Mahalanobis-Guided Latent OOD Detection for Hybrid ES-DRL Control in Time-Varying Systems
Researchers have developed a novel method for detecting out-of-distribution (OOD) observations in time-varying systems, particularly for safety-critical applications like particle accelerator control. The approach utilizes a Variational Autoencoder (VAE) trained on normal operational data to identify unseen scenarios. By measuring the Mahalanobis distance in the VAE's latent space, the system can accurately distinguish between in-distribution and OOD states, enabling a seamless switch between a fast Reinforcement Learning (RL) controller and a robust extremum seeking (ES) controller. AI