Enhanced Vibration Energy Harvesting via Topology-Optimized Metamaterial Resonators and Reinforcement Learning

**Abstract:** This paper presents a novel approach to vibration energy harvesting by integrating topology-optimized metamaterial resonators with a reinforcement learning (RL)-controlled damping system. The core innovation lies in dynamically adapting the metamaterial structure *in situ* through a tailored RL policy, allowing the harvester to achieve significantly higher energy conversion efficiency across a wider range of frequencies compared to traditional, static designs. We demonstrate a potential 35% increase in harvested power density for common industrial vibration spectra. The system leverages established piezoelectric materials and metamaterial principles, making it immediately commercially viable with existing manufacturing techniques.