  Natural disasters can cause substantial negative socio-economic impacts
around the world, due to mortality, relocation, rates, and reconstruction
decisions. Robotics has been successfully applied to identify and rescue
victims during the occurrence of a natural hazard. However, little effort has
been taken to deploy solutions where an autonomous robot can save the life of a
citizen by itself relocating it, without the need to wait for a rescue team
composed of humans. Reinforcement learning approaches can be used to deploy
such a solution, however, one of the most famous algorithms to deploy it, the
Q-learning, suffers from biased results generated when performing its learning
routines. In this research a solution for citizen relocation based on Partially
Observable Markov Decision Processes is adopted, where the capability of the
Double Q-learning in relocating citizens during a natural hazard is evaluated
under a proposed hazard simulation engine based on a grid world. The
performance of the solution was measured as a success rate of a citizen
relocation procedure, where the results show that the technique portrays a
performance above 100% for easy scenarios and near 50% for hard ones.
