# Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning

arXiv: https://arxiv.org/abs/1811.05010 · HTML: https://mlsanity.com/paper/1811.05010/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2018.

## Built on by (most cited first, 5 of 5)

- [Deep Learning based Multi-Label Image Classification of Protest Activities](https://mlsanity.com/paper/2301.04212/) (2023)
- [Routing algorithms as tools for integrating social distancing with emergency evacuation](https://mlsanity.com/paper/2103.03413/) (2021)
- [Factorized Deep Q-Network for Cooperative Multi-Agent Reinforcement Learning in Victim Tagging](https://mlsanity.com/paper/2503.00684/) (2025)
- [Public Health in Disaster: Emotional Health and Life Incidents Extraction during Hurricane Harvey](https://mlsanity.com/paper/2408.11133/) (2024)
- [Discovering Transmission Dynamics of COVID-19 in China](https://mlsanity.com/paper/2512.22787/) (2025)

## Abstract (arXiv)

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 the huge burst of help requests generated 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 the 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 rescue 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 the 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.