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Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning
To improve the feedback efficiency of HitL RL methods (i. e., require less feedback), this paper introduces Sub-optimal Data Pre-training, SDP, an approach that leverages reward-free, sub-optimal data to improve scalar- and preference-based HitL RL algorithms.
Reinforcement Teaching
To effectively learn such a teaching policy, we introduce a parametric-behavior embedder that learns a representation of the student's learnable parameters from its input/output behavior.
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