|Q-LEARNING WITH CENSORED DATA.
|Year of Publication
|Goldberg, Yair, and Michael R. Kosorok
|2012 Feb 01
We develop methodology for a multistage-decision problem with flexible number of stages in which the rewards are survival times that are subject to censoring. We present a novel Q-learning algorithm that is adjusted for censored data and allows a flexible number of stages. We provide finite sample bounds on the generalization error of the policy learned by the algorithm, and show that when the optimal Q-function belongs to the approximation space, the expected survival time for policies obtained by the algorithm converges to that of the optimal policy. We simulate a multistage clinical trial with flexible number of stages and apply the proposed censored-Q-learning algorithm to find individualized treatment regimens. The methodology presented in this paper has implications in the design of personalized medicine trials in cancer and in other life-threatening diseases.
|Q-learning with censored data.
|PubMed Central ID
|P01 CA142538 / CA / NCI NIH HHS / United States
P01 CA142538-01 / CA / NCI NIH HHS / United States
P01 CA142538-02 / CA / NCI NIH HHS / United States
P01 CA142538-03 / CA / NCI NIH HHS / United States
Q-LEARNING WITH CENSORED DATA.