  Navigation is a rich and well-grounded problem domain that drives progress in
many different areas of research: perception, planning, memory, exploration,
and optimisation in particular. Historically these challenges have been
separately considered and solutions built that rely on stationary datasets -
for example, recorded trajectories through an environment. These datasets
cannot be used for decision-making and reinforcement learning, however, and in
general the perspective of navigation as an interactive learning task, where
the actions and behaviours of a learning agent are learned simultaneously with
the perception and planning, is relatively unsupported. Thus, existing
navigation benchmarks generally rely on static datasets (Geiger et al., 2013;
Kendall et al., 2015) or simulators (Beattie et al., 2016; Shah et al., 2018).
To support and validate research in end-to-end navigation, we present
StreetLearn: an interactive, first-person, partially-observed visual
environment that uses Google Street View for its photographic content and broad
coverage, and give performance baselines for a challenging goal-driven
navigation task. The environment code, baseline agent code, and the dataset are
available at http://streetlearn.cc
