Title: A Survey of Optimizing ICU Sepsis Treatment Techniques in Reinforcement Learning
Primary area: reinforcement learning
Keywords: AI, RL, Sepsis, Agentic AI

Abstract: Sepsis is a life-threatening condition that affects millions of people worldwide each year, characterized by high mortality and complex clinical trajectories. To address these complexities and the demand for real-time decision-making in clinical practice, Reinforcement Learning(RL), which emphasizes sequential decision-making and long-term reward maximization, has emerged as a promising approach, and numerous studies have sought to apply RL to optimize sepsis treatment. However, to date, no comprehensive survey has systematically analyzed the achievements and limitations of RL in sepsis care.

 To bridge this gap, this paper (1)reviews the research landscape on RL-based approaches to sepsis treatment, (2)examines unresolved challenges and fundamental limitations of RL methods, (3)surveys recent technical advances designed to overcome these limitations and evaluates their strengths and weaknesses, and (4)proposes strategies for translating these methods into real-world clinical applications for sepsis management.

 In conclusion, this study synthesizes the current state and limitations of RL-based sepsis treatment research, underscores the necessity of multi-layered approaches to address these challenges, and highlights future directions, particularly the introduction of Agentic AI systems capable of moving beyond simple treatment recommendations toward autonomous planning and execution.

TL;DR: This paper reviews RL-based approaches to sepsis treatment, analyzes their limitations, and proposes the future integration of Agentic AI systems.