用强化学习选光谱波长，准确率比传统方法高45%。
Multi-Adapter PPO: A Cross-Attention Enhanced Wavelength Selection Framework for LIBS Quantitative Analysis
- 将波长选择建模为强化学习问题，引入交叉注意力与多适配器
- 在钢和煤数据集上预测准确率提升45.2%，综合评分提高28.4%
- 兼顾高精度、低特征冗余，适合工业现场的实时定量分析
激光诱导击穿光谱（LIBS）定量分析面临高维光谱数据带来的波长选择挑战，以及预测精度与特征效率之间的根本权衡。本文提出一种新型Multi-Adapter PPO框架，将波长选择转化为强化学习问题，利用交叉注意力机制和多个专用适配器捕捉复杂的光谱关系。该方法在钢和煤数据集上相比传统粒子群优化（PSO）平均提升28.4%的综合评分和45.2%的预测准确率。所提方法在保持可解释性和计算效率的同时，实现了LIBS定量分析的最先进性能。代码与数据集已公开：https://github.com/Hflying/MAPPO
原文摘要 · Abstract (English)
Laser-induced breakdown spectroscopy (LIBS) quantitative analysis faces critical challenges in wavelength selection due to high-dimensional spectral data and the fundamental trade-off between prediction accuracy and feature efficiency. This paper presents a novel Multi-Adapter PPO framework that transforms wavelength selection into a reinforcement learning problem, leveraging cross-attention mechanisms and multiple specialized adapters to capture complex spectral relationships. Our approach outperforms traditional Particle Swarm Optimization (PSO) by an average of 28.4\% in comprehensive score and 45.2\% in prediction accuracy across steel and coal datasets. The proposed method demonstrates superior performance in balancing prediction accuracy with feature efficiency, achieving state-of-the-art results in LIBS quantitative analysis while maintaining interpretability and computational efficiency. We released our code and dataset here: https://github.com/Hflying/MAPPO
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