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We introduce Recurrent State Representation Learning (RSRL) to tackle the problem of state representation learning in robotics for partially observable environments. To learn low dimensional state representations, we combine a Long Short Term Memory network with robotic priors. RSRL introduces new priors with landmarks and combines them with existing robotics priors in literature to train the representations. To evaluate the quality of the learned state representation, we introduce validationdoi:10.14279/depositonce-8307 fatcat:npfcchxc65gqlef5x352vhupci