# Adaptive Shielding under Uncertainty

arXiv: https://arxiv.org/abs/2010.03842 · HTML: https://mlsanity.com/paper/2010.03842/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2020.

## Built on by (most cited first, 18 of 18)

- [Online Shielding for Reinforcement Learning](https://mlsanity.com/paper/2212.01861/) (2022)
- [Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates](https://mlsanity.com/paper/2405.14058/) (2024)
- [Shielding Atari Games with Bounded Prescience](https://mlsanity.com/paper/2101.08153/) (2021)
- [Search-Based Testing of Reinforcement Learning](https://mlsanity.com/paper/2205.04887/) (2022)
- [Verification-Guided Shielding for Deep Reinforcement Learning](https://mlsanity.com/paper/2406.06507/) (2024)
- [Dynamic Shielding for Reinforcement Learning in Black-Box Environments](https://mlsanity.com/paper/2207.13446/) (2022)
- [Do Androids Dream of Electric Fences? Safety-Aware Reinforcement Learning with Latent Shielding](https://mlsanity.com/paper/2112.11490/) (2021)
- [Automata Learning meets Shielding](https://mlsanity.com/paper/2212.01838/) (2022)
- [Safety Shielding under Delayed Observation](https://mlsanity.com/paper/2307.02164/) (2023)
- [Neural Control and Certificate Repair via Runtime Monitoring](https://mlsanity.com/paper/2412.12996/) (2024)
- [Robust Probabilistic Shielding for Safe Offline Reinforcement Learning](https://mlsanity.com/paper/2605.10293/) (2026)
- [Shields to Guarantee Probabilistic Safety in MDPs](https://mlsanity.com/paper/2605.10888/) (2026)
- [Safety through Permissibility: Shield Construction for Fast and Safe Reinforcement Learning](https://mlsanity.com/paper/2405.19414/) (2024)
- [Human-aligned Safe Reinforcement Learning for Highway On-Ramp Merging in Dense Traffic](https://mlsanity.com/paper/2503.02624/) (2025)
- [Efficient Dynamic Shielding for Parametric Safety Specifications](https://mlsanity.com/paper/2505.22104/) (2025)
- [Adaptive GR(1) Specification Repair for Liveness-Preserving Shielding in Reinforcement Learning](https://mlsanity.com/paper/2511.02605/) (2025)
- [Explainably Safe Reinforcement Learning](https://mlsanity.com/paper/2606.04634/) (2026)
- [Adaptive Probabilistic Shielding by Learning MDPs for Safe Reinforcement Learning](https://mlsanity.com/paper/2608.19836/) (2026)

## Abstract (arXiv)

This paper targets control problems that exhibit specific safety and performance requirements. In particular, the aim is to ensure that an agent, operating under uncertainty, will at runtime strictly adhere to such requirements. Previous works create so-called shields that correct an existing controller for the agent if it is about to take unbearable safety risks. However, so far, shields do not consider that an environment may not be fully known in advance and may evolve for complex control and learning tasks. We propose a new method for the efficient computation of a shield that is adaptive to a changing environment. In particular, we base our method on problems that are sufficiently captured by potentially infinite Markov decision processes (MDP) and quantitative specifications such as mean payoff objectives. The shield is independent of the controller, which may, for instance, take the form of a high-performing reinforcement learning agent. At runtime, our method builds an internal abstract representation of the MDP and constantly adapts this abstraction and the shield based on observations from the environment. We showcase the applicability of our method via an urban traffic control problem.