信頼度を考慮した模倣学習：最適性の異なるデモンストレーションからの学習
Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality
最適性がばらつくデモンストレーションから、信頼度スコアと高性能な方策を同時に学習する枠組みCAILを提案し、理論的な収束保証と実機実験で有効性を示した論文。
著者: Songyuan Zhang, Zhangjie Cao, Dorsa Sadigh, Yanan Sui
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Most existing imitation learning approaches assume the demonstrations are drawn from experts who are optimal, but relaxing this assumption enables us to use a wider range of data. Standard imitation learning may learn a suboptimal policy from demonstrations with varying optimality. Prior works use confidence scores or rankings to capture beneficial information from demonstrations with varying optimality, but they suffer from many limitations, e.g., manually annotated confidence scores or high average optimality of demonstrations. In this paper, we propose a general framework to learn from demonstrations with varying optimality that jointly learns the confidence score and a well-performing policy. Our approach, Confidence-Aware Imitation Learning (CAIL) learns a well-performing policy from confidence-reweighted demonstrations, while using an outer loss to track the performance of our model and to learn the confidence. We provide theoretical guarantees on the convergence of CAIL and evaluate its performance in both simulated and real robot experiments. Our results show that CAIL significantly outperforms other imitation learning methods from demonstrations with varying optimality. We further show that even without access to any optimal demonstrations, CAIL can still learn a successful policy, and outperforms prior work.
