Summary of Enhancing Q-learning with Large Language Model Heuristics, by Xiefeng Wu
Enhancing Q-Learning with Large Language Model Heuristics
by Xiefeng Wu
First submitted to arxiv on: 6 May 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 This paper proposes a new framework called LLM-guided Q-learning, which combines the strengths of large language models (LLMs) and Q-learning to improve reinforcement learning in complex environments. By leveraging LLMs as heuristics, this approach aims to address challenges such as low inference speeds, hallucinations, and biasing final performance. Theoretical analysis shows that LLM-guided Q-learning adapts to hallucinations, improves sample efficiency, and avoids biasing final performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computers learn from feedback in complex situations. Right now, some methods are good but slow, while others are fast but not very accurate. The researchers propose a new method that combines the strengths of two approaches: learning from feedback (Q-learning) and using language models (LLMs). This combined approach can help with common problems like computers getting stuck or making mistakes. |
Keywords
» Artificial intelligence » Inference » Reinforcement learning
