Teaching a Machine to Read Maps with Deep Reinforcement Learning

Understand

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.