Dynamic resource allocation during natural disasters using multi-agent environment

Abstract

Natural disasters are devastating for a country and effective allocation of critical resources can mitigate the impact. While traditional approaches usually have difficulties in making optimal critical resource allocation, in this paper we introduce a novel hierarchical multi-agent reinforcement learning framework to model optimal resource allocation for natural disasters in real-time. On the lower level a set of agents navigate with the continuous time environment using deep reinforcement algorithms. On the higher level, a lead agent takes care of the global decision-making. Our framework achieves more efficient resource allocation in response to dynamic events and is applicable to problems where disaster evolves alongside the response efforts, where delays in response can lead to increased disaster severity and thus a greater need for resources.