ユークリッド対称性は強化学習と計画に活用できるか？
Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?
強化学習と計画問題におけるユークリッド群対称性を統一的に扱う理論を提案し、同変なサンプリングベース計画アルゴリズムを構築する手法を開発した。
著者: Linfeng Zhao, Owen Howell, Jung Yeon Park, Xupeng Zhu, Robin Walters, Lawson L. S. Wong
分類: cs.LG, cs.RO
原文アブストラクト
In robotic tasks, changes in reference frames typically do not influence the underlying physical properties of the system, which has been known as invariance of physical laws.These changes, which preserve distance, encompass isometric transformations such as translations, rotations, and reflections, collectively known as the Euclidean group. In this work, we delve into the design of improved learning algorithms for reinforcement learning and planning tasks that possess Euclidean group symmetry. We put forth a theory on that unify prior work on discrete and continuous symmetry in reinforcement learning, planning, and optimal control. Algorithm side, we further extend the 2D path planning with value-based planning to continuous MDPs and propose a pipeline for constructing equivariant sampling-based planning algorithms. Our work is substantiated with empirical evidence and illustrated through examples that explain the benefits of equivariance to Euclidean symmetry in tackling natural control problems.
