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In many control problems that include vision, optimal controls can beinferred from the location of the objects in the scene. This information can berepresented using feature points, which is a list of spatial locations inlearned feature maps of an input image. Previous works show that feature pointslearned using unsupervised pre-training or human supervision can provide goodfeatures for control tasks. In this paper, we show that it is possible to learnefficient feature point representations end-to-end, without the need forunsupervised pre-training, decoders, or additional losses. Our proposedarchitecture consists of a differentiable feature point extractor that feedsthe coordinates of the estimated feature points directly to a soft actor-criticagent. The proposed algorithm yields performance competitive to thestate-of-the art on DeepMind Control Suite tasks.

Learning of feature points without additional supervision improves reinforcement learning from images