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The paper addresses the problem of learning a model of Atari 2600 games (a popular testbed for reinforcement learning algorithms), in other words predicting future frames conditioned on action input. This is a challenging problem and its solution is a useful tool to build better controllers. The paper is clear and well-structured, and has convincing experiments (and videos). The model is a CNN (with a fully-connected layer) followed by multiplicative interactions with an action vector, followed by convolution decoding layers. The recurrent version has an LSTM layer added after the CNN.