  Learning is an inherently continuous phenomenon. When humans learn a new task
there is no explicit distinction between training and inference. As we learn a
task, we keep learning about it while performing the task. What we learn and
how we learn it varies during different stages of learning. Learning how to
learn and adapt is a key property that enables us to generalize effortlessly to
new settings. This is in contrast with conventional settings in machine
learning where a trained model is frozen during inference. In this paper we
study the problem of learning to learn at both training and test time in the
context of visual navigation. A fundamental challenge in navigation is
generalization to unseen scenes. In this paper we propose a self-adaptive
visual navigation method (SAVN) which learns to adapt to new environments
without any explicit supervision. Our solution is a meta-reinforcement learning
approach where an agent learns a self-supervised interaction loss that
encourages effective navigation. Our experiments, performed in the AI2-THOR
framework, show major improvements in both success rate and SPL for visual
navigation in novel scenes. Our code and data are available at:
https://github.com/allenai/savn .
