  In this paper we present a world model, which learns causal features using
the invariance principle. In particular, we use contrastive unsupervised
learning to learn the invariant causal features, which enforces invariance
across augmentations of irrelevant parts or styles of the observation. The
world-model-based reinforcement learning methods independently optimize
representation learning and the policy. Thus naive contrastive loss
implementation collapses due to a lack of supervisory signals to the
representation learning module. We propose an intervention invariant auxiliary
task to mitigate this issue. Specifically, we utilize depth prediction to
explicitly enforce the invariance and use data augmentation as style
intervention on the RGB observation space. Our design leverages unsupervised
representation learning to learn the world model with invariant causal
features. Our proposed method significantly outperforms current
state-of-the-art model-based and model-free reinforcement learning methods on
out-of-distribution point navigation tasks on the iGibson dataset. Moreover,
our proposed model excels at the sim-to-real transfer of our perception
learning module. Finally, we evaluate our approach on the DeepMind control
suite and enforce invariance only implicitly since depth is not available.
Nevertheless, our proposed model performs on par with the state-of-the-art
counterpart.
