# Practical Reinforcement Learning of Stabilizing Economic MPC

## TL;DR

This paper introduces an improved reinforcement learning algorithm tailored for stabilizing economic model predictive control, effectively combining RL and MPC to handle nonlinear dynamics and constraints without requiring precise models.

## Contribution

The paper presents a novel RL algorithm specifically designed for MPC, enhancing stability and performance in control tasks with uncertain models.

## Key findings

- The proposed method successfully stabilizes complex control systems in simulations.
- It outperforms traditional MPC in scenarios with model inaccuracies.
- The approach demonstrates robustness and improved closed-loop performance.

## Abstract

Reinforcement Learning (RL) has demonstrated a huge potential in learning optimal policies without any prior knowledge of the process to be controlled. Model Predictive Control (MPC) is a popular control technique which is able to deal with nonlinear dynamics and state and input constraints. The main drawback of MPC is the need of identifying an accurate model, which in many cases cannot be easily obtained. Because of model inaccuracy, MPC can fail at delivering satisfactory closed-loop performance. Using RL to tune the MPC formulation or, conversely, using MPC as a function approximator in RL allows one to combine the advantages of the two techniques. This approach has important advantages, but it requires an adaptation of the existing algorithms. We therefore propose an improved RL algorithm for MPC and test it in simulations on a rather challenging example.