# 模型和测量不确定性下动态治疗方案的鲁棒强化学习

> 原文链接：https://robotics.hellobike.com/papers/2026-10-15-2610-22336
> 频道：papers | 更新日期：2026-10-15 | 标签：arxiv, cs.RO
> 核心摘要：提出鲁棒强化学习框架，在临床数据校准的转移性前列腺癌模拟器上学习可靠自适应治疗策略。

# 模型和测量不确定性下动态治疗方案的鲁棒强化学习

::: v-pre
> 原文链接：[Robust Reinforcement Learning for Dynamic Treatment Regimes under Model and Measurement Uncertainty](https://arxiv.org/abs/2610.22336)（arXiv 2610.22336 · 发布日期：2026-10-15）

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

提出鲁棒强化学习框架，在临床数据校准的转移性前列腺癌模拟器上学习可靠自适应治疗策略。

## 核心要点

- 面向患者泛化、 biomarker 测量误差及疾病动力学变化下的可靠治疗决策问题。
- 将患者建模为隐藏上下文，用鲁棒上下文马尔可夫决策过程统一处理泛化与鲁棒性。
- 在临床数据校准的转移性前列腺癌自适应治疗模拟器中进行验证。
- 所得策略显著优于现有自适应治疗方法，并在多种场景下形成帕累托支配。

**验证范围与局限**：验证限于临床数据校准的转移性前列腺癌自适应治疗模拟器。

## 英文摘要 (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.

作者：薛澄泽, Xuan Wang, Ying Ding, Hairong Wang, Yan Li
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## 原始参考来源 (Sources)

- [arXiv 2610.22336：Robust Reinforcement Learning for Dynamic Treatment Regimes under Model and Measurement Uncertainty](https://arxiv.org/abs/2610.22336)