Model Based Reinforcement Learning focuses on sample efficiency by learning a model of the real environment while learning the optimal policy. Learning an explicit model not only helps in reducing the number of interactions with the real environment but also opens up opportunities to do transfer learning. In this talk I will be presenting my work on how to learn such a model for Atari games and how to leverage it to train a policy while having a budget on the number of interactions with the real environment.