A Noise-Robust Elicit-to-Optimize Framework for Distortion Riskmetrics via Inver
We propose a noise-robust elicit-to-optimize framework that integrates inverse reinforcement learning (IRL) and reinforcement learning (RL) for eliciting agents' risk preferences and optimizing policies under a broad class of risk objective
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