发表机构
The Chinese University of Hong Kong, Shenzhen; University of Illinois Urbana-Champaign（香港中文大学（深圳）; 伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究光子学逆设计中模拟预算受限问题，通过比较基线DQN和六个基于价值的变体，发现决斗式DQN是最可靠配置，能提高品质因数、降低波长误差、增加向上功率，为科学优化中算法收益归因提供可重现框架。
AI 中文摘要
光子晶体表面发射激光器（PCSELs）能将高功率运行与窄发散表面发射相结合，但优化耦合参数需耗费成本高昂的全波模拟。深度Q网络（DQN）优化可重用模拟转换来指导编辑，然而在严格的模拟预算下哪种价值学习机制仍可靠尚不清楚。我们通过在共享目标、模拟器、83次调用预算和四种匹配初始化条件下，比较基线DQN和六个基于价值的变体用于七变量PCSEL设计，来解决这一差距。除了端点，我们还分析样本效率、策略行为和物理响应，以区分有利起点或探索性跳跃带来的学习收益。决斗式DQN是唯一一种在所有四个种子上都有改进的变体。相对于最初评估的设计，其选择的结构将平均品质因数从[具体数值1]提高到[具体数值2]，波长误差降低64%，向上功率提高47%；与基线DQN相比，在相同预算下它们实现了更高的平均值。其他变体没有一致的改进；双DQN重现了基线轨迹，而彩虹精简版显示出高潜力但对种子有很强依赖性。这些结果确定决斗式DQN是模拟预算受限的PCSEL逆设计中测试的最可靠配置，并为在科学优化中归因算法收益提供了可重现框架。源代码可在该https网址公开获取。
英文摘要
Photonic-crystal surface-emitting lasers (PCSELs) can combine high-power operation with narrow-divergence surface emission, but optimizing coupled parameters requires costly full-wave simulations. Deep Q-network (DQN) optimization can reuse simulated transitions to guide edits, yet which value-learning mechanisms remain reliable under tight simulation budgets is unknown. We address this gap by comparing baseline DQN and six value-based variants for a seven-variable PCSEL design under a shared objective, simulator, 83-call budget, and four matched initializations. Beyond endpoints, we analyze sample efficiency, policy behavior, and physical response to separate learning gains from favorable starts or exploratory jumps. Dueling DQN is the only variant to improve all four seeds. Relative to the first evaluated designs, its selected structures increase the mean quality factor () from to (), reduce wavelength error by 64%, and increase upward power by 47%; compared with baseline DQN, they achieve a higher mean under the same budget. Other variants yield no consistent improvement; Double DQN reproduces baseline trajectories, while Rainbow-lite shows high upside but strong seed dependence. These results identify Dueling DQN as the most reliable configuration tested for simulation-budget-limited PCSEL inverse design and provide a reproducible framework for attributing algorithmic gains in scientific optimization. The source code is publicly available at https://github.com/Longying-Wen/PCSEL-RL.
