Summary of Symbolic Equation Solving Via Reinforcement Learning, by Lennart Dabelow and Masahito Ueda
Symbolic Equation Solving via Reinforcement Learning
by Lennart Dabelow, Masahito Ueda
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Symbolic Computation (cs.SC)
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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 This paper explores the application of machine learning methods to computer algebra, a field that typically relies on human-discovered and programmed rules. The authors propose a novel deep-learning interface involving a reinforcement-learning agent that operates a symbolic stack calculator to explore mathematical relations. This system is designed to perform exact transformations and avoid hallucination effects. Using linear equations as an example, the authors demonstrate how their reinforcement-learning agent autonomously discovers elementary transformation rules and step-by-step solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at using machine learning to help with math problems. Right now, humans have to figure out the rules for doing math operations like simplifying expressions or finding solutions to equations. The authors came up with a new way to do this by teaching a computer program to learn these rules on its own. They used an example of solving linear equations and showed that their program can discover the right steps to solve the problem without making mistakes. |
Keywords
* Artificial intelligence * Deep learning * Hallucination * Machine learning * Reinforcement learning
