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arXiv Machine Learning By Daisuke Yamada, Travis Pence, Vikas Singh

Learning Goal-Reaching Quasimetric Geometry From Finite-Time Reachability

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The paper introduces ReQRL, a method for learning quasimetric geometry in goal-conditioned reinforcement learning by constraining the critic’s value gradients with finite-horizon reachability. It decouples dynamical reachability from boundary geometry, estimating both from data using state-constrained optimal control principles. Experiments on OGBench show that ReQRL matches or surpasses existing quasimetric and offline GCRL approaches.

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