# Resilient Learning-Based Control Under Denial-of-Service Attacks

arXiv: https://arxiv.org/abs/2409.07766 · HTML: https://mlsanity.com/paper/2409.07766/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods.
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 12 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [Safe and constrained RL](https://mlsanity.com/deeprl/safe-and-constrained-rl/)

## Builds on (most cited first, 2 of 2)

- [Data-Driven Resilient Predictive Control under Denial-of-Service](https://mlsanity.com/paper/2110.12766/) (2021)
- [Robust Learning-based Predictive Control for Discrete-time Nonlinear Systems with Unknown Dynamics and State Constraints](https://mlsanity.com/paper/1911.09827/) (2019)

## Abstract (arXiv)

In this paper, we have proposed a resilient reinforcement learning method for discrete-time linear systems with unknown parameters, under denial-of-service (DoS) attacks. The proposed method is based on policy iteration that learns the optimal controller from input-state data amidst DoS attacks. We achieve an upper bound for the DoS duration to ensure closed-loop stability. The resilience of the closed-loop system, when subjected to DoS attacks with the learned controller and an internal model, has been thoroughly examined. The effectiveness of the proposed methodology is demonstrated on an inverted pendulum on a cart.