Interactive Symbolic Regression through Offline Reinforcement Learning: A Co-Design Framework

TL;DR

This paper introduces Sym-Q, an interactive reinforcement learning framework for symbolic regression that effectively incorporates domain knowledge and user interaction, outperforming existing methods on benchmarks and real-world cases.

Contribution

The paper presents Sym-Q, a novel offline reinforcement learning approach for symbolic regression that supports interactive co-design with domain experts, unlike previous transformer-based models.

Findings

Sym-Q surpasses existing SR algorithms on SSDNC benchmark.

Interactive co-design improves Sym-Q's performance on real-world problems.

Sym-Q enables dynamic user interaction during equation discovery.

Abstract

Symbolic Regression (SR) holds great potential for uncovering underlying mathematical and physical relationships from observed data. However, the vast combinatorial space of possible expressions poses significant challenges for both online search methods and pre-trained transformer models. Additionally, current state-of-the-art approaches typically do not consider the integration of domain experts' prior knowledge and do not support iterative interactions with the model during the equation discovery process. To address these challenges, we propose the Symbolic Q-network (Sym-Q), an advanced interactive framework for large-scale symbolic regression. Unlike previous large-scale transformer-based SR approaches, Sym-Q leverages reinforcement learning without relying on a transformer-based decoder. This formulation allows the agent to learn through offline reinforcement learning using any type of tree encoder, enabling more efficient training and inference. Furthermore, we propose a co-design mechanism, where the reinforcement learning-based Sym-Q facilitates effective interaction with domain experts at any stage of the equation discovery process. Users can dynamically modify generated nodes of the expression, collaborating with the agent to tailor the mathematical expression to best fit the problem and align with the assumed physical laws, particularly when there is prior partial knowledge of the expected behavior. Our experiments demonstrate that the pre-trained Sym-Q surpasses existing SR algorithms on the challenging SSDNC benchmark. Moreover, we experimentally show on real-world cases that its performance can be further enhanced by the interactive co-design mechanism, with Sym-Q achieving greater performance gains than other state-of-the-art models. Our reproducible code is available at https://github.com/EPFL-IMOS/Sym-Q.