Dynamic metasurface control using deep reinforcement learning

DOI10.1016/j.matcom.2022.02.016OpenAlexW4212958977MaRDI QIDQ2139890

Liang Li, Ying Zhao, Jonathan Viquerat, Stéphane Lanteri

Publication date: 19 May 2022

Published in: Mathematics and Computers in Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.matcom.2022.02.016

zbMATH Keywords

artificial neural networksdeep reinforcement learningdynamic metasurfaceproximal policy optimization

Mathematics Subject Classification ID

Biology and other natural sciences (92-XX) Systems theory; control (93-XX)

Uses Software

• OpenAI Gym
• GitHub
• Stable Baselines
• DARLA

Cites Work

• Unnamed Item
• Multilayer feedforward networks are universal approximators
• A review on deep reinforcement learning for fluid mechanics
• General Metasurface Synthesis Based on Susceptibility Tensors
• Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control
• Control of chaotic systems by deep reinforcement learning
• DARLA

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