This paper presents an approach for learning Model Predictive Control (MPC) schemes directly from data using Reinforcement Learning (RL) methods. The state-of-the-art learning methods use RL to improve the performance of parameterized MPC schemes. However, these learning algorithms are often gradient-based methods that require frequent evaluations of computationally expensive MPC schemes, thereby restricting their use on big datasets. We propose to tackle this issue by using tools from RL to learn a parameterized MPC scheme directly from data in an offline fashion. Our approach derives an MPC scheme without having to solve it over the collected dataset, thereby eliminating the computational complexity of existing techniques for big data. We evaluate the proposed method on three simulated experiments of varying complexity.
翻译:本文介绍了一种直接从使用强化学习方法的数据中学习模型预测控制(MPC)计划的方法。最先进的学习方法使用RL来改进参数化的MPC计划的业绩。然而,这些学习算法往往是基于梯度的方法,需要经常评估计算昂贵的MPC计划,从而限制其在大数据集中的使用。我们建议通过使用来自RL的工具直接从离线数据中学习参数化的MPC计划来解决这一问题。我们的方法产生一个MPC计划,而不必在所收集的数据集中加以解决,从而消除了现有大数据技术的计算复杂性。我们评估了三种模拟复杂实验的拟议方法。