Multi-Agent Learning for Precise and Collaborative Control of Anesthetics in TIVA.

Abstract

Precise and collaborative control of multiple anesthetics in Total Intravenous Anesthesia (TIVA) is essential for ensuring patient safety and maintaining the target depth of anesthesia (DoA). However, existing automated anesthesia control methods often fail to effectively capture the complex synergistic interactions between anesthetics and lack adaptability to patient-specific physiological variability, thereby limiting their clinical applicability. To address these issues, we propose AnesMADRL, a novel Multi-Agent Deep Reinforcement Learning (MADRL)-based framework that leverages the Counterfactual Multi-Agent algorithm for effective credit assignment between agents controlling propofol and remifentanil, and adopts a continuous action space to enable fine-grained dose adjustments. Furthermore, AnesMADRL integrates comprehensive patient-specific physiological indicators and employs a random forest-based simulator to generate dynamic and diverse training environments. Experimental results show that AnesMADRL significantly outperforms baseline methods and human expertise in terms of anesthetic efficiency and total drug consumption. Relative to human expertise, AnesMADRL achieves roughly twofold efficiency while reducing total dose to about one-half, highlighting its potential to enhance patient safety and optimize clinical outcomes.