EIG-based query selection for full-trajectory demonstrations reduces the number of human demonstrations needed to learn a reward in tabular gridworlds compared to random and entropy-based baselines.
Brown, Yuchen Cui, and Scott Niekum
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Toward Information Theoretic Active Inverse Reinforcement Learning
EIG-based query selection for full-trajectory demonstrations reduces the number of human demonstrations needed to learn a reward in tabular gridworlds compared to random and entropy-based baselines.