  Deep reinforcement learning (DRL) agents are often sensitive to visual
changes that were unseen in their training environments. 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.
This enables us to train high-performance policies that are robust to visual
distractions and can generalize to unseen environments. We demonstrate that our
approach can achieve SOTA performance on challenging visual control tasks, even
when the background is replaced with natural videos. In addition, we show that
our approach outperforms well-established baselines on generalization to unseen
environments using the large-scale Procgen benchmark.
