評価ベース強化学習
Rating-based Reinforcement Learning
サンプルペアの相対比較ではなく、個々の軌跡に対する人間の評価値を用いて報酬を学習する新しい強化学習手法を提案した。
著者: Devin White, Mingkang Wu, Ellen Novoseller, Vernon J. Lawhern, Nicholas Waytowich, Yongcan Cao
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
This paper develops a novel rating-based reinforcement learning approach that uses human ratings to obtain human guidance in reinforcement learning. Different from the existing preference-based and ranking-based reinforcement learning paradigms, based on human relative preferences over sample pairs, the proposed rating-based reinforcement learning approach is based on human evaluation of individual trajectories without relative comparisons between sample pairs. The rating-based reinforcement learning approach builds on a new prediction model for human ratings and a novel multi-class loss function. We conduct several experimental studies based on synthetic ratings and real human ratings to evaluate the effectiveness and benefits of the new rating-based reinforcement learning approach.
