用多智能体强化学习优化光子芯片设计，提升性能并减少样本需求。
Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits
- 将光子芯片设计分解为数千个二元变量，用多智能体强化学习求解。
- 仅需几千次环境采样即可完成设计，优于传统梯度优化方法。
- 适用于二维与三维光子器件设计，可作高效逆向设计新基准。
光子集成电路（PICs）的逆向设计传统上依赖基于梯度的优化方法，但易陷入局部最优，导致功能不理想。随着光学计算在现代硬件需求中的重要性上升，亟需更自适应的优化算法。本文提出一种强化学习（RL）环境及多智能体强化学习算法，用于PIC设计。通过将设计空间离散化为网格，将设计任务建模为包含数千个二元变量的优化问题。考虑了多个二维与三维设计任务，对应光学计算系统的光子器件。通过将设计空间分解为数千个独立智能体，算法仅需几千次环境交互即可完成优化，在二维和三维任务中均优于现有最先进梯度优化方法。本工作亦可作为光子逆向设计中高效强化学习的基准。
原文摘要 · Abstract (English)
Inverse design of photonic integrated circuits (PICs) has traditionally relied on gradientbased optimization. However, this approach is prone to end up in local minima, which results in suboptimal design functionality. As interest in PICs increases due to their potential for addressing modern hardware demands through optical computing, more adaptive optimization algorithms are needed. We present a reinforcement learning (RL) environment as well as multi-agent RL algorithms for the design of PICs. By discretizing the design space into a grid, we formulate the design task as an optimization problem with thousands of binary variables. We consider multiple two- and three-dimensional design tasks that represent PIC components for an optical computing system. By decomposing the design space into thousands of individual agents, our algorithms are able to optimize designs with only a few thousand environment samples. They outperform previous state-of-the-art gradient-based optimization in both twoand three-dimensional design tasks. Our work may also serve as a benchmark for further exploration of sample-efficient RL for inverse design in photonics.
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