Disaster management in real time simulation using machine learning

A series of carefully chosen decisions by an Emergency Responder during a disaster are vital in mitigating the loss of human lives and the recovery of critical infrastructures. In this paper we propose to assist a human Emergency Responder by modeling and simulating an intelligent agent using Reinforcement Learning. The goal of the agent will be to maximize the number of patients discharged from hospitals or on-site emergency units. It is suggested that by exposing such an intelligent agent to a large sequence of simulated disaster scenarios, the agent will capture enough experience and knowledge to enable it to select those actions which mitigate damage and casualties. This paper describes early results of our work that indicate that the use of Q-learning can successfully train an agent to make good choices, during a simulated disaster.