标题： 基于MPC的神经网络策略模仿学习用于人工胰腺 显示英文标题

标题： MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas

摘要： 尽管模型预测控制（MPC）目前是人工胰腺（AP）中胰岛素控制的主要算法，但它通常需要复杂的在线优化，这对于资源受限的医疗设备来说是不可行的。 MPC通常依赖于状态估计，这是一个容易出错的过程。 在本文中，我们介绍了一种新的AP控制方法，该方法使用模仿学习从MPC计算的示范中合成神经网络胰岛素策略。 这种策略计算效率高，并且通过在训练时用完整的状态信息对MPC进行操作，它们可以直接将测量值映射到最优治疗决策，从而绕过状态估计。 我们通过蒙特卡洛Dropout进行贝叶斯推断来学习策略，这使我们能够量化预测不确定性，并由此得出更安全的治疗决策。 我们证明了在特定患者模型下训练的控制策略可以很好地推广（在模型参数和扰动分布方面）到患者群体，始终优于具有状态估计的传统MPC。 显示英文摘要

摘要： Even though model predictive control (MPC) is currently the main algorithm for insulin control in the artificial pancreas (AP), it usually requires complex online optimizations, which are infeasible for resource-constrained medical devices. MPC also typically relies on state estimation, an error-prone process. In this paper, we introduce a novel approach to AP control that uses Imitation Learning to synthesize neural-network insulin policies from MPC-computed demonstrations. Such policies are computationally efficient and, by instrumenting MPC at training time with full state information, they can directly map measurements into optimal therapy decisions, thus bypassing state estimation. We apply Bayesian inference via Monte Carlo Dropout to learn policies, which allows us to quantify prediction uncertainty and thereby derive safer therapy decisions. We show that our control policies trained under a specific patient model readily generalize (in terms of model parameters and disturbance distributions) to patient cohorts, consistently outperforming traditional MPC with state estimation.