Deep Q-Network (DQN): Reinforcement Learning Based Approach for Secure Social Distancing Adherence with SARS-CoV-2 in Public Places

Abstract

The estimates taken far and wide to deal with the SARS-CoV-2 pandemic, limiting travel, shuttering superfluous organizations and implementing all social separating arrangements, are having serious monetary consequences. a noteworthy decrease in economic action spread over the economy the world, lasting in excess of a few months, typically clear in genuine GDP. Where it is formally announced a downturn. To quicken a strong expected recuperation with rising protectionism and unilateralism. There is a requirement for individuals to come out and face the circumstance. Despite the fact that it is established that separating individuals and investigating their contacts would be inadequate to control the SARS-CoV-2 pandemic, in light of the fact that there would be an excess of deferral between the beginning of indications and seclusion. Consequently, in these sorts of conditions it is to keep people groups from infection influence and early anticipation of these tainted individuals may prompt re development the economy too. We built up a numerical model utilizing profound Deep reinforcement learning (DRL) which is poised to revolutionize the field of artificial intelligence and the use of central algorithms in deep RL, specifically the deep Q-network (DQN), trust region policy optimization (TRPO). The proposed astute checking framework can be utilized as a reciprocal apparatus to be introduced at better places and consequently screen individuals receiving the security rules. With these prudent estimations, people will have the option to win this battle against SARS-CoV-2.