Shripad Deshmukh
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126
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ML×1AI×1
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2026
Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach
The paper proposes a feasible-reward-set framework to perform Inverse Reinforcement Learning (IRL) when data comes from multiple imperfect demonstrators, providing theoretical guarantees and practical algorithms.
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