Learning to Navigate in Cities Without a Map

TL;DR

This paper introduces an end-to-end deep reinforcement learning approach for city-scale navigation using Google StreetView data, enabling agents to learn and transfer navigation skills across multiple urban environments.

Contribution

It proposes a dual pathway architecture that combines general policies with locale-specific features for scalable city navigation, and provides an interactive environment for training and testing.

Findings

Agents successfully navigate multiple cities.

Agents can reach destinations kilometers away.

Transfer learning enables city-to-city generalization.

Abstract

Navigating through unstructured environments is a basic capability of intelligent creatures, and thus is of fundamental interest in the study and development of artificial intelligence. Long-range navigation is a complex cognitive task that relies on developing an internal representation of space, grounded by recognisable landmarks and robust visual processing, that can simultaneously support continuous self-localisation ("I am here") and a representation of the goal ("I am going there"). Building upon recent research that applies deep reinforcement learning to maze navigation problems, we present an end-to-end deep reinforcement learning approach that can be applied on a city scale. Recognising that successful navigation relies on integration of general policies with locale-specific knowledge, we propose a dual pathway architecture that allows locale-specific features to be encapsulated, while still enabling transfer to multiple cities. We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away. The project webpage http://streetlearn.cc contains a video summarising our research and showing the trained agent in diverse city environments and on the transfer task, the form to request the StreetLearn dataset and links to further resources. The StreetLearn environment code is available at https://github.com/deepmind/streetlearn