  The ability to use a 2D map to navigate a complex 3D environment is quite
remarkable, and even difficult for many humans. Localization and navigation is
also an important problem in domains such as robotics, and has recently become
a focus of the deep reinforcement learning community. In this paper we teach a
reinforcement learning agent to read a map in order to find the shortest way
out of a random maze it has never seen before. Our system combines several
state-of-the-art methods such as A3C and incorporates novel elements such as a
recurrent localization cell. Our agent learns to localize itself based on 3D
first person images and an approximate orientation angle. The agent generalizes
well to bigger mazes, showing that it learned useful localization and
navigation capabilities.
