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- W4384644682 abstract "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." @default.
- W4384644682 created "2023-07-19" @default.
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- W4384644682 date "2023-07-17" @default.
- W4384644682 modified "2023-09-25" @default.
- W4384644682 title "Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?" @default.
- W4384644682 doi "https://doi.org/10.48550/arxiv.2307.08226" @default.
- W4384644682 hasPublicationYear "2023" @default.
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