Reinforcement-learning environments built from one or more instruments can target reward states and generate varied training experiences from a finite dataset.

“at that point or you can also start taking subsets of instruments and you're like okay we're going to create reinforcement learning environments on the basis of this instrument alone or maybe it's across these different instruments to kind of get to some reward state so that's a way where this finite basket of data when viewed in different ways can then be expanded for training purposes”