Summary of From Reward Shaping to Q-shaping: Achieving Unbiased Learning with Llm-guided Knowledge, by Xiefeng Wu
From Reward Shaping to Q-Shaping: Achieving Unbiased Learning with LLM-Guided Knowledge
by Xiefeng Wu
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 This paper introduces Q-shaping, an approach that accelerates agent training by incorporating domain knowledge into the Q-value initialization process. The method aims to directly shape Q-values, improving sample efficiency without compromising optimality. A large language model is used as a heuristic provider to evaluate Q-shaping across 20 diverse environments. The results show significant enhancements in sample efficiency, outperforming both baseline and reward shaping methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Q-shaping is a new way to help robots learn faster by giving them good starting points based on what we know about the task. It’s like having a expert teacher who shows you the right direction to take. In this paper, they tested Q-shaping in many different situations and found that it makes learning much more efficient. |
Keywords
» Artificial intelligence » Large language model
