Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning

no code implementations • 12 Nov 2018 • Long Nguyen, Zhou Yang, Jiazhen Zhu, Jia Li, Fang Jin

To improve the efficiency of the emergency response in the immediate aftermath of a disaster, we propose a heuristic multi-agent reinforcement learning scheduling algorithm, named as ResQ, which can effectively schedule the rapid deployment of volunteers to rescue victims in dynamic settings.

Multi-agent Reinforcement Learning reinforcement-learning +2