HIQL

Exploiting Local Dynamics Regularity for Reusable Skills in Offline Hierarchical RL

arXiv:2605.26371v2 Announce Type: replace Abstract: Hierarchical Reinforcement Learning (HRL) promises to solve long-horizon Reinforcement Learning (RL) tasks more efficiently than non-hierarchical counterparts by discovering and reusing temporally-extended skills. However, obtaining skills that are actually reusable remains an open challenge. Towards this end, we focus on abstractions that exploit the intuition of local dynamics: local transitions in different global contexts require...