mlsanityData to 7 Oct 2026

Safe Reinforcement Learning Using Robust Action Governor

arXiv 2102.10643 ↗First posted 21 Feb 2021cs.LG cs.SY eess.SYcited by 12

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Abstract (arXiv)

Reinforcement Learning (RL) is essentially a trial-and-error learning procedure which may cause unsafe behavior during the exploration-and-exploitation process. This hinders the application of RL to real-world control problems, especially to those for safety-critical systems. In this paper, we introduce a framework for safe RL that is based on integration of a RL algorithm with an add-on safety supervision module, called the Robust Action Governor (RAG), which exploits set-theoretic techniques and online optimization to manage safety-related requirements during learning. We illustrate this proposed safe RL framework through an application to automotive adaptive cruise control.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 4 of its 21 references that are in the map. First posted on arXiv 21 Feb 2021.

Builds on

4 of 21 references.

References · 4

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  2. 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 256
  3. 2017A General Safety Framework for Learning-Based Control in Uncertain Robotic Systemscited by 150
  4. 2020Action Governor for Discrete-Time Linear Systems with Non-Convex Constraintscited by 3

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Cited by year

Citing papers per year: 2021: 3; 2022: 5; 2023: 2; 2024: 1; 2025: 1.

12 papers.

  1. 2022Towards Safe Reinforcement Learning with a Safety Editor Policycited by 28
  2. 2023A Survey on Physics Informed Reinforcement Learning: Review and Open Problemscited by 22
  3. 2021Safe Chance Constrained Reinforcement Learning for Batch Process Controlcited by 10
  4. 2021Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Controlcited by 4
  5. 2021Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehiclescited by 3
  6. 2022Guiding Safe Exploration with Weakest Preconditionscited by 3
  7. 2022Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agentscited by 2
  8. 2023Robust Reinforcement Learning through Efficient Adversarial Herdingcited by 2
  9. 2025A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspectivecited by 2
  10. 2022Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learningcited by 1
  11. 2022Safe Control and Learning Using Generalized Action Governorcited by 0
  12. 2024Safety-Aware Multi-Agent Learning for Dynamic Network Bridgingcited by 0