  Deep reinforcement learning (DRL) agents are often sensitive to visual
changes that were unseen in their training environments. 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 a
novel contrastive version of the Multi-View Information Bottleneck (MIB)
objective for temporal data. We train RL agents from pixels with this auxiliary
objective to learn robust representations that can compress away
task-irrelevant information and are predictive of task-relevant dynamics. This
approach enables us to train high-performance policies that are robust to
visual distractions and can generalize well to unseen environments. We
demonstrate that our approach can achieve SOTA performance on a diverse set of
visual control tasks in the DeepMind Control Suite when the background is
replaced with natural videos. In addition, we show that our approach
outperforms well-established baselines for generalization to unseen
environments on the Procgen benchmark. Our code is open-sourced and available
at https://github. com/BU-DEPEND-Lab/DRIBO.
