Self-supervised representation learning has achieved remarkable success in
recent years. By subverting the need for supervised labels, such approaches are
able to utilize the numerous unlabeled images that exist on the Internet and in
photographic datasets. Yet to build truly intelligent agents, we must construct
representation learning algorithms that can learn not only from datasets but
also learn from environments. An agent in a natural environment will not
typically be fed curated data. Instead, it must explore its environment to
acquire the data it will learn from. We propose a framework, curious
representation learning (CRL), which jointly learns a reinforcement learning
policy and a visual representation model. The policy is trained to maximize the
error of the representation learner, and in doing so is incentivized to explore
its environment. At the same time, the learned representation becomes stronger
and stronger as the policy feeds it ever harder data to learn from. Our learned
representations enable promising transfer to downstream navigation tasks,
performing better than or comparably to ImageNet pretraining without using any
supervision at all. In addition, despite being trained in simulation, our
learned representations can obtain interpretable results on real images. Code
is available at this https URL

通过提出一个奇思妙想的自学习框架，结合强化学习探索数据并训练视觉表示模型，使得机器能够从未标注的大规模图片数据中学习，得到一种强化学习的样本自提取方式，并在实际应用中展现了很好的转移性能。