Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits

TL;DR

This paper introduces multi-agent reinforcement learning algorithms for the inverse design of photonic integrated circuits, outperforming traditional gradient-based methods and providing a new benchmark for sample-efficient photonic design optimization.

Contribution

It presents a novel multi-agent RL framework for PIC inverse design, demonstrating superior performance over gradient-based methods and establishing a benchmark for future research.

Findings

Multi-agent RL outperforms gradient-based optimization in PIC design tasks.

The algorithms efficiently optimize thousands of binary variables with few environment samples.

The work provides a benchmark for sample-efficient RL in photonics inverse design.

Abstract

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.