###### Abstract  
This study assesses the impact of incorporating an adaptive learning mechanism into an agent-based model simulating behavior on a university campus during a pandemic outbreak, with the particular case of the COVID-19 pandemic. Our model not only captures individual behavior, but also serves as a powerful tool for assessing the efficacy of geolocalized policies in addressing campus overcrowding and infections. The main objective is to demonstrate RL's effectiveness in representing agent behavior and optimizing control policies through adaptive decision-making in response to evolving pandemic dynamics. By implementing RL, we identify different temporal patterns of overcrowding violations, shedding light on the complexity of human behavior within semi-enclosed environments. While we successfully reduce campus overcrowding, the study recognizes its limited impact on altering the pandemic's course, underlining the importance of comprehensive epidemic control strategies. Our research contributes to the understanding of adaptive learning in complex systems and provides insights for shaping future public health policies in similar community settings. It emphasizes the significance of considering individual decision-making influenced by adaptive learning, implementing targeted interventions, and the role of geospatial elements in pandemic control. Future research directions include exploring various parameter settings and updating representations of the disease's natural history to enhance the applicability of these findings. This study offers valuable insights into managing pandemics in community settings and highlights the need for multifaceted control strategies. agent-based modeling and simulation; reinforcement learning; COVID-19; social simulation +
Footnote †: journal: Medical  
the unique challenges these environments pose. They emphasize the complex social networks and the potential for asymptomatic spread among students. Their findings reveal that, under plausible conditions, a significant proportion of students could be infected without additional control measures, especially in communal living spaces like residences. This demonstrates that simply adopting general strategies might not be sufficient. Instead, targeted measures, such as geolocalized policies, become crucial. [PERSON] et al. [2] demonstrate that controlling disease transmission within universities requires a comprehensive approach that considers not only general guidelines, but also specific patterns of movement and interaction within the campus. Similarly, [PERSON] and [PERSON] [3] investigate the impact of COVID-19 on individual well-being by analyzing nearly 25 million pandemic-related tweets from 20 countries and 28 U.S. states over a month. While their study does not specifically target university campuses, they underscore the importance of government actions, such as lockdowns and social distancing measures, in response to the pandemic. The study's unique focus on geolocalized analysis suggests that this approach could aid policymakers in understanding the effects of interventions, with an emphasis on the increased use of Twitter during the pandemic. The authors propose that this methodology could assist policymakers in analyzing and monitoring the impact of interventions on the general population. In addition, [PERSON] et al. [4] demonstrate the role of individual human mobility in shaping the spatiotemporal dynamics of infectious disease transmission. [PERSON] et al. [4] focuses on the role of individual human mobility in shaping the spatiotemporal dynamics of infectious disease transmission. The study integrates a classical susceptible, exposed, infectious, and recovered (SEIR) individual-based model with an individual human mobility model, considering factors such as transition probabilities between localities, gravity-like components, and memory aspects.