Exploiting Local Dynamics Regularity for Reusable Skills in Offline Hierarchical RL

Researchers introduce CARL, a hierarchical reinforcement learning algorithm that discovers reusable skills by exploiting local dynamics regularity—the observation that similar action sequences solve similar local transitions across different contexts. When integrated with existing HRL methods like HIQL, CARL demonstrates improved performance on complex tasks and meaningful skill clustering in humanoid environments.

This research addresses a fundamental challenge in hierarchical reinforcement learning: creating skills that generalize across different situations rather than remaining task-specific. The core insight is elegant—local transitions in different global contexts often require similar action patterns, suggesting that skills learned from one context can transfer to others with proper alignment. CARL's contrastive learning approach identifies these action-based representations by recognizing when different global states require comparable low-level behaviors.

The problem this solves matters significantly for RL's practical deployment. Long-horizon tasks remain computationally expensive and sample-inefficient without proper abstractions. Previous HRL approaches often discover skills that lack interpretability or reusability, requiring expensive retraining for new tasks. By explicitly grounding skill discovery in local dynamics, CARL provides a principled framework that high-level policies can reason about meaningfully.

For the AI research community, this work bridges the gap between skill discovery and skill utility. Integration with HIQL on the OGBench benchmark validates that the approach yields tangible improvements over existing hierarchical methods. The qualitative clustering of meaningful skills suggests the algorithm discovers interpretable action primitives rather than arbitrary behavioral chunks, which could accelerate progress in long-horizon planning and multi-task learning.

Future developments will likely focus on scaling CARL to more diverse environments and investigating whether learned skills transfer across fundamentally different task distributions. The framework's applicability to real-world robotic control, where sample efficiency directly impacts deployment costs, represents a natural next frontier.

• →CARL discovers reusable hierarchical skills by aligning action sequences with local state transitions across different contexts.
• →The algorithm improves downstream task performance when integrated with HIQL on the OGBench benchmark.
• →Local dynamics regularity—the assumption that similar transitions need similar actions—provides a principled basis for skill abstraction.
• →Qualitative analysis shows CARL produces interpretable, meaningful skill clusters in complex humanoid environments.
• →The approach addresses a critical limitation in HRL: most discovered skills lack true reusability across different scenarios.