Before revision:  To address this problem, we introduce a robust representation learning approach for RL . We introduce an auxiliary objective based on the multi-view information bottleneck (MIB) principle which  encourages learning representations that are both predictive of the future and less sensitive to task-irrelevant distractions.
After revision:  To address this problem, we leverage the sequential nature of RL to learn robust representations that encode only task-relevant information from observations based on the unsupervised multi-view setting. Specifically, we introduce an auxiliary objective based on the multi-view information bottleneck (MIB) principle which encourages learning representations that are both predictive of the future and less sensitive to task-irrelevant distractions.