# Online Shielding for Stochastic Systems

arXiv: https://arxiv.org/abs/2012.09539 · HTML: https://mlsanity.com/paper/2012.09539/

## Key facts

- Cited by 11 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 11 of 11)

- [Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking](https://mlsanity.com/paper/2205.06750/) (2022)
- [Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopes](https://mlsanity.com/paper/2210.10691/) (2022)
- [Online Shielding for Reinforcement Learning](https://mlsanity.com/paper/2212.01861/) (2022)
- [Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments](https://mlsanity.com/paper/2205.06311/) (2022)
- [Shielding Atari Games with Bounded Prescience](https://mlsanity.com/paper/2101.08153/) (2021)
- [Correct-by-Construction Runtime Enforcement in AI -- A Survey](https://mlsanity.com/paper/2208.14426/) (2022)
- [Automata Learning meets Shielding](https://mlsanity.com/paper/2212.01838/) (2022)
- [Safety Shielding under Delayed Observation](https://mlsanity.com/paper/2307.02164/) (2023)
- [Safe Reinforcement Learning via Recovery-based Shielding with Gaussian Process Dynamics Models](https://mlsanity.com/paper/2602.12444/) (2026)
- [HAVA: Hybrid Approach to Value-Alignment through Reward Weighing for Reinforcement Learning](https://mlsanity.com/paper/2505.15011/) (2025)
- [Managing Action Preconditions in Neuro-Symbolic RL: Three Placement Strategies for Embodied Agents](https://mlsanity.com/paper/2609.16056/) (2026)

## Abstract (arXiv)

In this paper, we propose a method to develop trustworthy reinforcement learning systems. To ensure safety especially during exploration, we automatically synthesize a correct-by-construction runtime enforcer, called a shield, that blocks all actions that are unsafe with respect to a temporal logic specification from the agent. Our main contribution is a new synthesis algorithm for computing the shield online. Existing offline shielding approaches compute exhaustively the safety of all states-action combinations ahead-of-time, resulting in huge offline computation times, large memory consumption, and significant delays at run-time due to the look-ups in a huge database. The intuition behind online shielding is to compute during run-time the set of all states that could be reached in the near future. For each of these states, the safety of all available actions is analysed and used for shielding as soon as one of the considered states is reached. Our proposed method is general and can be applied to a wide range of planning problems with stochastic behavior. For our evaluation, we selected a 2-player version of the classical computer game SNAKE. The game requires fast decisions and the multiplayer setting induces a large state space, computationally expensive to analyze exhaustively. The safety objective of collision avoidance is easily transferable to a variety of planning tasks.