Summary of Offline Policy Learning Via Skill-step Abstraction For Long-horizon Goal-conditioned Tasks, by Donghoon Kim et al.
Offline Policy Learning via Skill-step Abstraction for Long-horizon Goal-Conditioned Tasks
by Donghoon Kim, Minjong Yoo, Honguk Woo
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed offline goal-conditioned (GC) policy learning framework, called GLvSA, addresses the challenge of sparse rewards in achieving long-horizon goals by decomposing them into near-term goals aligned with acquired skills. The framework learns a GC policy progressively while modeling skill-step abstractions from existing data. A hierarchical GC policy is devised for efficient fine-tuning, allowing parameter-efficient adaptation to various long-horizon goals. Experimental results on the maze and Franka kitchen environments demonstrate GLvSA’s superiority in adapting GC policies, outperforming existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to learn goal-conditioned policies that can work well with sparse rewards. The idea is to break down long-term goals into smaller, more achievable steps, based on the skills you’ve learned from past data. This helps the policy learn more efficiently and effectively. The researchers tested this approach in different environments and found it outperforms other methods. This breakthrough could lead to better AI systems that can adapt to new situations. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient
