Learning-based MPC from Big Data Using Reinforcement Learning
This addresses a bottleneck for researchers and practitioners in control systems by enabling more efficient learning from large datasets, though it appears incremental as it builds on existing RL and MPC frameworks.
The paper tackles the computational inefficiency of gradient-based reinforcement learning methods for learning Model Predictive Control schemes from big data by proposing an offline approach that eliminates the need to solve MPC over the dataset, achieving results on three simulated experiments.
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.