State representation learning with recurrent capsule networks
Louis Annabi, M. Ortíz · arXiv (Cornell University) · 2018
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.