TY - GEN
T1 - Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning
AU - Yang, Zhou
AU - Nguyen, Long
AU - Zhu, Jiazhen
AU - Pan, Zhenhe
AU - Li, Jia
AU - Jin, Fang
N1 - Funding Information:
VI. ACKNOWLEDGMENT This work was supported by the U.S. National Science Foundation (NSF) under Grant CNS-1737634.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/7
Y1 - 2020/12/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85103693675&partnerID=8YFLogxK
U2 - 10.1109/ASONAM49781.2020.9381416
DO - 10.1109/ASONAM49781.2020.9381416
M3 - Conference contribution
AN - SCOPUS:85103693675
T3 - Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
SP - 565
EP - 572
BT - Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
A2 - Atzmuller, Martin
A2 - Coscia, Michele
A2 - Missaoui, Rokia
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 December 2020 through 10 December 2020
ER -