论文标题
基于模型的1糖型血糖控制类型的增强学习
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control
论文作者
论文摘要
在本文中，我们调查了基于模型的增强学习的使用，以帮助具有胰岛素剂量决策的1型糖尿病患者。所提出的体系结构由多个回声状态网络组成，以预测血糖水平以及用于计划的模型预测控制器。回声状态网络是一个经常性神经网络的版本，它使我们能够以在线方式学习时间序列数据的长期依赖项。此外，我们解决了更健壮控制的不确定性的量化。在这里，我们使用回声状态网络的集合来捕获模型（认知）不确定性。我们通过FDA批准的UVA/Padova 1型糖尿病模拟器评估了该方法，并将结果与基线算法（例如基底支柱控制器和深Q学习）进行了比较。结果表明，基于模型的增强学习算法可以表现出比测试的大多数虚拟型糖尿病人概况的基线算法相同或更好。
In this paper we investigate the use of model-based reinforcement learning to assist people with Type 1 Diabetes with insulin dose decisions. The proposed architecture consists of multiple Echo State Networks to predict blood glucose levels combined with Model Predictive Controller for planning. Echo State Network is a version of recurrent neural networks which allows us to learn long term dependencies in the input of time series data in an online manner. Additionally, we address the quantification of uncertainty for a more robust control. Here, we used ensembles of Echo State Networks to capture model (epistemic) uncertainty. We evaluated the approach with the FDA-approved UVa/Padova Type 1 Diabetes simulator and compared the results against baseline algorithms such as Basal-Bolus controller and Deep Q-learning. The results suggest that the model-based reinforcement learning algorithm can perform equally or better than the baseline algorithms for the majority of virtual Type 1 Diabetes person profiles tested.
