Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification

Abstract:We present HBPI-UCRL, a model-based algorithm for hierarchical reinforcement learning (HRL) that learns high-level and low-level policies in parallel. HBPI-UCRL exploits the fact that a high-level transition corresponds to a multi-step transition at the low level. We introduce two conditions on the low-level dynamics that are sufficient to make parallel HRL learnable. When these conditions hold, we prove that HBPI-UCRL has a polynomial sample complexity in the problem parameters. In the sparse-reward, goal-directed setting, our 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.

Machine Learning (cs.LG)

arXiv:2607.29294 [cs.LG]

(or arXiv:2607.29294v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.29294 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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