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

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The paper introduces a robust reinforcement learning framework that learns adaptive treatment policies for patients, accounting for uncertainties in biomarker measurements and disease dynamics. By treating the patient as a hidden context, the authors unify generalization and robustness through robust contextual Markov decision processes. Applied to a simulator of metastatic prostate cancer calibrated to clinical data, the resulting policies outperform existing adaptive therapy methods, achieving robustness with minimal performance loss under accurate biomarkers and expected disease evolution, and Pareto-dominating the prior approach across varied scenarios.

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