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Deep learning algorithms have recently appeared that pre-train hidden layers of neural networks in unsupervised ways, leading to state-of-the-art performance on large classification problems. These methods can also pre-train networks used for reinforcement learning. However, this ignores the additional information that exists in a reinforcement learning paradigm via the ongoing sequence of state, action, new state tuples. This paper demonstrates that learning a predictive model of statedoi:10.1109/ijcnn.2015.7280824 dblp:conf/ijcnn/AndersonLE15 fatcat:k2i272yv2jaj3a5ctrq725tlbm