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Abstract:Unsupervised learning of compact and relevant state representations has been proved very useful at solving complex reinforcement learning tasks. In this paper, we propose a recurrent capsule network that learns such representations by trying to predict the future observations in an agent's trajectory.
* 4 pages, 4 figures, NIPS Workshop on Modeling the Physical World:
Perception, Learning, and Control