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

翻译：摘要：高维光谱数据以及预测精度与特征效率之间的根本性权衡，给激光诱导击穿光谱（LIBS）定量分析中的波长选择带来了关键挑战。本文提出了一种新颖的多适配器PPO框架，该框架将波长选择转化为一个强化学习问题，利用交叉注意力机制和多个专用适配器来捕捉复杂的光谱关系。在钢铁和煤数据集上，我们的方法在综合得分上平均比传统粒子群优化（PSO）高出28.4%，在预测精度上高出45.2%。所提出的方法在平衡预测精度与特征效率方面表现出色，在保持可解释性和计算效率的同时，在LIBS定量分析中取得了最先进的结果。我们在此发布了代码和数据集：https://github.com/Hflying/MAPPO