mlsanityData to 7 Oct 2026

End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

arXiv 1903.08792 ↗First posted 21 Mar 2019cs.LG cs.SY eess.SYcited by 256

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Abstract (arXiv)

Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process. Real world systems would realistically fail or break before an optimal controller can be learned. To address this issue, we propose a controller architecture that combines (1) a model-free RL-based controller with (2) model-based controllers utilizing control barrier functions (CBFs) and (3) on-line learning of the unknown system dynamics, in order to ensure safety during learning. Our general framework leverages the success of RL algorithms to learn high-performance controllers, while the CBF-based controllers both guarantee safety and guide the learning process by constraining the set of explorable polices. We utilize Gaussian Processes (GPs) to model the system dynamics and its uncertainties. Our novel controller synthesis algorithm, RL-CBF, guarantees safety with high probability during the learning process, regardless of the RL algorithm used, and demonstrates greater policy exploration efficiency. We test our algorithm on (1) control of an inverted pendulum and (2) autonomous car-following with wireless vehicle-to-vehicle communication, and show that our algorithm attains much greater sample efficiency in learning than other state-of-the-art algorithms and maintains safety during the entire learning process.

Key facts. Cited by 256 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Mar 2019.

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Citing papers per year: 2020: 1; 2021: 46; 2022: 41; 2023: 53; 2024: 42; 2025: 32; 2026: 41.

256 papers.

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  1. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 337
  2. 2022Physics-Informed Machine Learning: A Survey on Problems, Methods and Applicationscited by 88
  3. 2021Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPscited by 78
  4. 2021Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificatescited by 71
  5. 2022Constrained Variational Policy Optimization for Safe Reinforcement Learningcited by 69
  6. 2022Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentationcited by 63
  7. 2022Constrained Update Projection Approach to Safe Policy Optimizationcited by 55
  8. 2022Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environmentscited by 54
  9. 2024Agile But Safe: Learning Collision-Free High-Speed Legged Locomotioncited by 53
  10. 2023State-wise Safe Reinforcement Learning: A Surveycited by 48
  11. 2021Safe Reinforcement Learning Using Robust Control Barrier Functionscited by 44
  12. 2024A Survey of Constraint Formulations in Safe Reinforcement Learningcited by 42
38 more
  1. 2021CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothingcited by 41
  2. 2023Safe Reinforcement Learning via Probabilistic Logic Shieldscited by 41
  3. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 40
  4. 2021How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Reviewcited by 36
  5. 2021Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violationscited by 36
  6. 2022Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizabilitycited by 32
  7. 2022Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarkingcited by 31
  8. 2021Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safetycited by 30
  9. 2021Safe Driving via Expert Guided Policy Optimizationcited by 29
  10. 2022Towards Safe Reinforcement Learning with a Safety Editor Policycited by 28
  11. 2022Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Controlcited by 28
  12. 2021Safe Pontryagin Differentiable Programmingcited by 25
  13. 2021Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learningcited by 25
  14. 2022Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guaranteescited by 24
  15. 2022Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopescited by 23
  16. 2022Safe Reinforcement Learning Using Black-Box Reachability Analysiscited by 22
  17. 2023A Survey on Physics Informed Reinforcement Learning: Review and Open Problemscited by 22
  18. 2022Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Pathcited by 21
  19. 2021Neural Network Repair with Reachability Analysiscited by 19
  20. 2021Learning Robust Output Control Barrier Functions from Safe Expert Demonstrationscited by 19
  21. 2022Online Shielding for Reinforcement Learningcited by 19
  22. 2025Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classificationcited by 19
  23. 2021Safe Reinforcement Learning with Chance-constrained Model Predictive Controlcited by 18
  24. 2022Evaluating Model-free Reinforcement Learning toward Safety-critical Taskscited by 18
  25. 2022SABLAS: Learning Safe Control for Black-box Dynamical Systemscited by 17
  26. 2022GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systemscited by 17
  27. 2021Learning from Demonstrations for Autonomous Soft-tissue Retractioncited by 15
  28. 2022Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interactioncited by 15
  29. 2021Safe Exploration by Solving Early Terminated MDPcited by 13
  30. 2021Safe Autonomous Racing via Approximate Reachability on Ego-visioncited by 13
  31. 2021Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learningcited by 13
  32. 2023Trajectory Generation, Control, and Safety with Denoising Diffusion Probabilistic Modelscited by 13
  33. 2025On Almost Surely Safe Alignment of Large Language Models at Inference-Timecited by 13
  34. 2021Safe Reinforcement Learning Using Robust Action Governorcited by 12
  35. 2021Risk-averse autonomous systems: A brief history and recent developments from the perspective of optimal controlcited by 12
  36. 2021Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approachcited by 12
  37. 2022Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarioscited by 12
  38. 2023Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learningcited by 12

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