Double-deep Q-learning to increase the efficiency of metasurface holograms

We use a double deep Q-learning network (DDQN) to find the right material type and the optimal geometrical design for metasurface holograms to reach high efficiency. The DDQN acts like an intelligent sweep and could identify the optimal results in ~5.7 billion states after only 2169 steps. The optim...

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Detalles Bibliográficos
Autores principales: Sajedian, Iman, Lee, Heon, Rho, Junsuk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6662763/
https://www.ncbi.nlm.nih.gov/pubmed/31358783
http://dx.doi.org/10.1038/s41598-019-47154-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6662763/
https://www.ncbi.nlm.nih.gov/pubmed/31358783
http://dx.doi.org/10.1038/s41598-019-47154-z

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