Learning to navigate in cities without a map DeepMind

We depart from the traditional approaches which rely on explicit mapping and exploration (like a cartographer who tries to localise themselves and draw a map at the same time). Our approach, in contrast, is to learn to navigate as humans used to do, without maps, GPS localisation, or other aids, using only visual observations. We build a neural network agent that inputs images observed from the environment and predicts the next action it should take in that environment. We train it end-to-end using deep reinforcement learning, similarly to some recent work on learning to navigate in complex 3D mazes and reinforcement learning with unsupervised auxiliary tasks for playing games. Unlike those studies, which were conducted on small-scale simulated maze environments, we utilise city-scale real-world data, including complex intersections, footpaths, tunnels, and diverse topology across London, Paris, and New York City.