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

arXiv: https://arxiv.org/abs/2606.11474 · HTML: https://mlsanity.com/paper/2606.11474/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Builds on (most cited first, 16 of 16)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://mlsanity.com/paper/1610.00633/) (2016)
- [Reliable, Adaptable, and Attributable Language Models with Retrieval](https://mlsanity.com/paper/2403.03187/) (2024)
- [Automated Reinforcement Learning (AutoRL): A Survey and Open Problems](https://mlsanity.com/paper/2201.03916/) (2022)
- [Safe Reinforcement Learning by Imagining the Near Future](https://mlsanity.com/paper/2202.07789/) (2022)
- [Actor-Critic Model Predictive Control: Differentiable Optimization meets Reinforcement Learning for Agile Flight](https://mlsanity.com/paper/2306.09852/) (2023)
- [Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and Detection](https://mlsanity.com/paper/2404.07099/) (2024)
- [Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning](https://mlsanity.com/paper/2001.00496/) (2019)
- [Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and Results](https://mlsanity.com/paper/2107.04982/) (2021)
- [Policy Entropy for Out-of-Distribution Classification](https://mlsanity.com/paper/2005.12069/) (2020)
- [Efficient Action-Constrained Reinforcement Learning via Acceptance-Rejection Method and Augmented MDPs](https://mlsanity.com/paper/2503.12932/) (2025)
- [Online Policies for Real-Time Control Using MRAC-RL](https://mlsanity.com/paper/2103.16551/) (2021)
- [A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning](https://mlsanity.com/paper/2109.09889/) (2021)
- [Improved Robustness of Deep Reinforcement Learning for Control of Time-Varying Systems by Bounded Extremum Seeking](https://mlsanity.com/paper/2510.02490/) (2025)
- [Planning for Change: Reinforcement Learning Combined with Bounded Extremum Seeking for Robotic Control under Distribution Shift](https://mlsanity.com/paper/2604.01142/) (2026)

## Built on by (most cited first, 1 of 1)

- [Bidirectional Autoregressive Latent Diffusion for Forward and Inverse Magnetohydrodynamics](https://mlsanity.com/paper/2606.29620/) (2026)

## 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.