近日，国防科技大学江天团队研究了基于深度强化学习的多波长光学信息处理。2025年4月15日，《光：科学与应用》杂志发表了这一成果。

多波长光学信息处理系统通常用于光学神经网络和宽带信号处理。然而，它们的有效性往往受到制造、传输和环境因素引起的频率选择性响应的影响。为了缓解这些问题，研究组引入了一种受深度确定性策略梯度训练策略启发的深度强化学习校准（DRC）方法。该方法持续自主地从系统中学习，有效地积累了校准策略的经验知识，与传统方法相比具有更强的适应性。

在基于色散补偿光纤、微环谐振器阵列和马赫曾德干涉仪阵列的系统中，使用多波长光载波作为光源，DRC方法能够在21次迭代内完成相应的信号处理功能。该方法提供了高效准确的控制，使其适用于光卷积计算加速、微波光子信号处理和光网络路由等应用。

附：英文原文

Title: Multi-wavelength optical information processing with deep reinforcement learning

Author: Yan, Qiuquan, Ouyang, Hao, Tao, Zilong, Shen, Meili, Du, Shiyin, Zhang, Jun, Liu, Hengzhu, Hao, Hao, Jiang, Tian

Issue&Volume: 2025-04-15

Abstract: Multi-wavelength optical information processing systems are commonly utilized in optical neural networks and broadband signal processing. However, their effectiveness is often compromised by frequency-selective responses caused by fabrication, transmission, and environmental factors. To mitigate these issues, this study introduces a deep reinforcement learning calibration (DRC) method inspired by the deep deterministic policy gradient training strategy. This method continuously and autonomously learns from the system, effectively accumulating experiential knowledge for calibration strategies and demonstrating superior adaptability compared to traditional methods. In systems based on dispersion compensating fiber, micro-ring resonator array, and Mach-Zehnder interferometer array that use multi-wavelength optical carriers as the light source, the DRC method enables the completion of the corresponding signal processing functions within 21 iterations. This method provides efficient and accurate control, making it suitable for applications such as optical convolution computation acceleration, microwave photonic signal processing, and optical network routing.

DOI: 10.1038/s41377-025-01846-6

Source: https://www.nature.com/articles/s41377-025-01846-6