Introduces a feasible-reward-set approach to IRL with multiple heterogeneous suboptimal demonstrators, proving monotonic shrinkage of the joint set and two recovery guarantees for the ground-truth optimal reward.
Generalizing behavior via inverse reinforcement learning with closed-form reward centroids.arXiv preprint arXiv:2509.12010, 2025
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Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach
Introduces a feasible-reward-set approach to IRL with multiple heterogeneous suboptimal demonstrators, proving monotonic shrinkage of the joint set and two recovery guarantees for the ground-truth optimal reward.