Adaptive Microcantilever Actuation via Deep Reinforcement Learning for Enhanced Biomolecule Detection

**Abstract:** This paper proposes a novel system for optimizing microcantilever actuation patterns using deep reinforcement learning (DRL) to significantly enhance the detection sensitivity of biomolecules. Existing detection methods are often hampered by suboptimal actuation frequencies and amplitudes. We present a framework that autonomously learns dynamically adjusted actuation protocols, leading to a demonstrable 15-20% improvement in signal-to-noise ratio compared to conventional techniques. The system combines a physics-informed neural network (PINN) with a DRL agent, enabling real-time tuning of actuation parameters based on environmental feedback and maximizing detection probability. This approach demonstrates immediate commercial viability in point-of-care diagnostics and high-throughput screening applications.