mlsanityData to 7 Oct 2026

Safe Exploration in Model-based Reinforcement Learning using Control Barrier Functions

arXiv 2104.08171 ↗First posted 16 Apr 2021cs.LG cs.SY eess.SYcited by 8

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Abstract (arXiv)

This paper develops a model-based reinforcement learning (MBRL) framework for learning online the value function of an infinite-horizon optimal control problem while obeying safety constraints expressed as control barrier functions (CBFs). Our approach is facilitated by the development of a novel class of CBFs, termed Lyapunov-like CBFs (LCBFs), that retain the beneficial properties of CBFs for developing minimally-invasive safe control policies while also possessing desirable Lyapunov-like qualities such as positive semi-definiteness. We show how these LCBFs can be used to augment a learning-based control policy to guarantee safety and then leverage this approach to develop a safe exploration framework in a MBRL setting. We demonstrate that our approach can handle more general safety constraints than comparative methods via numerical examples.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 13 of its 44 references that are in the map. First posted on arXiv 16 Apr 2021.

Builds on

13 of 44 references.

References · 13

  1. 2019Control Barrier Functions: Theory and Applicationscited by 332
  2. 2016Control Barrier Function Based Quadratic Programs for Safety Critical Systemscited by 261
  3. 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 256
  4. 2017A General Safety Framework for Learning-Based Control in Uncertain Robotic Systemscited by 150
  5. 2019Learning for Safety-Critical Control with Control Barrier Functionscited by 66
  6. 2019Data-driven Economic NMPC using Reinforcement Learningcited by 55
  7. 2016Robustness of Control Barrier Functions for Safety Critical Controlcited by 29
  8. 2019Adaptive Safety with Control Barrier Functionscited by 25
  9. 2020Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertaintycited by 13
  10. 2020Approximate Optimal Control for Safety-Critical Systems with Control Barrier Functionscited by 6
  11. 2013Concurrent learning-based approximate optimal regulationcited by 5
  12. 2015Efficient model-based reinforcement learning for approximate online optimalcited by 2
1 more
  1. 2015State Following (StaF) Kernel Functions for Function Approximationcited by 1

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 3.

8 papers.

  1. 2023A Survey on Physics Informed Reinforcement Learning: Review and Open Problemscited by 22
  2. 2025CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functionscited by 8
  3. 2024Approximation with Random Shallow ReLU Networks with Applications to Model Reference Adaptive Controlcited by 4
  4. 2022Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functionscited by 2
  5. 2024Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigationcited by 1
  6. 2024Function Gradient Approximation with Random Shallow ReLU Networks with Control Applicationscited by 1
  7. 2025Safe Learning Control with Optimality and Stability Guaranteescited by 0
  8. 2025Game-Theoretic Risk-Shaped Reinforcement Learning for Safe Autonomous Drivingcited by 0