强化学习框架用于癌症自适应治疗：建模不确定性下的稳健策略
Robust Reinforcement Learning for Dynamic Treatment Regimes under Model and Measurement Uncertainty
把鲁棒强化学习用在前列腺癌自适应治疗上，患者当隐藏上下文建模，模拟器里比现有方法更稳还不掉性能，做医疗AI的可以看看。
论文提出一个鲁棒强化学习框架，用于学习跨患者泛化的自适应治疗策略，将患者建模为隐藏上下文，通过鲁棒上下文马尔可夫决策过程统一处理泛化与鲁棒性问题。在根据临床数据校准的转移性前列腺癌自适应治疗模拟器中，该框架生成的策略在生物标志物准确、疾病按预期演化时几乎无性能损失。在多种场景下，所得策略对现有自适应治疗方法形成帕累托占优。
Robust Reinforcement Learning for Dynamic Treatment Regimes under Model and Measurement Uncertainty
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.
