  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
