Summary of Interactive Symbolic Regression Through Offline Reinforcement Learning: a Co-design Framework, by Yuan Tian et al.
Interactive Symbolic Regression through Offline Reinforcement Learning: A Co-Design Framework
by Yuan Tian, Wenqi Zhou, Michele Viscione, Hao Dong, David Kammer, Olga Fink
First submitted to arxiv on: 7 Feb 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 proposed Symbolic Q-network (Sym-Q) framework addresses the challenges of large-scale symbolic regression by leveraging reinforcement learning without relying on a transformer-based decoder. This allows for more efficient training and inference. The Sym-Q framework enables effective interaction with domain experts, facilitating iterative equation discovery and co-design of mathematical expressions that align with physical laws. Experimental results demonstrate that pre-trained Sym-Q surpasses existing SR algorithms on the challenging SSDNC benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Symbolic Q-network (Sym-Q) is a new way to find simple math equations from data. Right now, it’s hard for computers to do this because there are so many possible equations and not all of them make sense. Sym-Q lets humans work with the computer to find the right equation by giving feedback at different stages of the process. This helps make sure the equation is correct and makes sense in the context of what we know about how things work in the world. The results show that Sym-Q does a better job than other methods on some really tough problems. |
Keywords
* Artificial intelligence * Decoder * Inference * Regression * Reinforcement learning * Transformer
