注意散漫制御スイート：ピクセル入力強化学習のための挑戦的ベンチマーク
The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels
DM Controlに背景・色・カメラ姿勢の3種の視覚的妨害を加えた新しいベンチマークを構築し、最先端の視覚ベース強化学習が妨害下で性能低下することを示した。
著者: Austin Stone, Oscar Ramirez, Kurt Konolige, Rico Jonschkowski
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
Robots have to face challenging perceptual settings, including changes in viewpoint, lighting, and background. Current simulated reinforcement learning (RL) benchmarks such as DM Control provide visual input without such complexity, which limits the transfer of well-performing methods to the real world. In this paper, we extend DM Control with three kinds of visual distractions (variations in background, color, and camera pose) to produce a new challenging benchmark for vision-based control, and we analyze state of the art RL algorithms in these settings. Our experiments show that current RL methods for vision-based control perform poorly under distractions, and that their performance decreases with increasing distraction complexity, showing that new methods are needed to cope with the visual complexities of the real world. We also find that combinations of multiple distraction types are more difficult than a mere combination of their individual effects.
