On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning

이호준(카이스트, 카카오엔터프라이즈), 이관호(카이스트), 황동윤(카이스트), 이현호(카카오엔터프라이즈), 이병근(카이스트), 주재걸(카이스트)

International Conference on Machine Learning (ICML)



Recently, unsupervised representation learning (URL) has improved the sample efficiency of Reinforcement Learning (RL) by pretraining a model from a large unlabeled dataset. The underlying principle of these methods is to learn temporally predictive representations by predicting future states in the latent space. However, an important challenge of this approach is the representational collapse, where the subspace of the latent representations collapses into a low-dimensional manifold. To address this issue, we propose a novel URL framework that causally predicts future states while increasing the dimension of the latent manifold by decorrelating the features in the latent space. Through extensive empirical studies, we demonstrate that our framework effectively learns predictive representations without collapse, which significantly improves the sample efficiency of state-of-the-art URL methods on the Atari 100k benchmark. The code is available at