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

2025-09-24

AI Summary

Controlling large-scale active particle systems—such as crowd evacuation or robot swarms—faces challenges in scalability, robustness, and high per-agent control cost. Method: This paper proposes a leader-guided control strategy integrating reinforcement learning (RL) with artificial potential fields (APFs). It employs a generalized Vicsek model to characterize the dynamic coupling between leaders and the collective, uses RL to optimize leader trajectories, and leverages APFs for decentralized, individual-free swarm response. Contribution/Results: The approach significantly improves evacuation efficiency and control stability in hazardous scenarios: it reduces evacuation time by 23% and enhances robustness by 18% over a pure RL baseline. The implementation is open-sourced, and multi-scale simulations validate its scalability and generalization across system sizes and configurations.

Technology Category

• Multiagent Systems: Adversarial Agents
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Behavior Learning & Control

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Developing scalable control strategies for large active particle systems
• Overcoming individual agent control limitations in complex scenarios
• Creating robust evacuation strategies using leader-follower approaches

Innovation

• Reinforcement learning combined with artificial forces
• Generalized Vicsek model for leader guidance
• Leader-based control strategy for evacuation