A Reinforcement Learning Approach to Domain-Knowledge Inclusion Using Grammar Guided Symbolic Regression

• URL: http://arxiv.org/abs/2202.04367v1
• Date: Wed, 9 Feb 2022 10:13:14 GMT
• Title: A Reinforcement Learning Approach to Domain-Knowledge Inclusion Using Grammar Guided Symbolic Regression
• Authors: Laure Crochepierre (RTE, LORIA, ORPAILLEUR, UL), Lydia Boudjeloud-Assala (LORIA, ORPAILLEUR, UL), Vincent Barbesant (RTE)
• Abstract summary: We propose a Reinforcement-Based Grammar-Guided Symbolic Regression (RBG2-SR) method. RBG2-SR constrains the representational space with domain-knowledge using context-free grammar as reinforcement action space. We show that our method is competitive against other state-of-the-art methods on the benchmarks and offers the best error-complexity trade-off.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, symbolic regression has been of wide interest to provide an interpretable symbolic representation of potentially large data relationships. Initially circled to genetic algorithms, symbolic regression methods now include a variety of Deep Learning based alternatives. However, these methods still do not generalize well to real-world data, mainly because they hardly include domain knowledge nor consider physical relationships between variables such as known equations and units. Regarding these issues, we propose a Reinforcement-Based Grammar-Guided Symbolic Regression (RBG2-SR) method that constrains the representational space with domain-knowledge using context-free grammar as reinforcement action space. We detail a Partially-Observable Markov Decision Process (POMDP) modeling of the problem and benchmark our approach against state-of-the-art methods. We also analyze the POMDP state definition and propose a physical equation search use case on which we compare our approach to grammar-based and non-grammarbased symbolic regression methods. The experiment results show that our method is competitive against other state-of-the-art methods on the benchmarks and offers the best error-complexity trade-off, highlighting the interest of using a grammar-based method in a real-world scenario.

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