aiqbench analysis
This arXiv preprint applies robust reinforcement learning, specifically robust contextual Markov decision processes, to adaptive cancer therapy in a simulator calibrated to clinical data. The reported gains are relative to an existing adaptive therapy method inside that simulator, so clinical validity remains untested. For ML readers, the main takeaway is the methodological approach to handling hidden patient context, biomarker measurement error, and dynamics shift in a safety-relevant setting.
Robust Reinforcement Learning for Dynamic Treatment Regimes under Model and Measurement Uncertainty
arXiv:2610.22336v1 Announce Type: new Abstract: 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 data, we obtain generalizable and robust policies that substantially outperform the existing adaptive therapy method. In particular, we attain robustness with almost no loss of performance when the biomarker is accurate and the disease evolves as anticipated, and find that the resulting policy Pareto-dominates the existing method across a diverse array of scenarios. Our findings suggest that robust RL holds great promise as a principled methodology for learning reliable adaptive treatment policies.
Abstract: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 data, we obtain generalizable and robust policies that substantially outperform the existing adaptive therapy method. In particular, we attain robustness with almost no loss of performance when the biomarker is accurate and the disease evolves as anticipated, and find that the resulting policy Pareto-dominates the existing method across a diverse array of scenarios. Our findings suggest that robust RL holds great promise as a principled methodology for learning reliable adaptive treatment policies.
Machine Learning (cs.LG)
arXiv:2610.22336 [cs.LG]
(or arXiv:2610.22336v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.22336 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 237 words
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