How can AI coordinate fire engines and traffic lights?
Urban Emergency Rescue Based on Multi-Agent Collaborative Learning: Coordination Between Fire Engines and Traffic Lights
This paper explores coordinating fire trucks and traffic lights during emergencies using multi-agent reinforcement learning (MARL) within a Unity simulator. A QMIX-based algorithm allows fire trucks and traffic lights to learn coordinated strategies, optimizing for fast emergency response while minimizing collisions and adhering to traffic rules. A reward function encourages goal achievement, collision avoidance, and efficient time management. The open-source simulation environment facilitates research on multi-agent systems for smart city traffic management. Key to LLM-based multi-agent development is the focus on decentralized execution of learned strategies following centralized training, illustrating how LLMs can be employed to enhance agent decision-making in distributed web environments. The system's reliance on a well-defined reward function and the observation of agents' interactions offers insight into how to structure incentives and data exchange in LLM-driven multi-agent applications.