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On Generalizing Preference-based Reinforcement Learning to Multi-Objective Problems
We study reinforcement learning (RL) from pairwise human-generated trajectory comparisons. We generalize preference-based RL by formalizing a novel setting in which the expert can also label trajectory pairs as incomparable when neither trajectory "dominates" the other. We introduce the learning pro
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