to apply something like counterfactual regret minimization to more mainstream reinforcement learning topics. So, I think there was a paper from Berkeley recently on regret minimization in single-agent settings. So, I think there is definitely potential to extend the research and to the more traditional reinforcement learning settings, but I have not looked into that yet. >> [inaudible] a little bit, is there anything I'm learning? So, in the real world one of the things that's really difficult is, I don't know the rules of the game