Exploiting Local Dynamics Regularity for Reusable Skills in Offline Hierarchical RL
Quick Answer
This paper shows that The CARL algorithm enhances Hierarchical Reinforcement Learning by leveraging local dynamics for skill reusability, showing improved performance on the OGBench benchmark when integrated with HIQL.
Quick Take
The CARL algorithm enhances Hierarchical Reinforcement Learning by leveraging local dynamics for skill reusability, showing improved performance on the OGBench benchmark when integrated with HIQL. This approach allows for better alignment of action sequences with varying global contexts, facilitating efficient skill reuse in complex humanoid environments.
Key Points
- CARL stands for Contrastive Action-based Representations for Reusable Local Control.
- The algorithm focuses on local transitions requiring similar action sequences across contexts.
- Qualitative clustering of meaningful skills was observed in humanoid environments.
- Integration with HIQL led to improved performance on the OGBench benchmark.
- The approach addresses the challenge of reusing skills in Hierarchical Reinforcement Learning.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2605. 26371v1 Announce Type: new 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.
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →Adversarial Social Epistemology for Assemblies of Humans and Large Language Models
The paper introduces Adversarial Social Epistemology (ASE) to analyze how agents manipulate trust in public communications, highlighting mechanisms that undermine the reliability of testimony and inference. It critiques existing frameworks like epistemic bubbles and misinformation diffusion, proposing a new language for understanding trust breaches and auditing inferential chains in densely interactive environments involving humans and large language models.