procedure you collect data you learn a model you find a policy in the learned model you deploy that policy you take a new data and prove your world model and repeat so that's what they did and this gives for the carpal swing up and so after about twenty trations with this we would be able to learn to swing it up now a couple of other world models papers is the actions additional video prediction using deep networks in atari games just at the top here worth checking out model-based reinforced planning for atari it's another one