モデルベースメタ適応によるロボット制御
Robotic Control Using Model Based Meta Adaption
メタ強化学習を用いて、あるタスクで学習した好ましいロボットの振る舞いを類似タスク群に素早く適応させるMeta Adaptation Controller (MAC)を提案した論文。
著者: Karam Daaboul, Joel Ikels, Marius Zöllner
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
In machine learning, meta-learning methods aim for fast adaptability to unknown tasks using prior knowledge. Model-based meta-reinforcement learning combines reinforcement learning via world models with Meta Reinforcement Learning (MRL) for increased sample efficiency. However, adaption to unknown tasks does not always result in preferable agent behavior. This paper introduces a new Meta Adaptation Controller (MAC) that employs MRL to apply a preferred robot behavior from one task to many similar tasks. To do this, MAC aims to find actions an agent has to take in a new task to reach a similar outcome as in a learned task. As a result, the agent will adapt quickly to the change in the dynamic and behave appropriately without the need to construct a reward function that enforces the preferred behavior.
