Robust Reinforcement Learning for Dynamic Treatment Regimes under Model and Measurement Uncertainty
Paper recorded by Signals 4 on 2026-10-15 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-15 on arXiv · recorded by Signals 4 on 2026-10-16
Category: cs.LG · 机器学习 · first seen 2026-10-16
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 fr
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Cite this page: Robust Reinforcement Learning for Dynamic Treatment Regimes under Model and Measurement Uncertainty: the #268 most recent of 749 cs.LG papers we have recorded (as of 2026-10-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/robust-reinforcement-learning-for-dynamic-treatment-regimes-under-model-and-meas.html
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