Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning

TL;DR

This paper presents a noise-resilient symbolic regression method that uses reinforcement learning and a dynamic gating module to effectively recover symbolic expressions from high-noise data, outperforming existing approaches.

Contribution

The authors introduce a novel reinforcement learning framework with a noise-resilient gating module and a mixed path entropy bonus for improved symbolic regression under noisy conditions.

Findings

Outperforms baseline methods on high-noise data benchmarks.

Achieves state-of-the-art results on clean data benchmarks.

Demonstrates robustness and efficacy in noisy symbolic regression tasks.

Abstract

Symbolic regression (SR) has emerged as a pivotal technique for uncovering the intrinsic information within data and enhancing the interpretability of AI models. However, current state-of-the-art (sota) SR methods struggle to perform correct recovery of symbolic expressions from high-noise data. To address this issue, we introduce a novel noise-resilient SR (NRSR) method capable of recovering expressions from high-noise data. Our method leverages a novel reinforcement learning (RL) approach in conjunction with a designed noise-resilient gating module (NGM) to learn symbolic selection policies. The gating module can dynamically filter the meaningless information from high-noise data, thereby demonstrating a high noise-resilient capability for the SR process. And we also design a mixed path entropy (MPE) bonus term in the RL process to increase the exploration capabilities of the policy. Experimental results demonstrate that our method significantly outperforms several popular baselines on benchmarks with high-noise data. Furthermore, our method also can achieve sota performance on benchmarks with clean data, showcasing its robustness and efficacy in SR tasks.