# Inverting Self-Triggered Control: Adversarial Reinforcement Learning for Sparse Denial-of-Service Attacks

arXiv: https://arxiv.org/abs/2609.12016 · HTML: https://mlsanity.com/paper/2609.12016/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 10 Sep 2026.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 6 of 6)

- [Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2101.00292/) (2021)
- [Reinforcement Learning for Feedback-Enabled Cyber Resilience](https://mlsanity.com/paper/2107.00783/) (2021)
- [Analysis of Stochastic Switched Systems with Application to Networked Control Under Jamming Attacks](https://mlsanity.com/paper/1704.07647/) (2017)
- [Stabilization of Networked Control Systems under DoS Attacks and Output Quantization](https://mlsanity.com/paper/1709.08149/) (2017)
- [A Jointly Optimal Design of Control and Scheduling in Networked Systems under Denial-of-Service Attacks](https://mlsanity.com/paper/2103.05893/) (2021)
- [Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance](https://mlsanity.com/paper/2605.12561/) (2026)

## Abstract (arXiv)

Self-triggered reinforcement learning control (RL-STC) learns the sparsest control schedule that preserves Lyapunov-decreasing stability under a Run-Time Assurance (RTA) override. We invert this: an adversarial RL agent learns the sparsest jamming or Denial-of-Service (DoS) schedule that destabilizes the closed loop, with a Lyapunov-increase admissibility predicate mirroring the defender's safety certificate. We prove a plant-property lower bound on the minimum jam count required for an immediate hold-last medium-access-control adversary to force a crash against a self-triggered controller (STC) satisfying a Lyapunov contract, and recover a certificate-level analog of the consecutive-grouping optimality of prior count-budget DoS scheduling as a corollary. This extends the DoS-scheduling count-budget analysis from periodic and linear-time-invariant to STC controllers. Empirically, we train against four fixed defenders per plant (one Linear Quadratic Regulator (LQR) and three RL-STC) on Pendulum, CartPole, and Quadrotor2D. The learned adversary is the only adversary that crashes every defender on every plant at 100%: greedy misses Quadrotor2D LQR on 42% of episodes and periodic misses Pendulum LQR on 97%. On jam-time-per-failure it beats baselines by up to 2.8×, and shows its widest absolute margin on Quadrotor2D LQR. Robustness ablations show that Gaussian observation noise exceeding the initial-state magnitude and position-only observation both preserve 100% failure rate and keep the learned adversary strictly ahead of both baselines on jam-time-per-failure.