Learning navigation without building maps
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. Moreover, the approach we use support city-specific learning and optimisation as well as general, transferable navigation behaviours.
Modular neural network architecture that can transfer to new cities
The neural network inside our agent consists of three parts: 1) a convolutional network that can process images and extract visual features, 2) a locale-specific recurrent neural network that is implicitly tasked with memorising the environment as well as learning a representation of “here” (current position of the agent) and of “there” (location of the goal) and 3) a locale-invariant recurrent network that produces the navigation policy over the agent’s actions. The locale-specific module is designed to be interchangeable and, as its name indicates, unique to each city where the agent navigates, whereas the vision module and the policy module can be locale-invariant.
Source: https://deepmind.com/blog/article/learning-to-navigate-cities-without-a-map
