Summary
Researchers Chengze Xie, Xuan Wang, Ying Ding, Hairong Wang, and Yan Li present a robust reinforcement learning (RL) framework for learning adaptive treatment policies that generalize across patients and remain reliable under biomarker measurement errors and shifts in disease dynamics. The approach models each patient as a hidden context and addresses generalization and robustness in a unified manner via robust contextual Markov decision processes. Applied to adaptive therapy for metastatic prostate cancer in a simulator calibrated to clinical data, the framework yields generalizable and robust policies that substantially outperform the existing adaptive therapy method. Notably, robustness is attained with almost no performance loss when biomarkers are accurate and disease evolves as anticipated, and the resulting policy Pareto-dominates existing methods across a diverse set of scenarios. Published on arXiv as 2610.22336, the work demonstrates that robust RL is a principled approach for learning reliable adaptive treatment strategies in healthcare.
Paper Overview
- Field: Machine Learning
- Authors: Chengze Xie, Xuan Wang, Ying Ding, Hairong Wang, Yan Li
- Published: 2026-10-15
- arXiv: 2610.22336
Summary
The authors 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, the framework addresses generalization and robustness in a unified manner through robust contextual Markov decision processes.
Key Findings
- Applied to adaptive therapy for metastatic prostate cancer in a simulator calibrated to clinical data, the framework obtains generalizable and robust policies that substantially outperform the existing adaptive therapy method.
- Robustness is attained with almost no loss of performance when the biomarker is accurate and the disease evolves as anticipated.
- The resulting policy Pareto-dominates existing methods across a diverse array of scenarios.
Conclusion
Robust RL shows great promise as a principled approach for learning reliable adaptive treatment policies in clinical settings.
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*Automatically collected on 2026-10-17.*
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