SECANT: 視覚ポリシーのゼロショット汎化のための自己エキスパートクローニング
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies
弱い画像拡張でRLによりエキスパートを訓練し、強い拡張で生徒ネットワークが模倣学習することで、未知の視覚環境へのゼロショット汎化を実現した手法。
著者: Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, Anima Anandkumar
分類: cs.LG, cs.AI, cs.CV, cs.RO
原文アブストラクト
Generalization has been a long-standing challenge for reinforcement learning (RL). Visual RL, in particular, can be easily distracted by irrelevant factors in high-dimensional observation space. In this work, we consider robust policy learning which targets zero-shot generalization to unseen visual environments with large distributional shift. We propose SECANT, a novel self-expert cloning technique that leverages image augmentation in two stages to decouple robust representation learning from policy optimization. Specifically, an expert policy is first trained by RL from scratch with weak augmentations. A student network then learns to mimic the expert policy by supervised learning with strong augmentations, making its representation more robust against visual variations compared to the expert. Extensive experiments demonstrate that SECANT significantly advances the state of the art in zero-shot generalization across 4 challenging domains. Our average reward improvements over prior SOTAs are: DeepMind Control (+26.5%), robotic manipulation (+337.8%), vision-based autonomous driving (+47.7%), and indoor object navigation (+15.8%). Code release and video are available at https://linxifan.github.io/secant-site/.
