# A Reinforcement learning method for Optical Thin-Film Design

arXiv: https://arxiv.org/abs/2102.09398 · HTML: https://mlsanity.com/paper/2102.09398/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 38 references that are in the map.
- First posted on arXiv 13 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Automated Optical Multi-layer Design via Deep Reinforcement Learning](https://mlsanity.com/paper/2006.11940/) (2020)

## Built on by (most cited first, 1 of 1)

- [Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits](https://mlsanity.com/paper/2506.18627/) (2025)

## Abstract (arXiv)

Machine learning, especially deep learning, is dramatically changing the methods associated with optical thin-film inverse design. The vast majority of this research has focused on the parameter optimization (layer thickness, and structure size) of optical thin-films. A challenging problem that arises is an automated material search. In this work, we propose a new end-to-end algorithm for optical thin-film inverse design. This method combines the ability of unsupervised learning, reinforcement learning(RL) and includes a genetic algorithm to design an optical thin-film without any human intervention. Furthermore, with several concrete examples, we have shown how one can use this technique to optimize the spectra of a multi-layer solar absorber device.