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Yu Luo

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Open access Aug 2026

Embodied Learning under Policy and Dynamics Shifts

This work proposes Transition Occupancy Matching as a unifying principle to resolve policy and dynamics shifts within a single mathematical framework and introduces Occupancy-Matching Policy Optimization (OMPO), a novel algorithm that optimizes a surrogate objective explicitly correcting for transition discrepancies.

Yu Luo, Lei Lv, Fuchun Sun et al. · 0 citations

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