Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning

Zhou Yang, Long Nguyen, Jiazhen Zhu, Zhenhe Pan, Jia Li, Fang Jin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Ahstract-A crucial and time-sensitive task when any disaster occurs is to rescue victims and distribute resources to the right groups and locations. This task is challenging in populated urban areas, due to a huge burst of help requests made in a very short period. 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 a rapid deployment of volunteers to rescue victims in dynamic settings. The core concept is to quickly identify victims and volunteers from social network data and then schedule rescue parties with an adaptive learning algorithm. This framework performs two key functions: 1) identify trapped victims and volunteers, and 2) optimize the volunteers' rescue strategy in a complex time-sensitive environment. The proposed ResQ algorithm can speed up the training processes through a heuristic function which reduces the state-Action space by identifying a set of particular actions over others. Experimental results showed that the proposed heuristic multi-Agent reinforcement learning based scheduling outperforms several state-of-Art methods, in terms of both reward rate and response times.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
EditorsMartin Atzmuller, Michele Coscia, Rokia Missaoui
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages565-572
Number of pages8
ISBN (Electronic)9781728110561
DOIs
StatePublished - Dec 7 2020
Event12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 - Virtual, Online, Netherlands
Duration: Dec 7 2020Dec 10 2020

Publication series

NameProceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

Conference

Conference12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
CountryNetherlands
CityVirtual, Online
Period12/7/2012/10/20

Fingerprint Dive into the research topics of 'Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning'. Together they form a unique fingerprint.

Cite this