algorithm is soft is a d4 PG which is still yard are a logger so if you think about the essence here reinforced winning is about learning to achieve goals and if the underlying space is low dimensional there is a low dimensional state shims will be able to recover that low dimensional state and then learn just as efficiently from pixels as from state and how might we do that well we've seen a lot of success in past lectures with contrast of learning for computer vision in fact we saw with CTC that it was possible by using