Urban Emergency Rescue Based on Multi-Agent Collaborative Learning: Coordination Between Fire Engines and Traffic Lights

TL;DR

This paper introduces a simulation framework using collaborative learning techniques within Unity to optimize urban emergency rescue operations involving fire engines and traffic lights.

Contribution

It presents a flexible simulation framework integrating collaborative learning for urban emergency rescue, enabling realistic evaluation of multi-agent coordination strategies.

Findings

Framework effectively simulates emergency rescue scenarios

Supports flexible configurations for agents and strategies

Facilitates evaluation of collaborative learning in urban emergencies

Abstract

Nowadays, traffic management in urban areas is one of the major economic problems. In particular, when faced with emergency situations like firefighting, timely and efficient traffic dispatching is crucial. Intelligent coordination between multiple departments is essential to realize efficient emergency rescue. In this demo, we present a framework that integrates techniques for collaborative learning methods into the well-known Unity Engine simulator, and thus these techniques can be evaluated in realistic settings. In particular, the framework allows flexible settings such as the number and type of collaborative agents, learning strategies, reward functions, and constraint conditions in practice. The framework is evaluated for an emergency rescue scenario, which could be used as a simulation tool for urban emergency departments.