Reinforcement learning for closed-loop control of anesthesia.

This thesis explores advanced reinforcement learning (RL) algorithms and their in- novative application in anesthesia control using a MATLAB/Simulink virtual pa- tient simulation environment. Key RL techniques such as Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), Soft Actor-Critic (SAC), Proximal Policy Optimization (PPO), and Trust Region Policy Optimization (TRPO) are analyzed for their contributions to enhancing RL system stability, efficiency, and effectiveness. DDPG is noted for handling continuous ac- tion spaces, while TD3 mitigates overestimation bias through twin critic networks and delayed updates. SAC leverages entropy regularization to balance exploration and exploitation, PPO employs a surrogate objective for stable training, and TRPO ensures risk mitigation with conservative policy updates. These RL algorithms were trained, tested, and utilized for control purposes within the MATLAB/Simulink virtual patient simulation environment. This platform en- abled precise and adaptive drug administration, which is critical for patient safety and optimal surgical outcomes. The adaptability of RL algorithms addresses the variability in patient responses, enabling real-time optimization of drug dosages and enhancing the robustness of anesthesia delivery systems. The potential for fully auto- mated anesthesia systems is explored, highlighting challenges such as data collection, regulatory acceptance, and the need for interdisciplinary collaboration between tech- nologists, clinicians, and regulators. In conclusion, this thesis underscores the transformative potential of advanced RL algorithms in healthcare, particularly in anesthesia control. By leveraging the adap- tive learning capabilities of RL within a robust simulation environment, significant improvements in precision, adaptability, and safety of medical procedures are achiev- able. This research contributes to the broader field of intelligent healthcare systems, demonstrating how RL can revolutionize patient care and clinical outcomes