Imitation Learning by Reinforcement Learning
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Imitation Learning algorithms learn a policy from demonstrations of expert behavior. Somewhat counterintuitively, we show that, for deterministic experts, imitation learning can be done by reduction to reinforcement learning, which is commonly considered more difficult. We conduct experiments which confirm that our reduction works well in practice for a continuous control task.
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