Game theoretic approaches to training neural networks • /r/MachineLearning

I recently read the paper on GANs, and from what I understand, the networks are trained by making them play a minimax game. I was curious if there is research being done in training networks by having them play more sophisticated games; i.e. expectiminimax or a variation of minimax that only requires a single player. I know GANs are fairly effective, but I surprisingly haven't come across a lot of literature exploring more complicated games. Is it because the notoriously training difficulty of GANs scales with the complexity of the game?