自己教師あり学習は本当にピクセルからの強化学習を改善するのか？
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?
ピクセルベースのオンライン強化学習において、自己教師あり学習（SSL）が画像拡張のみのベースラインを有意に上回るか検証し、既存のSSLとRLの同時最適化フレームワークの限界を明らかにした。
著者: Xiang Li, Jinghuan Shang, Srijan Das, Michael S. Ryoo
分類: cs.LG, cs.CV, cs.RO
原文アブストラクト
We investigate whether self-supervised learning (SSL) can improve online reinforcement learning (RL) from pixels. We extend the contrastive reinforcement learning framework (e.g., CURL) that jointly optimizes SSL and RL losses and conduct an extensive amount of experiments with various self-supervised losses. Our observations suggest that the existing SSL framework for RL fails to bring meaningful improvement over the baselines only taking advantage of image augmentation when the same amount of data and augmentation is used. We further perform evolutionary searches to find the optimal combination of multiple self-supervised losses for RL, but find that even such a loss combination fails to meaningfully outperform the methods that only utilize carefully designed image augmentations. After evaluating these approaches together in multiple different environments including a real-world robot environment, we confirm that no single self-supervised loss or image augmentation method can dominate all environments and that the current framework for joint optimization of SSL and RL is limited. Finally, we conduct the ablation study on multiple factors and demonstrate the properties of representations learned with different approaches.
