  We develop algorithms for imitation learning from policy data that was
corrupted by temporally correlated noise in expert actions. When noise affects
multiple timesteps of recorded data, it can manifest as spurious correlations
between states and actions that a learner might latch on to, leading to poor
policy performance. To break up these spurious correlations, we apply modern
variants of the instrumental variable regression (IVR) technique of
econometrics, enabling us to recover the underlying policy without requiring
access to an interactive expert. In particular, we present two techniques, one
of a generative-modeling flavor (DoubIL) that can utilize access to a
simulator, and one of a game-theoretic flavor (ResiduIL) that can be run
entirely offline. We find both of our algorithms compare favorably to
behavioral cloning on simulated control tasks.
