生涯連合強化学習：クラウドロボットシステムにおけるナビゲーション学習アーキテクチャ
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
クラウド上の共有モデルを知識融合アルゴリズムで更新し、転移学習を組み合わせてロボットが経験を蓄積・活用しながら新しい環境に適応するナビゲーション学習手法を提案した。
著者: Boyi Liu, Lujia Wang, Ming Liu
分類: cs.RO, cs.AI, cs.DC, cs.LG, cs.SY, eess.SY
原文アブストラクト
This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website based on LFRL.
