Summary of Mitigating Information Loss in Tree-based Reinforcement Learning Via Direct Optimization, by Sascha Marton et al.
Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization
by Sascha Marton, Tim Grams, Florian Vogt, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 introduces SYMPOL, a novel method for symbolic tree-based on-policy reinforcement learning. It combines a tree-based model with a policy gradient method to enable agents to learn and adapt actions while maintaining interpretability. The approach is evaluated on benchmark RL tasks, showing superiority over alternative methods in terms of performance and interpretability. SYMPOL enables end-to-end learning of interpretable decision trees within standard on-policy RL algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SYMPOL is a new way for computers to learn and make decisions while keeping track of why they’re making certain choices. This helps us understand how the computer is thinking, which can be important in many situations. The researchers used this method to solve some classic decision-making problems, and it did better than other methods that tried to do something similar. |
Keywords
* Artificial intelligence * Reinforcement learning
