# Safe Reinforcement Learning Using Robust Action Governor

arXiv: https://arxiv.org/abs/2102.10643 · HTML: https://mlsanity.com/paper/2102.10643/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 21 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks](https://mlsanity.com/paper/1903.08792/) (2019)
- [A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems](https://mlsanity.com/paper/1705.01292/) (2017)
- [Action Governor for Discrete-Time Linear Systems with Non-Convex Constraints](https://mlsanity.com/paper/2005.08358/) (2020)

## Built on by (most cited first, 14 of 14)

- [Towards Safe Reinforcement Learning with a Safety Editor Policy](https://mlsanity.com/paper/2201.12427/) (2022)
- [A Survey on Physics Informed Reinforcement Learning: Review and Open Problems](https://mlsanity.com/paper/2309.01909/) (2023)
- [Safe Chance Constrained Reinforcement Learning for Batch Process Control](https://mlsanity.com/paper/2104.11706/) (2021)
- [A Review of Safe Reinforcement Learning Methods for Modern Power Systems](https://mlsanity.com/paper/2407.00304/) (2024)
- [Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents](https://mlsanity.com/paper/2207.04117/) (2022)
- [Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control](https://mlsanity.com/paper/2112.06018/) (2021)
- [Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehicles](https://mlsanity.com/paper/2112.11217/) (2021)
- [Guiding Safe Exploration with Weakest Preconditions](https://mlsanity.com/paper/2209.14148/) (2022)
- [Robust Reinforcement Learning through Efficient Adversarial Herding](https://mlsanity.com/paper/2306.07408/) (2023)
- [A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective](https://mlsanity.com/paper/2503.23650/) (2025)
- [Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learning](https://mlsanity.com/paper/2207.08240/) (2022)
- [Safe Control and Learning Using Generalized Action Governor](https://mlsanity.com/paper/2211.12628/) (2022)
- [Safety-Aware Multi-Agent Learning for Dynamic Network Bridging](https://mlsanity.com/paper/2404.01551/) (2024)
- [Space Processor Computation Time Analysis for Reinforcement Learning and Run Time Assurance Control Policies](https://mlsanity.com/paper/2405.06771/) (2024)

## 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.