In Reinforcement Learning (RL) environments, detecting environment drift is essential for maintaining robust policy performance in production systems, particularly within the context of MLOps. This paper proposes EDSVM, a novel environment drift detection method, which trains Support Vector Machines on undrifted and synthetic drifted examples generated by altering transition dynamics. By using decision function values as drift indicators, our method achieves competitive results compared to state-of-the-art baselines for the area-under-the-curve (AUC) metric. Additionally, we evaluate the performance of EDSVM when integrated with various Change Point Detection algorithms in terms of delay and false alarms, highlighting its potential for automating the monitoring of RL policies and supporting adaptive updates to production pipelines in MLOps workflows.

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