GAS constructs a graph in a learned temporal-distance representation and uses shortest-path search to select subgoals, outperforming prior offline HRL methods on stitching and exploration benchmarks.
This environment requires sequential reasoning and diverse manipulation skill composition (Park et al., 2025a)
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Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning
GAS constructs a graph in a learned temporal-distance representation and uses shortest-path search to select subgoals, outperforming prior offline HRL methods on stitching and exploration benchmarks.