approaches to train a linear controller to maximize expected reward of a robot so step one two and three is the unsupervised learning that can happen ahead of time and then you can run RL on that representation that you've learned so one thing that's real interesting here is that remember the Omnitech where yaw would say oh well you know reinforced learning is a cherry on the cake which is tiny compared to the cake and why are something for spent in the chair because it's not a lot of rewards it's just small amount of reward signal