An early constrained reinforcement learning approach adapted TRPO's performance-improvement bound to costs; CPO aims to keep the system within its constraints with 99% probability after an update.

“At kind of all of these guarantees you try to encode into uh kind of into the training process itself. And that different ways of doing that. So one of the f very earliest was uh basically they went and looked at T_R_P_O_ and said well we have this like performance improvement bound, we can do exactly the same thing for costs.”

“And this gave it like C_P_O_ like where effectively it says, well if we are within the constraints and we are with ninety nine percent of our probability after doing that, set, we're still in there.”