  In this paper we compare learning-based methods and classical methods for
navigation in virtual environments. We construct classical navigation agents
and demonstrate that they outperform state-of-the-art learning-based agents on
two standard benchmarks: MINOS and Stanford Large-Scale 3D Indoor Spaces. We
perform detailed analysis to study the strengths and weaknesses of learned
agents and classical agents, as well as how characteristics of the virtual
environment impact navigation performance. Our results show that learned agents
have inferior collision avoidance and memory management, but are superior in
handling ambiguity and noise. These results can inform future design of
navigation agents.
