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In reinforcement learning (machine learning), inverse reinforcement learning refers to recovering the reward function an expert appears to optimize from demonstrations, then learning a policy that performs about as well under it. This meaning comes from Abbeel (2004) in Twenty-first international conference on Machine learning - ICML '04.[1]
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