mlsanityData to 13 Oct 2026

Mahalanobis-Guided Latent OOD Detection for Hybrid ES-DRL Control in Time-Varying Systems

arXiv 2606.11474 ↗First posted 9 Jun 2026cs.LG cs.SY eess.SYcited by 1

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Abstract (arXiv)

In this paper, we study Mahalanobis-guided latent out-of-distribution (OOD) detection for test-time RL controller switching in nonlinear time-varying systems. RL controllers can quickly control high-dimensional systems within the training distribution, but their performance can degrade when time-varying dynamics produce unseen observations. We consider a combined ES--DRL controller, where RL provides fast in-distribution actions and bounded extremum seeking (ES) provides robust model-independent control under OOD operation. The key challenge is deciding when to switch. We train a variational autoencoder (VAE) on in-distribution beam-profile observations and use Mahalanobis distance in the VAE latent space to detect OOD beam profiles at test time. This OOD decision sets a binary switch that selects either the RL controller or the ES controller. We evaluate the approach in safety-critical particle accelerator control. In this setting, spatial magnet motion creates OOD beam profiles that were not seen during RL training. Visualization of the VAE latent space shows that the proposed method identifies this OOD scenario and provides an interpretable signal for switching between RL and ES in the combined controller.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 9 Jun 2026.

Builds on

16 of 24 references.

References · 16

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  12. 2025Efficient Action-Constrained Reinforcement Learning via Acceptance-Rejection Method and Augmented MDPscited by 8
4 more
  1. 2021Online Policies for Real-Time Control Using MRAC-RLcited by 5
  2. 2021A Distance-based Anomaly Detection Framework for Deep Reinforcement Learningcited by 5
  3. 2025Improved Robustness of Deep Reinforcement Learning for Control of Time-Varying Systems by Bounded Extremum Seekingcited by 3
  4. 2026Planning for Change: Reinforcement Learning Combined with Bounded Extremum Seeking for Robotic Control under Distribution Shiftcited by 2

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Citing papers per year: 2026: 1.

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  1. 2026Bidirectional Autoregressive Latent Diffusion for Forward and Inverse Magnetohydrodynamicscited by 1