  Imitation learning trains policies to map from input observations to the
actions that an expert would choose. In this setting, distribution shift
frequently exacerbates the effect of misattributing expert actions to nuisance
correlates among the observed variables. We observe that a common instance of
this causal confusion occurs in partially observed settings when expert actions
are strongly correlated over time: the imitator learns to cheat by predicting
the expert's previous action, rather than the next action. To combat this
"copycat problem", we propose an adversarial approach to learn a feature
representation that removes excess information about the previous expert action
nuisance correlate, while retaining the information necessary to predict the
next action. In our experiments, our approach improves performance
significantly across a variety of partially observed imitation learning tasks.
