When expert supervision is available, practitioners often use imitation learning with varying degrees of success. We show that when an expert has access to privileged information that is unavailable to the student, this information is marginalized in the student policy during imitation learning resulting in an imitation gap and, potentially, poor results. Prior work bridges this gap via a progression from imitation learning to reinforcement learning. While often successful, gradual progression fails for tasks that require frequent switches between exploration and memorization skills. To better address these tasks and alleviate the imitation gap we propose Adaptive Insubordination (ADVISOR), which dynamically weights imitation and reward-based reinforcement learning losses during training, enabling switching between imitation and exploration. On a suite of challenging didactic and MiniGrid tasks, we show that ADVISOR outperforms pure imitation, pure reinforcement learning, as well as their sequential and parallel combinations.
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