Sepsis is the third leading cause of death worldwide and the main cause of mortality in hospitals1–3, but the best treatment strategy remains uncertain. In particular, evidence suggests that current practices in the administration of intravenous fluids and vasopressors are suboptimal and likely induce harm in a proportion of patients1,4–6. To tackle this sequential decision-making problem, we developed a reinforcement learning agent, the artificial intelligence (AI) Clinician, which learns from data to predict patient dynamics given specific treatment decisions. Our agent extracted implicit knowledge from an amount of patient data that exceeds many-fold the life-time experience of human clinicians and learned optimal treatment by having analysed myriads of (mostly sub-optimal) treatment decisions. We demonstrate that the value of the AI Clinician’s selected treatment is on average reliably higher than the human clinicians. In a large validation cohort independent from the training data, mortality was lowest in patients where clinicians’ actual doses matched the AI policy. Our model provides individualized and clinically interpretable treatment decisions for sepsis that could improve patient outcomes.