Learning-based MPC from Big Data Using Reinforcement Learning

Understand

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.