Missing Data and Uncertainty in Batch Reinforcement Learning
نویسندگان
چکیده
We present a method for batch Q-learning when some of the data are missing. Our approach uses Bayesian multiple imputation to build Q-functions using all of the observed data. This is safer than using complete case analysis, i.e. throwing out incomplete training samples, because it can avoid non-response bias when possible. We also present a method for assessing confidence in the learned Qfunctions, and demonstrate our methods on real multistage clinical trial data.
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تاریخ انتشار 2008