Detecting Model Drifts in Non-Stationary Environment Using Edit Operation Measures

• URL: http://arxiv.org/abs/2509.11367v1
• Date: Sun, 14 Sep 2025 17:48:06 GMT
• Title: Detecting Model Drifts in Non-Stationary Environment Using Edit Operation Measures
• Authors: Chang-Hwan Lee, Alexander Shim,
• Abstract summary: This paper proposes a novel framework to detect such drifts by analyzing the distributional changes in sequences of agent behavior.<n>We introduce a suite of edit operation-based measures to quantify deviations between state-action trajectories generated under stationary and perturbed conditions.<n>Our experiments demonstrate that these measures can effectively distinguish drifted from non-drifted scenarios, even under varying levels of noise.
• Score: 45.88028371034407
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Reinforcement learning (RL) agents typically assume stationary environment dynamics. Yet in real-world applications such as healthcare, robotics, and finance, transition probabilities or reward functions may evolve, leading to model drift. This paper proposes a novel framework to detect such drifts by analyzing the distributional changes in sequences of agent behavior. Specifically, we introduce a suite of edit operation-based measures to quantify deviations between state-action trajectories generated under stationary and perturbed conditions. Our experiments demonstrate that these measures can effectively distinguish drifted from non-drifted scenarios, even under varying levels of noise, providing a practical tool for drift detection in non-stationary RL environments.

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