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Can Euclidean Symmetry Help in Reinforcement Learning and Planning
Linfeng Zhao · Owen Howell · Jung Yeon Park · Xupeng Zhu · Robin Walters · Lawson Wong

Fri Jul 28 02:10 PM -- 03:00 PM (PDT) @
in 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML) »
Event URL: https://openreview.net/forum?id=ox5jxm5exv »

In robotic tasks, changes of reference frames typically do not affect the underlying physical meaning. These are isometric transformations, including translations, rotations, and reflections, called Euclidean group. In this work, we study reinforcement learning and planning tasks that have Euclidean group symmetry. We provide a theory that extends prior work (on symmetry in reinforcement learning, planning, and optimal control) to compact Lie groups and covers them as special cases, and show examples to explain the benefits of equivariance to Euclidean symmetry. We extend the 2D path planning with value-based planning to continuous MDPs and propose a pipeline for equivariant sampling-based planning algorithm with empirical evidence.

Author Information

Linfeng Zhao (Northeastern University)
Owen Howell (Northeastern University)
Jung Yeon Park (Northeastern University)
Xupeng Zhu (Northeastern University)
Robin Walters (Northeastern University)
Lawson Wong (Northeastern University)

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