id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-184424-t8yhu7p8	Miralles-Pechu'an, Luis	A Deep Q-learning/genetic Algorithms Based Novel Methodology For Optimizing Covid-19 Pandemic Government Actions	2020-05-15		.txt	text/plain	7039	397	58	In this paper, we use the SEIR epidemiological model (Susceptible Exposed Infected Recovered) for infectious diseases to represent the evolution of the virus COVID-19 over time in the population. The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system focused on meeting two objectives: firstly, getting few people infected so that hospitals are not overwhelmed with critical patients, and secondly, avoiding taking drastic measures for too long which can potentially cause serious damage to the economy. In this section, we describe the state of the art of the essential components of our methodology: The SEIR model that simulates the spread of the COVID-19 [11] in the population, and two other techniques implemented to discover the best actions for combating the pandemic according to the goals of each government, DQL [12] and GA [10] .	./cache/cord-184424-t8yhu7p8.txt	./txt/cord-184424-t8yhu7p8.txt