. Specifically because there is not necessarily uh at a stationary”

“deterministic policy anymore. I have to be careful with this. There are non-stationary deterministic policies and there are deterministic and there are stochastic policies which are stationary.”