State representation learning with recurrent capsule networks

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