# Searching for Optimal Runtime Assurance via Reachability and Reinforcement Learning

arXiv: https://arxiv.org/abs/2310.04288 · HTML: https://mlsanity.com/paper/2310.04288/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 53 references that are in the map.
- First posted on arXiv 6 Oct 2023.

## Builds on (most cited first, 19 of 19)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees](https://mlsanity.com/paper/1909.05304/) (2019)
- [Omega-Regular Objectives in Model-Free Reinforcement Learning](https://mlsanity.com/paper/1810.00950/) (2018)
- [Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning](https://mlsanity.com/paper/1909.07299/) (2019)
- [Safe Reinforcement Learning via Shielding under Partial Observability](https://mlsanity.com/paper/2204.00755/) (2022)
- [A Learning Based Approach to Control Synthesis of Markov Decision Processes for Linear Temporal Logic Specifications](https://mlsanity.com/paper/1409.5486/) (2014)
- [Run Time Assurance for Safety-Critical Systems: An Introduction to Safety Filtering Approaches for Complex Control Systems](https://mlsanity.com/paper/2110.03506/) (2021)
- [SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems](https://mlsanity.com/paper/1808.07921/) (2018)
- [Formal Controller Synthesis for Continuous-Space MDPs via Model-Free Reinforcement Learning](https://mlsanity.com/paper/2003.00712/) (2020)
- [The Black-Box Simplex Architecture for Runtime Assurance of Autonomous CPS](https://mlsanity.com/paper/2102.12981/) (2021)
- [Model-Free Reinforcement Learning for Symbolic Automata-encoded Objectives](https://mlsanity.com/paper/2202.02404/) (2022)
- [Online monitoring for safe pedestrian-vehicle interactions](https://mlsanity.com/paper/1910.05599/) (2019)
- [Comparing Run Time Assurance Approaches for Safe Spacecraft Docking](https://mlsanity.com/paper/2110.00447/) (2021)
- [A Universal Framework for Generalized Run Time Assurance with JAX Automatic Differentiation](https://mlsanity.com/paper/2209.01120/) (2022)
- [Verse: A Python library for reasoning about multi-agent hybrid system scenarios](https://mlsanity.com/paper/2301.08714/) (2023)

## Abstract (arXiv)

A runtime assurance system (RTA) for a given plant enables the exercise of an untrusted or experimental controller while assuring safety with a backup (or safety) controller. The relevant computational design problem is to create a logic that assures safety by switching to the safety controller as needed, while maximizing some performance criteria, such as the utilization of the untrusted controller. Existing RTA design strategies are well-known to be overly conservative and, in principle, can lead to safety violations. In this paper, we formulate the optimal RTA design problem and present a new approach for solving it. Our approach relies on reward shaping and reinforcement learning. It can guarantee safety and leverage machine learning technologies for scalability. We have implemented this algorithm and present experimental results comparing our approach with state-of-the-art reachability and simulation-based RTA approaches in a number of scenarios using aircraft models in 3D space with complex safety requirements. Our approach can guarantee safety while increasing utilization of the experimental controller over existing approaches.