MPC4RL -- A Software Package for Reinforcement Learning based on Model Predictive Control

Understand

In this paper, we present an early software integrating Reinforcement Learning (RL) with Model Predictive Control (MPC).

• Our aim is to make recent theoretical contributions from the literature more accessible to both the RL and MPC communities.
• We combine standard software tools developed by the RL community, such as Gymnasium, stable-baselines3, or CleanRL with the acados toolbox, a widely-used software package for efficient MPC algorithms.
• Our core contribution is MPC4RL, an open-source Python package that supports learning-enhanced MPC schemes for existing acados implementations.