用强化学习逆向设计随机系统的传输矩阵，实现精准光场调控。
Inverse design of the transmission matrix in a random system using Reinforcement Learning
- 用近端策略优化算法在非凸目标空间中优化传输矩阵。
- 实现固定功率比零透射、简并特征点及均匀信道参与等特殊模式。
- 适合光学逆设计、智能材料与量子信息领域研究者参考。
本文提出一种基于强化学习的散射系统逆向设计方法，通过修改传输矩阵实现特定功能。采用近端策略优化（Proximal Policy Optimization）算法，在高度非凸的目标函数空间中进行搜索，成功构建三类传输矩阵：(1) 秩-1矩阵下的固定功率转换比与零透射模式；(2) 特征值简并的奇异点及单向模式转换；(3) 当传输本征值简并时，强制各通道均匀参与。该方法为复杂随机系统中的功能定制提供了新路径。
原文摘要 · Abstract (English)
This work presents an approach to the inverse design of scattering systems by modifying the transmission matrix using reinforcement learning. We utilize Proximal Policy Optimization to navigate the highly non-convex landscape of the object function to achieve three types of transmission matrices: (1) Fixed-ratio power conversion and zero-transmission mode in rank-1 matrices, (2) exceptional points with degenerate eigenvalues and unidirectional mode conversion, and (3) uniform channel participation is enforced when transmission eigenvalues are degenerate.
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