  Sepsis is a dangerous condition that is a leading cause of patient mortality.
Treating sepsis is highly challenging, because individual patients respond very
differently to medical interventions and there is no universally agreed-upon
treatment for sepsis. In this work, we explore the use of continuous
state-space model-based reinforcement learning (RL) to discover high-quality
treatment policies for sepsis patients. Our quantitative evaluation reveals
that by blending the treatment strategy discovered with RL with what clinicians
follow, we can obtain improved policies, potentially allowing for better
medical treatment for sepsis.
