mlsanityData to 14 Oct 2026

Safe model-based Reinforcement Learning via Model Predictive Control and Control Barrier Functions

arXiv 2512.04856 ↗First posted 4 Dec 2025eess.SY cs.SYcited by 1

On the map: Deep reinforcement learning methods → Safe and constrained RL

Abstract (arXiv)

Optimal control strategies are often combined with safety certificates to ensure both performance and safety in safety-critical systems. A prominent example is combining Model Predictive Control (MPC) with Control Barrier Functions (CBF). Yet, efficient tuning of MPC parameters and choosing an appropriate class K function in the CBF is challenging and problem dependent. This paper introduces a safe model-based Reinforcement Learning (RL) framework where a parametric MPC controller incorporates a CBF constraint with a parameterized class K function and serves as a function approximator to learn improved safe control policies from data. Three variations of the framework are introduced, distinguished by the way the optimization problem is formulated and the class K function is parameterized, including neural architectures. Numerical experiments on a discrete double-integrator with static and dynamic obstacles demonstrate that the proposed methods improve performance while ensuring safety.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 18 references that are in the map. First posted on arXiv 4 Dec 2025.

Builds on

10 of 18 references.

References · 10

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019Control Barrier Functions: Theory and Applicationscited by 372
  3. 2016Control Barrier Function Based Quadratic Programs for Safety Critical Systemscited by 287
  4. 2019Data-driven Economic NMPC using Reinforcement Learningcited by 68
  5. 2020Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Functioncited by 49
  6. 2021Enhancing Feasibility and Safety of Nonlinear Model Predictive Control with Discrete-Time Control Barrier Functionscited by 11
  7. 2021Safety-Critical Control using Optimal-decay Control Barrier Function with Guaranteed Point-wise Feasibilitycited by 7
  8. 2023On the Optimality, Stability, and Feasibility of Control Barrier Functions: An Adaptive Learning-Based Approachcited by 4
  9. 2023Reinforcement Learning with Model Predictive Control for Highway Ramp Meteringcited by 4
  10. 2025When Do Transformers Outperform Feedforward and Recurrent Networks? A Statistical Perspectivecited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Stable Transformer-Actor-Critic Model Predictive Control: A Contraction Analysis Approachcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Probabilistically safe and efficient model-based reinforcement learning
  2. 2026A Control Barrier Function-Constrained Model Predictive Control Framework for Safe Reinforcement Learning
  3. 2025Safe Learning Control with Optimality and Stability Guarantees
  4. 2025A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions
  5. 2025Multi-Constraint Safe Reinforcement Learning via Closed-form Solution for Log-Sum-Exp Approximation of Control Barrier Functions
  6. 2026Safe Reinforcement Learning using Ideas from Model Predictive Control
  7. 2024Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems
  8. 2024Safe Deep Model-Based Reinforcement Learning with Lyapunov Functions