Advances in Multi-Agent Systems for Emergency Response and Environmental Monitoring

The field of multi-agent systems is experiencing significant growth, driven by the increasing demand for effective emergency response and environmental monitoring solutions. Recent research has focused on developing innovative multi-agent reinforcement learning approaches to tackle complex challenges in these areas. These approaches have shown great promise in enhancing the efficiency and effectiveness of emergency response operations, such as search and rescue missions, and environmental monitoring tasks, like plume tracing and pollution source localization. Notably, the integration of large language models and hierarchical reinforcement learning frameworks has improved the scalability and robustness of multi-agent systems in dynamic environments. Some particularly noteworthy papers in this area include:

  • A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue, which proposes a novel algorithm for optimizing task allocation among heterogeneous agents in emergency rescue scenarios.
  • Scalable UAV Multi-Hop Networking via Multi-Agent Reinforcement Learning with Large Language Models, which presents a framework for establishing robust emergency communication networks using UAVs and large language models.

Sources

3D Characterization of Smoke Plume Dispersion Using Multi-View Drone Swarm

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue

Scalable UAV Multi-Hop Networking via Multi-Agent Reinforcement Learning with Large Language Models

Multi-source Plume Tracing via Multi-Agent Reinforcement Learning

Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning

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