State representation learning aims to capture latent factors of an
environment. Contrastive methods have performed better than generative models
in previous state representation learning research. Although some researchers
realize the connections between masked image modeling and contrastive
representation learning, the effort is focused on using masks as an
augmentation technique to represent the latent generative factors better.
Partially observable environments in reinforcement learning have not yet been
carefully studied using unsupervised state representation learning methods.
In this article, we create an unsupervised state representation learning
scheme for partially observable states. We conducted our experiment on a
previous Atari 2600 framework designed to evaluate representation learning
models. A contrastive method called Spatiotemporal DeepInfomax (ST-DIM) has
shown state-of-the-art performance on this benchmark but remains inferior to
its supervised counterpart. Our approach improves ST-DIM when the environment
is not fully observable and achieves higher F1 scores and accuracy scores than
the supervised learning counterpart. The mean accuracy score averaged over
categories of our approach is ~66%, compared to ~38% of supervised learning.
The mean F1 score is ~64% to ~33%.

本文提出了一种用于部分可观测状态的无监督状态表示学习方案，相比受监督学习的 ST-DIM 方法在 Atari 游戏中的表现提高了，平均准确率得分达到了约 66％，平均 F1 得分为约 64％。