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Robust Reinforcement Learning for Dynamic Treatment Regimes under Model and Measurement Uncertainty

L5 · ResearcherResearcharXiv· 10/15/2026

Presents novel methodology for reliable medical treatment policies using robust reinforcement learning with clinical applications.

AI Summary

Researchers developed a robust RL framework for adaptive cancer treatment that outperforms existing methods under biomarker measurement errors and disease dynamics shifts.

Excerpt

We develop a robust reinforcement learning (RL) framework for learning adaptive treatment policies that generalize across patients and remain reliable under errors in the measured biomarker and shifts in the disease dynamics. By modeling the patient as a hidden context, we address generalization and robustness in a unified manner through robust contextual Markov decision processes. Applying our framework to adaptive therapy for metastatic prostate cancer in a simulator calibrated to clinical dat

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