Abstract
Disaster mitigation of severe catastrophic events depend heavily on effective decisions that are made by officials. The goal of disaster management is to make decisions that properly reallocate and redistribute the scarce resources produced by the available interconnected-critical infrastructures (CI's). This paper investigates the application of Monte Carlo (MC)-based policy estimation in reinforcement learning (RL) to mount up experience from a massive number of simulations. This method, in conjunction with an optimised set of RL parameters, will help the RL agent to explore and exploit those trajectories that lead to an optimum result in a reasonable time. It shows that a learning agent using MC estimation policy, through interactions with an environment of simulated disastrous scenarios (i2Sim-infrastrucuture interdependency simulator) is capable of making informed decisions for complex systems in a timely manner.