论文标题
在没有任何监督的情况下，学习在影像逼真的环境中视觉导航
Learning to Visually Navigate in Photorealistic Environments Without any Supervision
论文作者
论文摘要
在现实的环境中学习导航，在现实的环境中，代理必须仅依靠视觉输入是一项具有挑战性的任务，部分原因是缺乏位置信息使得在培训期间很难提供监督。在本文中，我们介绍了一种新颖的方法，可以学习从图像输入中导航，而无需外部监督或奖励。我们的方法包括三个阶段：学习第一人称视图的良好表示，然后学习使用内存探索，并最终通过设定自己的目标来学习导航。该模型仅具有固有的奖励训练，以便可以将其应用于具有图像观测值的任何环境。我们通过培训代理来浏览Gibson数据集中只有RGB输入的挑战性的光真逼真环境来展示我们方法的好处。
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 by setting its own goals. The model is trained with intrinsic rewards only so that it can be applied to any environment with image observations. We show the benefits of our approach by training an agent to navigate challenging photo-realistic environments from the Gibson dataset with RGB inputs only.
