  Building sample-efficient agents that generalize out-of-distribution (OOD) in
real-world settings remains a fundamental unsolved problem on the path towards
achieving higher-level cognition. One particularly promising approach is to
begin with low-dimensional, pretrained representations of our world, which
should facilitate efficient downstream learning and generalization. By training
240 representations and over 10,000 reinforcement learning (RL) policies on a
simulated robotic setup, we evaluate to what extent different properties of
pretrained VAE-based representations affect the OOD generalization of
downstream agents. We observe that many agents are surprisingly robust to
realistic distribution shifts, including the challenging sim-to-real case. In
addition, we find that the generalization performance of a simple downstream
proxy task reliably predicts the generalization performance of our RL agents
under a wide range of OOD settings. Such proxy tasks can thus be used to select
pretrained representations that will lead to agents that generalize.
