# A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems

arXiv: https://arxiv.org/abs/2604.21030 · HTML: https://mlsanity.com/paper/2604.21030/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 94 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 14 of 14)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Cautious Model Predictive Control using Gaussian Process Regression](https://mlsanity.com/paper/1705.10702/) (2017)
- [Safe Reinforcement Learning Using Robust MPC](https://mlsanity.com/paper/1906.04005/) (2019)
- [Data-driven Economic NMPC using Reinforcement Learning](https://mlsanity.com/paper/1904.04152/) (2019)
- [Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification](https://mlsanity.com/paper/2502.02133/) (2025)
- [Learning for MPC with Stability & Safety Guarantees](https://mlsanity.com/paper/2012.07369/) (2020)
- [Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning](https://mlsanity.com/paper/2111.04146/) (2021)
- [Robust Model Predictive Control with Polytopic Model Uncertainty through System Level Synthesis](https://mlsanity.com/paper/2203.11375/) (2022)
- [Model Predictive Control for Dynamic Cloth Manipulation: Parameter Learning and Experimental Validation](https://mlsanity.com/paper/2209.05798/) (2022)
- [Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review](https://mlsanity.com/paper/2308.08496/) (2023)
- [BP-MPC: Optimizing the Closed-Loop Performance of MPC using BackPropagation](https://mlsanity.com/paper/2312.15521/) (2023)
- [Self-Organizing Dual-Buffer Adaptive Clustering Experience Replay (SODACER) for Safe Reinforcement Learning in Optimal Control](https://mlsanity.com/paper/2601.06540/) (2026)
- [Bi-Level Control of Weaving Sections in Mixed Traffic Environments with Connected and Automated Vehicles](https://mlsanity.com/paper/2403.16225/) (2024)

## Abstract (arXiv)

The integration of Model Predictive Control (MPC) and Reinforcement Learning (RL) has emerged as a promising paradigm for constrained decision-making and adaptive control. MPC offers structured optimization, explicit constraint handling, and established stability tools, whereas RL provides data-driven adaptation and performance improvement in the presence of uncertainty and model mismatch. Despite the rapid growth of research on RL--MPC integration, the literature remains fragmented, particularly for control architectures built on linear or linearized predictive models. This paper presents a comprehensive Systematic Literature Review (SLR) of RL--MPC integrations for linear and linearized systems, covering peer-reviewed and formally indexed studies published until 2025. The reviewed studies are organized through a multi-dimensional taxonomy covering RL functional roles, RL algorithm classes, MPC formulations, cost-function structures, and application domains. In addition, a cross-dimensional synthesis is conducted to identify recurring design patterns and reported associations among these dimensions within the reviewed corpus. The review highlights methodological trends, commonly adopted integration strategies, and recurring practical challenges, including computational burden, sample efficiency, robustness, and closed-loop guarantees. The resulting synthesis provides a structured reference for researchers and practitioners seeking to design or analyze RL--MPC architectures based on linear or linearized predictive control formulations.