A Comprehensive Study on Preference-based Reward Learning

The author proposes a novel approach to active learning for reward functions, focusing on aligning the learned reward with the true reward based on specific metrics. By optimizing queries to learn rewards up to an equivalence class, the method outperforms traditional information gain methods.