Learning to Visually Navigate in Photorealistic Environments Without any Supervision. (arXiv:2004.04954v1 [cs.CV] CROSS LISTED)

Learning to navigate in a realistic setting where an agent must rely solely
on visual inputs is a challenging task, in part because the lack of position
information makes it difficult to provide supervision during training. In this
paper, we introduce a novel approach for learning to navigate from image inputs
without external supervision or reward. Our approach consists of three stages:
learning a good representation of first-person views, then learning to explore
using memory, and finally learning to navigate …

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