視覚連続制御を極める：改良データ拡張強化学習
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
データ拡張を用いたモデルフリー強化学習DrQを改良し、ピクセル観測からヒューマノイド歩行を含む複雑な連続制御タスクを単一GPUで効率的に学習できるDrQ-v2を提案した論文。
著者: Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto
分類: cs.AI, cs.LG
原文アブストラクト
We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data augmentation to learn directly from pixels. We introduce several improvements that yield state-of-the-art results on the DeepMind Control Suite. Notably, DrQ-v2 is able to solve complex humanoid locomotion tasks directly from pixel observations, previously unattained by model-free RL. DrQ-v2 is conceptually simple, easy to implement, and provides significantly better computational footprint compared to prior work, with the majority of tasks taking just 8 hours to train on a single GPU. Finally, we publicly release DrQ-v2's implementation to provide RL practitioners with a strong and computationally efficient baseline.
