The authors present a model-free policy-based reinforcement learning model that introduces perturbations on the pattern of a metasurface. The objective is to learn a policy that changes the size of the patches, and therefore the impedance in the sides of an artificially structured material. The proposed iterative model assigns the highest reward when the patch sizes allow the transmission along a constrained path and penalties when the patch sizes make the surface wave radiate to the sides of the metamaterial. After convergence, the proposed model learns an optimal patch pattern that achieves lateral confinement along the metasurface. Simulation results show that the proposed learned-pattern can effectively guide the electromagnetic wave through a metasurface, maintaining its instantaneous eigenstate when the homogeneity is perturbed. Moreover, the pattern learned to prevent reflections by changing the patch sizes adiabatically. The reflection coefficient S 1 , 2 shows that most of the power gets transferred from the source to the destination with the proposed design.

中文翻译：

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通过强化学习横向限制高阻抗表面波

作者提出了一种基于策略的无模型强化学习模型，该模型引入了对超表面模式的扰动。目标是学习改变贴片大小的策略，从而改变人工结构材料侧面的阻抗。当补丁尺寸允许沿受限路径传输时，所提出的迭代模型分配最高奖励，当补丁尺寸使表面波辐射到超材料的侧面时，则分配最高奖励。收敛后，所提出的模型学习最佳补丁模式，实现沿超表面的横向限制。仿真结果表明，所提出的学习模式可以有效地引导电磁波通过超表面，并在均匀性受到扰动时保持其瞬时本征态。而且，该模式学会了通过绝热改变补丁大小来防止反射。反射系数 S 1 , 2 表明大部分功率从源传输到目标设计。