mlsanityData to 14 Oct 2026

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

arXiv 2604.21030 ↗First posted 22 Apr 2026eess.SY cs.AI cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

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.

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.

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14 of 94 references.

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  1. 2025Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification4 Feb

Other references · 13

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 371
  3. 2017Cautious Model Predictive Control using Gaussian Process Regressioncited by 91
  4. 2019Safe Reinforcement Learning Using Robust MPCcited by 85
  5. 2019Data-driven Economic NMPC using Reinforcement Learningcited by 68
  6. 2020Learning for MPC with Stability & Safety Guaranteescited by 14
  7. 2021Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learningcited by 7
  8. 2022Robust Model Predictive Control with Polytopic Model Uncertainty through System Level Synthesiscited by 6
  9. 2022Model Predictive Control for Dynamic Cloth Manipulation: Parameter Learning and Experimental Validationcited by 5
  10. 2023Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Reviewcited by 4
  11. 2023BP-MPC: Optimizing the Closed-Loop Performance of MPC using BackPropagationcited by 3
  12. 2026Self-Organizing Dual-Buffer Adaptive Clustering Experience Replay (SODACER) for Safe Reinforcement Learning in Optimal Controlcited by 2
1 more
  1. 2024Bi-Level Control of Weaving Sections in Mixed Traffic Environments with Connected and Automated Vehiclescited by 1

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  1. 2025MPCritic: A plug-and-play MPC architecture for reinforcement learning
  2. 2025MPC4RL -- A Software Package for Reinforcement Learning based on Model Predictive Control
  3. 2026Accelerating Reinforcement Learning via MPC Solver-Gradient Guidance for Weights-varying MPC
  4. 2025A view on learning robust goal-conditioned value functions: Interplay between RL and MPC
  5. 2025On Architectures for Combining Reinforcement Learning and Model Predictive Control with Runtime Improvements
  6. 2025Intersection of Reinforcement Learning and Bayesian Optimization for Intelligent Control of Industrial Processes: A Safe MPC-based DPG using Multi-Objective BO
  7. 2023IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control
  8. 2024Towards an Adaptable and Generalizable Optimization Engine in Decision and Control: A Meta Reinforcement Learning Approach