MetaSeeker: sketching an open invisible space with self-play reinforcement learning - Bei Wu; Chao Qian; Zhedong Wang; Pujing Lin; Erping Li; Hongsheng Chen (Light: Science & Applications)
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 features 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.