# An effective control of large systems of active particles: An application to evacuation problem

arXiv: https://arxiv.org/abs/2509.19972 · HTML: https://mlsanity.com/paper/2509.19972/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 74 references that are in the map.
- First posted on arXiv 24 Sep 2025.

## Builds on (most cited first, 16 of 16)

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- [Invisible control of self-organizing agents leaving unknown environments](https://mlsanity.com/paper/1504.04064/) (2015)
- [Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment](https://mlsanity.com/paper/1905.09673/) (2019)
- [Event-Triggered Communication and Control of Networked Systems for Multi-Agent Consensus](https://mlsanity.com/paper/1712.00429/) (2017)
- [Cooperative Control of Linear Multi-Agent Systems via Distributed Output Regulation and Transient Synchronization](https://mlsanity.com/paper/1406.0085/) (2014)
- [Umbrella Reinforcement Learning -- computationally efficient tool for hard non-linear problems](https://mlsanity.com/paper/2411.14117/) (2024)

## Abstract (arXiv)

Manipulation of large systems of active particles is a serious challenge across diverse domains, including crowd management, control of robotic swarms, and coordinated material transport. The development of advanced control strategies for complex scenarios is hindered, however, by the lack of scalability and robustness of the existing methods, in particular, due to the need of an individual control for each agent. One possible solution involves controlling a system through a leader or a group of leaders, which other agents tend to follow. Using such an approach we develop an effective control strategy for a leader, combining reinforcement learning (RL) with artificial forces acting on the system. To describe the guidance of active particles by a leader we introduce the generalized Vicsek model. This novel method is then applied to the problem of the effective evacuation by a robot-rescuer (leader) of large groups of people from hazardous places. We demonstrate, that while a straightforward application of RL yields suboptimal results, even for advanced architectures, our approach provides a robust and efficient evacuation strategy. The source code supporting this study is publicly available at: https://github.com/cinemere/evacuation.