ノイズ誘導輸送による模倣学習
Noise-Guided Transport for Imitation Learning
少数の専門家デモしかない低データ状況で、模倣学習を最適輸送問題として捉え敵対的訓練で解く軽量なオフポリシー手法を提案し、高次元ヒューマノイド制御でも20遷移程度で高性能を示した。
著者: Lionel Blondé, Joao A. Candido Ramos, Alexandros Kalousis
分類: cs.LG, cs.AI
原文アブストラクト
We consider imitation learning in the low-data regime, where only a limited number of expert demonstrations are available. In this setting, methods that rely on large-scale pretraining or high-capacity architectures can be difficult to apply, and efficiency with respect to demonstration data becomes critical. We introduce Noise-Guided Transport (NGT), a lightweight off-policy method that casts imitation as an optimal transport problem solved via adversarial training. NGT requires no pretraining or specialized architectures, incorporates uncertainty estimation by design, and is easy to implement and tune. Despite its simplicity, NGT achieves strong performance on challenging continuous control tasks, including high-dimensional Humanoid tasks, under ultra-low data regimes with as few as 20 transitions.