# Distributed Secure Learning Control for Large-scale Multirobots under Stealthy Actuator Attacks

arXiv: https://arxiv.org/abs/2609.06896 · HTML: https://mlsanity.com/paper/2609.06896/

## 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 7 of its 49 references that are in the map.
- First posted on arXiv 7 Sep 2026.

## Placed in

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

## Builds on (most cited first, 7 of 7)

- [Distributed Reinforcement Learning for Decentralized Linear Quadratic Control: A Derivative-Free Policy Optimization Approach](https://mlsanity.com/paper/1912.09135/) (2019)
- [Policy Search for Model Predictive Control with Application to Agile Drone Flight](https://mlsanity.com/paper/2112.03850/) (2021)
- [Distributed Learning of Decentralized Control Policies for Articulated Mobile Robots](https://mlsanity.com/paper/1901.08537/) (2019)
- [Robust Multi-Robot Active Target Tracking Against Sensing and Communication Attacks](https://mlsanity.com/paper/2109.09838/) (2021)
- [Toward Scalable Multirobot Control: Fast Policy Learning in Distributed MPC](https://mlsanity.com/paper/2412.19669/) (2024)
- [Intelligent Physical Attack Against Mobile Robots With Obstacle-Avoidance](https://mlsanity.com/paper/1910.06461/) (2019)
- [Resilient Time-Varying Formation Tracking for Mobile Robot Networks under Deception Attacks on Positioning](https://mlsanity.com/paper/2110.10678/) (2021)

## Abstract (arXiv)

Distributed learning control for multirobot systems (MRS) offers significant flexibility in presence of uncertainties but lacks provable performance guarantees. A promising direction involves integrating reinforcement learning (RL) into distributed model predictive control (DMPC), leveraging the strengths of RL in nonlinear policy design and the receding-horizon replanning capabilities of DMPC. However, ensuring secure control within such a learning framework under malicious cyber attacks, particularly stealthy ones, remains a critical challenge, because the distributed policies generation depends on information exchange among neighbors, where compromised agents can rapidly influence the behavior of others through the communication network. This article proposes a distributed secure learning control (DSLC) framework for large-scale MRS under malicious, stealthy actuator attacks. Our framework offers two key features: (i) a unified approach that enables secure learning control across various coordination scenarios and (ii) a game-theoretic distributed learning-based predictive control strategy that learns how to balance the attacker and defender through a differential-game based DMPC framework. Specifically, DSLC employs a distributed attacker-actor-critic architecture to learn the optimal defense and attack policies online within each prediction interval. Unlike numerical optimization-based controllers that calculate open-loop control sequences, our method simultaneously generates adversarial attack policies and corresponding defense policies in analytical closed-loop form. The defense policies could be directly generalized to MRS with varying scales and diverse actuator attack probabilities. The effectiveness and scalability of DSLC are validated through comprehensive simulations and real-world experiments in multiple wheeled robots via various control tasks.