NBDI: A Simple and Effective Termination Condition for Skill Extraction from Task-Agnostic Demonstrations
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916744896970752 |
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| author | Kim, Myunsoo Lee, Hayeong Shim, Seong-Woong Seo, JunHo Lee, Byung-Jun |
| author_facet | Kim, Myunsoo Lee, Hayeong Shim, Seong-Woong Seo, JunHo Lee, Byung-Jun |
| contents | Intelligent agents are able to make decisions based on different levels of granularity and duration. Recent advances in skill learning enabled the agent to solve complex, long-horizon tasks by effectively guiding the agent in choosing appropriate skills. However, the practice of using fixed-length skills can easily result in skipping valuable decision points, which ultimately limits the potential for further exploration and faster policy learning. In this work, we propose to learn a simple and effective termination condition that identifies decision points through a state-action novelty module that leverages agent experience data. Our approach, Novelty-based Decision Point Identification (NBDI), outperforms previous baselines in complex, long-horizon tasks, and remains effective even in the presence of significant variations in the environment configurations of downstream tasks, highlighting the importance of decision point identification in skill learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12668 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NBDI: A Simple and Effective Termination Condition for Skill Extraction from Task-Agnostic Demonstrations Kim, Myunsoo Lee, Hayeong Shim, Seong-Woong Seo, JunHo Lee, Byung-Jun Machine Learning Artificial Intelligence Intelligent agents are able to make decisions based on different levels of granularity and duration. Recent advances in skill learning enabled the agent to solve complex, long-horizon tasks by effectively guiding the agent in choosing appropriate skills. However, the practice of using fixed-length skills can easily result in skipping valuable decision points, which ultimately limits the potential for further exploration and faster policy learning. In this work, we propose to learn a simple and effective termination condition that identifies decision points through a state-action novelty module that leverages agent experience data. Our approach, Novelty-based Decision Point Identification (NBDI), outperforms previous baselines in complex, long-horizon tasks, and remains effective even in the presence of significant variations in the environment configurations of downstream tasks, highlighting the importance of decision point identification in skill learning. |
| title | NBDI: A Simple and Effective Termination Condition for Skill Extraction from Task-Agnostic Demonstrations |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.12668 |