← Back to all news
arXiv Machine Learning By Vittorio Lippi

Learning Changes the World and Then What Was Learned has Changed: Training and Deploying Neural Networks in the Control Loop

Read the original on arXiv Machine Learning →

The paper discusses how offline-trained neural networks, when used in closed-loop control systems, can change the state distribution they were trained on, leading to performance degradation. It introduces a taxonomy of failure modes—Closed-Loop Identification Error, deployment-induced Action Error, and continuous Tracking Error—and uses discrete-time sensitivity analysis to explain how these errors arise. The authors propose time-scale separated online adaptation policies to mitigate distribution shift, validated on a nonlinear Duffing oscillator, showing that bounded continuous learning can prevent trajectory divergence while static deployments cannot.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
2d ago

Accelerating Nonlinear MPC by Behavior Cloning with Separated Timescales: Application to Nuclear Reactor Load-Following

The paper introduces Structured Representation Learning for Behavior Cloning (SRLBC), an architecture that separates embeddings for slow, fast, and exogenous state variables in multiscale dynamical systems. Applied to a pressurized water reactor load‑following benchmark, SRLBC outperforms shared‑embedding baselines in cumulative cost, imitation accuracy, and constraint satisfaction, achieving near‑NMPC performance at 15× lower computational cost. When combined with a safety‑augmented approximate NMPC, the resulting controller is 6.8× faster than full NMPC while maintaining at least 99.8% feasibility, and it degrades least under distribution shift.

By Perceval Beja-Battais (CB), Alain Grosset{\^e}te (CB), Nicolas Vayatis (CB)
ragdiffusionbenchmarkssafety
More like this →
arXiv Machine Learning
Sep 22

Autonomous Model Lifecycle Management for Digital Twin-Based Manufacturing Control

The paper introduces a closed‑loop cyber‑physical system for autonomous model lifecycle management in automotive manufacturing, deployed since 2023. It manages paired physics and reinforcement‑learning models, selecting the best candidate through competitive retraining cycles and a Conductor orchestrator that handles plant‑wide inventories and fallback controls. The system incorporates an operator‑trust gate that rejects 23% of policies that deviate from established practice, achieving 28‑45% process stability improvements with no safety incidents.

By Zhengyang (Cissy), Gu, Thomas Cook, Fredaljohn Rohrbaugh, Joseph E. Hernandez, Chris Couch
agentsreinforcement-learningsafety
More like this →