Author

E. Kaufmann

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Jul 2026

Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification

In the sparse-reward, goal-directed setting, the sample complexity upper bound for HBPI-UCRL is strictly lower than that of its non-hierarchical counterpart, providing theoretical justification for the empirical success of HRL.

Anders Jonsson, E. Kaufmann, Gianmarco Tedeschi et al. · arXiv.org · 0 citations