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Safe Reinforcement Learning Using Robust MPC

arXiv 1906.04005 ↗First posted 10 Jun 2019eess.SY cs.SYcited by 72

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Abstract (arXiv)

Reinforcement Learning (RL) has recently impressed the world with stunning results in various applications. While the potential of RL is now well-established, many critical aspects still need to be tackled, including safety and stability issues. These issues, while partially neglected by the RL community, are central to the control community which has been widely investigating them. Model Predictive Control (MPC) is one of the most successful control techniques because, among others, of its ability to provide such guarantees even for uncertain constrained systems. Since MPC is an optimization-based technique, optimality has also often been claimed. Unfortunately, the performance of MPC is highly dependent on the accuracy of the model used for predictions. In this paper, we propose to combine RL and MPC in order to exploit the advantages of both and, therefore, obtain a controller which is optimal and safe. We illustrate the results with a numerical example in simulations.

Key facts. Cited by 72 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 11; 2023: 6; 2024: 18; 2025: 18; 2026: 6.

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  1. 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 169
  2. 2022Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarkingcited by 31
  3. 2023Physics-Informed Machine Learning for Modeling and Control of Dynamical Systemscited by 28
  4. 2021Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrianscited by 20
  5. 2025Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classificationcited by 19
  6. 2022Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertaintycited by 11
  7. 2021Safe Chance Constrained Reinforcement Learning for Batch Process Controlcited by 10
  8. 2021Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correctioncited by 10
  9. 2021Reinforcement Learning of the Prediction Horizon in Model Predictive Controlcited by 9
  10. 2023Revisiting Implicit Differentiation for Learning Problems in Optimal Controlcited by 8
  11. 2024Stability-informed Bayesian Optimization for MPC Cost Function Learningcited by 8
  12. 2024Learning to Boost the Performance of Stable Nonlinear Systemscited by 7
38 more
  1. 2025Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Controlcited by 7
  2. 2022Neural Lyapunov Differentiable Predictive Controlcited by 6
  3. 2024Safe and Stable Closed-Loop Learning for Neural-Network-Supported Model Predictive Controlcited by 6
  4. 2021Regret Analysis of Learning-Based MPC with Partially-Unknown Cost Functioncited by 5
  5. 2022Equipping Black-Box Policies with Model-Based Advice for Stable Nonlinear Controlcited by 5
  6. 2023Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learningcited by 5
  7. 2021Stability-Constrained Markov Decision Processes Using MPCcited by 4
  8. 2021Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Controlcited by 4
  9. 2022Learning Stochastic Parametric Differentiable Predictive Control Policiescited by 4
  10. 2024Deployable Reinforcement Learning with Variable Control Ratecited by 4
  11. 2024Critic as Lyapunov function (CALF): a model-free, stability-ensuring agentcited by 4
  12. 2025Solving Multi-Agent Safe Optimal Control with Distributed Epigraph Form MARLcited by 4
  13. 2021Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehiclescited by 3
  14. 2022CT-DQN: Control-Tutored Deep Reinforcement Learningcited by 3
  15. 2024Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systemscited by 3
  16. 2024Optimal Transport-Assisted Risk-Sensitive Q-Learningcited by 3
  17. 2025Imitation Learning of MPC with Neural Networks: Error Guarantees and Sparsificationcited by 3
  18. 2021A Reinforcement Learning-based Economic Model Predictive Control Framework for Autonomous Operation of Chemical Reactorscited by 2
  19. 2021Adaptive control of a mechatronic system using constrained residual reinforcement learningcited by 2
  20. 2021A note on stabilizing reinforcement learningcited by 2
  21. 2022Pontryagin Optimal Control via Neural Networkscited by 2
  22. 2023Reinforcement Learning with Model Predictive Control for Highway Ramp Meteringcited by 2
  23. 2024DeepSafeMPC: Deep Learning-Based Model Predictive Control for Safe Multi-Agent Reinforcement Learningcited by 2
  24. 2024Learning Model Predictive Control Parameters via Bayesian Optimization for Battery Fast Chargingcited by 2
  25. 2025On the Design of Safe Continual RL Methods for Control of Nonlinear Systemscited by 2
  26. 2025Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)cited by 2
  27. 2022Guided Safe Shooting: model based reinforcement learning with safety constraintscited by 1
  28. 2023A modular framework for stabilizing deep reinforcement learning controlcited by 1
  29. 2024Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigationcited by 1
  30. 2024Incremental Gauss-Newton Descent for Machine Learningcited by 1
  31. 2024Reinforcement Learning Compensated Model Predictive Control for Off-road Driving on Unknown Deformable Terraincited by 1
  32. 2024Stochastic Reinforcement Learning with Stability Guarantees for Control of Unknown Nonlinear Systemscited by 1
  33. 2025Predictive Response Optimization: Using Reinforcement Learning to Fight Online Social Network Abusecited by 1
  34. 2025Exploiting Prior Knowledge in Preferential Learning of Individualized Autonomous Vehicle Driving Stylescited by 1
  35. 2025MAD: A Magnitude And Direction Policy Parametrization for Stability Constrained Reinforcement Learningcited by 1
  36. 2025Accelerated Learning with Linear Temporal Logic using Differentiable Simulationcited by 1
  37. 2026Sharing the Control Authority Between Deep Reinforcement Learning and Model Predictive Control: Application to Multi-Class Transportation Networkscited by 1
  38. 2021Two-step reinforcement learning for model-free redesign of nonlinear optimal regulatorcited by 0

50 most cited of 72 All 72 (JSON)