Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning
This addresses a critical bottleneck in symbolic regression for researchers and practitioners dealing with noisy datasets, though it appears incremental as it builds on existing reinforcement learning approaches.
The paper tackled the problem of symbolic regression failing on high-noise data by introducing a noise-resilient method that uses reinforcement learning with a gating module, achieving state-of-the-art performance on benchmarks with both noisy and clean data.
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.