无模型的光学处理器使用现场强化学习与近距离政策优化.
Yuhang Li1,2, Shiqi Chen1,2, Tingyu Gong3
1Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
Light, science & applications
|December 31, 2025
概括
我们开发了一种无模型增强学习方法,用于训练衍射光学处理器. 这种方法使光学系统的现场优化速度更快,更稳定,并考虑到现实世界的缺陷.
科学领域:
- 计算物理学的计算物理.
- 光学工程的光学工程.
- 机器学习 机器学习
背景情况:
- 衍射光学网络为高速,节能计算提供了潜力.
- 优化衍射层是由于硬件缺陷和噪声而具有挑战性的.
- 现有的 in situ 方法存在缓慢的融合和不稳定性.
研究的目的:
- 引入一种新的强化学习方法,用于在现场训练衍射光学处理器.
- 克服现有的物理光学系统优化方法的局限性.
主要方法:
- 利用近接政策优化 (PPO),一种无模型的强化学习算法.
- 应用PPO用于直接在物理衍射光学系统上进行现场培训.
- 提高了测量数据的有效重复使用率,并限制了政策更新以保持稳定性.
主要成果:
- 在各种任务中显示出更快的融合和更好的性能:能量聚焦,图像生成,偏差校正和分类.
- 通过模拟和物理实验验验证了方法.
- 展示了该方法处理未知的现实世界的缺陷的能力.
结论:
- 无模型强化学习方法为训练复杂光学系统提供了一个可扩展的框架.
- 这种方法使得在现实实验约束下更快,更准确和更强大的现场优化成为可能.
- 消除了对显式系统建模或先前知识的需求.
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