公園を散歩：モデルフリー強化学習で20分で歩行を学習
A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning
モデルフリー強化学習と調整済みコントローラを組み合わせ、実世界で四足歩行をわずか20分で学習できることを示した研究。
著者: Laura Smith, Ilya Kostrikov, Sergey Levine
分類: cs.RO, cs.AI
原文アブストラクト
Deep reinforcement learning is a promising approach to learning policies in uncontrolled environments that do not require domain knowledge. Unfortunately, due to sample inefficiency, deep RL applications have primarily focused on simulated environments. In this work, we demonstrate that the recent advancements in machine learning algorithms and libraries combined with a carefully tuned robot controller lead to learning quadruped locomotion in only 20 minutes in the real world. We evaluate our approach on several indoor and outdoor terrains which are known to be challenging for classical model-based controllers. We observe the robot to be able to learn walking gait consistently on all of these terrains. Finally, we evaluate our design decisions in a simulated environment.
