データ拡張で強化学習のサンプル効率を向上させる
Towards More Sample Efficiency in Reinforcement Learning with Data Augmentation
観測データを反射対称性や緩い目標定義で再利用する2つのデータ拡張手法を提案し、深層強化学習の学習速度を大幅に向上させた。
著者: Yijiong Lin, Jiancong Huang, Matthieu Zimmer, Juan Rojas, Paul Weng
分類: cs.AI, cs.RO
原文アブストラクト
Deep reinforcement learning (DRL) is a promising approach for adaptive robot control, but its current application to robotics is currently hindered by high sample requirements. We propose two novel data augmentation techniques for DRL in order to reuse more efficiently observed data. The first one called Kaleidoscope Experience Replay exploits reflectional symmetries, while the second called Goal-augmented Experience Replay takes advantage of lax goal definitions. Our preliminary experimental results show a large increase in learning speed.
