Putting exploration and learning into the control loop gives reinforcement learning greater power to solve general problems, including problems whose solutions are not already known.

“Put this into the loop itself, and that gives you a call, it gives you huge amounts of power. Because now you can actually solve much more general problems, problems we don't even have an idea of how to solve them in the first place. And this is also maybe where like the the reward is enough people then go in and say well, it becomes not an arbitrary problem with like arbitrary uh definition. And we can do like the exploration we can do, we can do do the learning we can do like the optimal control”