  Reinforcement learning from large-scale offline datasets provides us with the
ability to learn policies without potentially unsafe or impractical
exploration. Significant progress has been made in the past few years in
dealing with the challenge of correcting for differing behavior between the
data collection and learned policies. However, little attention has been paid
to potentially changing dynamics when transferring a policy to the online
setting, where performance can be up to 90% reduced for existing methods. In
this paper we address this problem with Augmented World Models (AugWM). We
augment a learned dynamics model with simple transformations that seek to
capture potential changes in physical properties of the robot, leading to more
robust policies. We not only train our policy in this new setting, but also
provide it with the sampled augmentation as a context, allowing it to adapt to
changes in the environment. At test time we learn the context in a
self-supervised fashion by approximating the augmentation which corresponds to
the new environment. We rigorously evaluate our approach on over 100 different
changed dynamics settings, and show that this simple approach can significantly
improve the zero-shot generalization of a recent state-of-the-art baseline,
often achieving successful policies where the baseline fails.
