  Informational parsimony provides a useful inductive bias for learning
representations that achieve better generalization by being robust to noise and
spurious correlations. We propose \textit{information gating} as a way to learn
parsimonious representations that identify the minimal information required for
a task. When gating information, we can learn to reveal as little information
as possible so that a task remains solvable, or hide as little information as
possible so that a task becomes unsolvable. We gate information using a
differentiable parameterization of the signal-to-noise ratio, which can be
applied to arbitrary values in a network, e.g., erasing pixels at the input
layer or activations in some intermediate layer. When gating at the input
layer, our models learn which visual cues matter for a given task. When gating
intermediate layers, our models learn which activations are needed for
subsequent stages of computation. We call our approach \textit{InfoGating}. We
apply InfoGating to various objectives such as multi-step forward and inverse
dynamics models, Q-learning, and behavior cloning, highlighting how InfoGating
can naturally help in discarding information not relevant for control. Results
show that learning to identify and use minimal information can improve
generalization in downstream tasks. Policies based on InfoGating are
considerably more robust to irrelevant visual features, leading to improved
pretraining and finetuning of RL models.
