Summary of Rl-llm-dt: An Automatic Decision Tree Generation Method Based on Rl Evaluation and Llm Enhancement, by Junjie Lin et al.
RL-LLM-DT: An Automatic Decision Tree Generation Method Based on RL Evaluation and LLM Enhancement
by Junjie Lin, Jian Zhao, Lin Liu, Yue Deng, Youpeng Zhao, Lanxiao Huang, Xia Lin, Wengang Zhou, Houqiang Li
First submitted to arxiv on: 16 Dec 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 The proposed RL-LLM-DT method combines reinforcement learning (RL) and large language models (LLMs) to automate decision tree generation for two-player zero-sum games. The approach iteratively refines a decision tree by identifying its weaknesses through RL and generating improved code using LLMs. This integrated method demonstrates significant enhancements in robustness and adaptability, as seen in the curling game where the AI ranks first on the Jidi platform among 34 AIs. The paper’s contribution lies in leveraging LLMs to automate decision tree refinement, enabling full automation of strategy enhancement processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to improve decision trees for games like curling. They combined two techniques: reinforcement learning (RL) and large language models (LLMs). RL helps find the weaknesses in the decision tree, while LLMs generate an improved version of the tree. This process keeps repeating until no more improvements can be made. The team tested this method with a curling AI that ranked first out of 34 on the Jidi platform. |
Keywords
» Artificial intelligence » Decision tree » Reinforcement learning
