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Online Active Preference Learning from Sparse Expert Feedback in Continuous Control
In continuous-control reinforcement learning, direct reward design can bedifficult when desired behavior is multi-dimensional, and expert feedback is often too limited to query exhaustively. This thesis develops an online active-preference learning framework that combines a fixed expert log-density
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