Using reinforcement learning, a deep Q-network was used to design polarization-independent, perfect solar absorbers. The deep Q-network selected the geometrical properties and materials of a symmetric three-layer metamaterial made up of circular rods on top of two films. The combination of all the possible permutations gives around 500 billion possible designs. In around 30,000 steps, the deep Q-network was able to produce 1250 structures that have an integrated absorption of higher than 90% in the visible region, with a maximum of 97.6% and an integrated absorption of less than 10% in the 8–13 µm wavelength region, with a minimum of 1.37%. A statistical analysis of the distribution of materials and geometrical parameters that make up the solar absorbers is presented.

中文翻译：

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产生与偏振无关的完美太阳能吸收器的深Q网络：统计报告。

通过强化学习，一个深层的Q网络被用来设计偏振无关的完美太阳能吸收器。深层Q网络选择了由两层薄膜顶部的圆棒组成的对称三层超材料的几何特性和材料。所有可能排列的组合给出了大约5,000亿种可能的设计。在大约30,000步中，深层Q网络能够产生1250个结构，这些结构在可见光区域的综合吸收率超过90％，最大吸收率达到97.6％，而在8-阶中的综合吸收率不到10％波长区域为13 µm，最小值为1.37％。对构成太阳能吸收器的材料和几何参数的分布进行了统计分析。