# Secure Planning Against Stealthy Attacks via Model-Free Reinforcement Learning

arXiv: https://arxiv.org/abs/2011.01882 · HTML: https://mlsanity.com/paper/2011.01882/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Learning Optimal Strategies for Temporal Tasks in Stochastic Games](https://mlsanity.com/paper/2102.04307/) (2021)
- [Accelerated Learning with Linear Temporal Logic using Differentiable Simulation](https://mlsanity.com/paper/2506.01167/) (2025)

## Abstract (arXiv)

We consider the problem of security-aware planning in an unknown stochastic environment, in the presence of attacks on control signals (i.e., actuators) of the robot. We model the attacker as an agent who has the full knowledge of the controller as well as the employed intrusion-detection system and who wants to prevent the controller from performing tasks while staying stealthy. We formulate the problem as a stochastic game between the attacker and the controller and present an approach to express the objective of such an agent and the controller as a combined linear temporal logic (LTL) formula. We then show that the planning problem, described formally as the problem of satisfying an LTL formula in a stochastic game, can be solved via model-free reinforcement learning when the environment is completely unknown. Finally, we illustrate and evaluate our methods on two robotic planning case studies.