A deep reinforcement learning based decision-making approach for avoiding crowd situation within the case of Covid'19 pandemic.

Abstract

Individuals' flow's fluidifcation in the same way as the thinning of the population's concentration remains among major concerns within the context of the pandemic crisis situations. The recent COVID‐19 pandemic crisis is a typical example of the aforementioned where on despite of the containment phases that radically isolate the population but are not applicable persistently, people have to adapt their behavior to new daily‐life situations tempering Individuals' stream, avoiding tides, and watering down population's concentration. Crowd evacuation is one of the well‐known research domains that can play a pertinent role to face the challenge of the COVID‐19 pandemic. In fact, considering the population's concentration thinning within the slant of the “crowd evacuation” paradigm allows managing the flow of the population, and consequently, decreasing the probable number of infected cases. In other words, crowd evacuation modeling and simulation with the aim of better‐exploiting individuals' flow allow the study and analysis of different possible outcomes for designing population's concentration thinning strategies. In this article, a new decision‐making approach is proposed in order to cope with the aforesaid challenges, which relies on an independent Deep Q Network with an improved SIR model (IDQN‐I‐SIR). The machine‐learning component (i.e., IDQN) is in charge of the agent's movements control and I‐SIR (improved “susceptible‐infected‐recovered” individuals) model is responsible to control the virus spread. We demonstrate the effectiveness of IDQN‐I‐SIR through a case‐study of individuals' flow's management with infected cases' avoidance in an emergency department (often overcrowded in context of a pandemic crisis).