Balancing Similarity-Contrast in Unsupervised Representation Learning: Evaluation with Reinforcement Learning

Abstract

In this paper, we provided an unsupervised contrastive representation learning method which uses contrastive views in which both spatial and temporal similarity-contrast is balanced. The balanced views are created by taking pixels from the anchor sample and any randomly selected negative sample and balancing the ratio of number of pixels taken from the anchor and the negative. Then these balanced views are paired with the anchor to create the positive contrastive views and all other samples paired with the anchor are taken as negative contrastive views. We made the evaluation using reinforcement learning tasks on Atari games and Deep Mind Control suites (DMControl). Our evaluations on 26 Atari games and six DMControl tasks show that the proposed method is superior in learning spatio-temporally evolving factors of the environment by capturing the relevant task controlling generative factors from the agents’ raw observations.