AGENTIC-SYSTEMSMay 27, 20261 min read10 views

Exploiting Local Dynamics Regularity for Reusable Skills in Offline Hierarchical RL

Researchers have introduced CARL, a new algorithm that optimizes Offline Hierarchical Reinforcement Learning (HRL). By exploiting local dynamics regularity, CARL enables agents to discover and reuse complex skills more effectively in long-horizon tasks.

Computer Science > Artificial Intelligence

Title:Exploiting Local Dynamics Regularity for Reusable Skills in Offline Hierarchical RL

View PDF HTML (experimental)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 similar kinds of action sequences. By aligning these contexts with the action sequences they require, we are able to learn which skills to reuse and where to reuse them. In principle, this information should benefit many HRL algorithms, where high-level policies have to reason about the low-level skills they use. The resulting algorithm CARL (Contrastive Action-based Representations for Reusable Local Control) shows both qualitative clustering of meaningful skills in complex humanoid environments and improved downstream performance on the OGBench benchmark when integrated with HIQL.

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Source: arXiv cs.AI Recent

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