Reinforcement Learning
Neuro-Dynamic Programming
Learning to Predict by the Methods of Temporal Differences
Machine Learning
SIAM Journal on Control and Optimization
Genetic network programming with actor-critic and its application to stock trading model
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A survey of insulin-dependent diabetes-part II: control methods
International Journal of Telemedicine and Applications - Regular issue
Fuzzy-PID Control for the Regulation of Blood Glucose in Diabetes
GCIS '09 Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 02
Computer Methods and Programs in Biomedicine
Physiologic insulin delivery with insulin feedback: A control systems perspective
Computer Methods and Programs in Biomedicine
Brief paper: Average cost temporal-difference learning
Automatica (Journal of IFAC)
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A novel adaptive approach for glucose control in individuals with type 1 diabetes under sensor-augmented pump therapy is proposed. The controller, is based on Actor-Critic (AC) learning and is inspired by the principles of reinforcement learning and optimal control theory. The main characteristics of the proposed controller are (i) simultaneous adjustment of both the insulin basal rate and the bolus dose, (ii) initialization based on clinical procedures, and (iii) real-time personalization. The effectiveness of the proposed algorithm in terms of glycemic control has been investigated in silico in adults, adolescents and children under open-loop and closed-loop approaches, using announced meals with uncertainties in the order of +/-25% in the estimation of carbohydrates. The results show that glucose regulation is efficient in all three groups of patients, even with uncertainties in the level of carbohydrates in the meal. The percentages in the A+B zones of the Control Variability Grid Analysis (CVGA) were 100% for adults, and 93% for both adolescents and children. The AC based controller seems to be a promising approach for the automatic adjustment of insulin infusion in order to improve glycemic control. After optimization of the algorithm, the controller will be tested in a clinical trial.