  There has been considerable interest in leveraging RL and stochastic control
methods to learn optimal treatment strategies for critically ill patients,
directly from observational data. However, there is significant ambiguity on
the control objective and on the best reward choice for the standard RL
objective. In this work, we propose a clinically motivated control objective
for critically ill patients, for which the value functions have a simple
medical interpretation. Further, we present theoretical results and adapt our
method to a practical Deep RL algorithm, which can be used alongside any value
based Deep RL method. We experiment on a large sepsis cohort and show that our
method produces results consistent with clinical knowledge.
