ゼロから始めない：事前データを活用したロボット強化学習の自動化
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning
多様なオフラインデータを活用し、新タスクへの適応と環境リセットを自律的に行うロボット強化学習手法を提案。サンプル効率と汎化性能を向上させる。
著者: Homer Walke, Jonathan Yang, Albert Yu, Aviral Kumar, Jedrzej Orbik, Avi Singh, Sergey Levine
分類: cs.RO, cs.LG
原文アブストラクト
Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill acquisition for robotic systems. However, in practice, real-world robotic RL typically requires time consuming data collection and frequent human intervention to reset the environment. Moreover, robotic policies learned with RL often fail when deployed beyond the carefully controlled setting in which they were learned. In this work, we study how these challenges can all be tackled by effective utilization of diverse offline datasets collected from previously seen tasks. When faced with a new task, our system adapts previously learned skills to quickly learn to both perform the new task and return the environment to an initial state, effectively performing its own environment reset. Our empirical results demonstrate that incorporating prior data into robotic reinforcement learning enables autonomous learning, substantially improves sample-efficiency of learning, and enables better generalization. Project website: https://sites.google.com/view/ariel-berkeley/
