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cs.AI2024
PREDICT: Preference Reasoning by Evaluating Decomposed preferences Inferred from Candidate Trajectories
Stephane Aroca-Ouellette, Natalie Mackraz, Barry-John Theobald +1
Accommodating human preferences is essential for creating AI agents that deliver personalized and effective interactions. Recent work has shown the potential for LLMs to infer pref…
cs.AI2024★ 1 cited
Sample-Efficient Preference-based Reinforcement Learning with Dynamics Aware Rewards
Katherine Metcalf, Miguel Sarabia, Natalie Mackraz +1
Preference-based reinforcement learning (PbRL) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent behaviors. We show that…