Summary of Rgmdt: Return-gap-minimizing Decision Tree Extraction in Non-euclidean Metric Space, by Jingdi Chen et al.
RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space
by Jingdi Chen, Hanhan Zhou, Yongsheng Mei, Carlee Joe-Wong, Gina Adam, Nathaniel D. Bastian, Tian Lan
First submitted to arxiv on: 21 Oct 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 paper presents an interpretable reinforcement learning framework for multi-agent decision-making. By establishing an upper bound on the return gap between oracle expert policies and extracted decision trees, the authors recast the problem as a non-Euclidean clustering task. They propose an iteratively-grow-DT procedure for decentralized DT extractions and introduce the Return-Gap-Minimization Decision Tree (RGMDT) algorithm. RGMDT outperforms heuristic baselines on tasks like D4RL while achieving nearly optimal returns under given complexity constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning algorithms have made great progress in solving complex problems, but their lack of interpretability makes it hard for humans to understand how they work. The paper solves this problem by finding a way to make decision trees understandable and useful in multi-agent situations. They create an algorithm that can extract decision trees from complex reinforcement learning models and show that it works well on certain tasks. |
Keywords
» Artificial intelligence » Clustering » Decision tree » Deep learning » Reinforcement learning
