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

To Learn or Not to Learn: Analyzing the Role of Learning for Navigation in Virtual Environments