MetaSeeker: sketching an open invisible space with self-play reinforcement learning

Bei Wu; Chao Qian; Zhedong Wang; Pujing Lin; Erping Li; Hongsheng Chen

doi:10.1038/s41377-025-01876-0

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MetaSeeker: sketching an open invisible space with self-play reinforcement learning

Original Articles

- MetaSeeker: sketching an open invisible space with self-play reinforcement learning
- MetaSeeker: sketching an open invisible space with self-play reinforcement learning
- LSA 2025年14卷第8期页码：2213-2225
- Affiliations：
  
  1.ZJU-UIUC Institute, Interdisciplinary Center for Quantum Information, State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University, Hangzhou, China
  2.ZJU-Hangzhou Global Science and Technology Innovation Center, Zhejiang Key Laboratory of Intelligent Electromagnetic Control and Advanced Electronic Integration, Hangzhou, China
  3.Jinhua Institute of Zhejiang University, Zhejiang University, Jinhua, China
  4.Zhejiang Province Key Laboratory of Intelligent Vehicle Electronics, Hangzhou Dianzi University, Hangzhou, China
- Author bio：
  
  Chao Qian (chaoq@intl.zju.edu.cn)
  Hongsheng Chen (hansomchen@zju.edu.cn)
- Funds：
- DOI：10.1038/s41377-025-01876-0
  中图分类号：
- 收稿日期：2024-11-11，
  
  修回日期：2025-04-07，
  
  录用日期：2025-04-24，
  
  网络出版日期：2025-06-04，
  
  纸质出版日期：2025-08-31
- Accepted：
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MetaSeeker: sketching an open invisible space with self-play reinforcement learning[J]. LSA, 2025,14(8):2213-2225.

Wu, B. et al. MetaSeeker: sketching an open invisible space with self-play reinforcement learning. Light: Science & Applications, 14, 2213-2225 (2025).
MetaSeeker: sketching an open invisible space with self-play reinforcement learning[J]. LSA, 2025,14(8):2213-2225. DOI： 10.1038/s41377-025-01876-0.

Wu, B. et al. MetaSeeker: sketching an open invisible space with self-play reinforcement learning. Light: Science & Applications, 14, 2213-2225 (2025). DOI： 10.1038/s41377-025-01876-0.

摘要

Abstract

Controlling electromagnetic (EM) waves at will is fundamentally important for diverse applications

ranging from optical microcavities

super-resolution imaging

to quantum information processing. Decades ago

the forays into metamaterials and transformation optics have ignited unprecedented interest to create an invisibility cloak—a closed space with any object inside invisible. However

all feat

ures of the scattering waves become stochastic and uncontrollable when EM waves interact with an open and disordered environment

making an open invisible space almost impossible. Counterintuitively

here we for the first time present

an open

cluttered

and dynamic

but invisible space

wherein any freely-moving object maintains invisible. To adapt to the disordered environment

we randomly organize a swarm of reconfigurable metasurfaces

and master them by MetaSeeker

a population-based reinforcement learning (RL). MetaSeeker constructs a narcissistic internal world to mirror the stochastic physical world

capable of autonomous preferment

evolution

and adaptation. In the perception-decision-execution experiment

multiple RL agents automatically interact with the ever-changing environments and integrate a post-hoc explainability to visualize the decision-making process. The hidden objects

such as vehicle cluster and experimenter

can freely scale

race

and track in the invisible space

with the environmental similarity of 99.5%. Our results constitute a monumental stride to reshape the evolutionary landscape of metasurfaces from individual to swarm intelligence and usher in the remote management of entire EM space.

关键词

Keywords

references

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