Summary of Skilltree: Explainable Skill-based Deep Reinforcement Learning For Long-horizon Control Tasks, by Yongyan Wen et al.
SkillTree: Explainable Skill-Based Deep Reinforcement Learning for Long-Horizon Control Tasks
by Yongyan Wen, Siyuan Li, Rongchang Zuo, Lei Yuan, Hangyu Mao, Peng Liu
First submitted to arxiv on: 19 Nov 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 Deep reinforcement learning (DRL) has achieved significant success in various research areas, but its reliance on neural networks limits practical applications due to a lack of transparency. Decision trees are an attractive alternative, offering explainability, but they struggle with high-dimensional long-horizon continuous control tasks due to their limited expressiveness. This paper proposes SkillTree, a novel framework that reduces complex continuous action spaces into discrete skill spaces. The hierarchical approach integrates a differentiable decision tree within the high-level policy to generate skill embeddings, guiding the low-level policy in executing skills. By making skill decisions explainable, the method achieves skill-level explainability, enhancing understanding of the decision-making process. Experimental results demonstrate performance comparable to skill-based neural networks in complex robotic arm control domains, with SkillTree offering explanations at the skill level, increasing transparency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning has done well in some areas, but it’s not always clear how it makes decisions. This is because most methods use complex computer programs called neural networks. Decision trees are a simpler way to make decisions, but they’re not good enough for very complex tasks. To solve this problem, the researchers propose SkillTree, a new way to break down complex actions into smaller, more manageable skills. By using decision trees to decide which skills to use, the method becomes clearer and easier to understand. The results show that SkillTree works just as well as some other methods in controlling robotic arms, and it provides explanations for why it makes certain decisions. |
Keywords
* Artificial intelligence * Decision tree * Reinforcement learning
