### Adaptive Learning in ABMS
In the transmission process, our assumptions regarding the probabilities governing the spread of COVID-19 across the campus are derived from the existing literature, which is based on well-established research and forms the foundation for modeling how the virus propagates throughout the university campus. Conversely, for capturing human behavior within the campus, particularly daily campus routines, we draw from a combination of sources. Our approach incorporates expert opinions, data obtained from the university, and our comprehensive knowledge of the system. This multifaceted approach ensures that our model realistically represents how individuals conduct their daily activities on campus during a pandemic. The primary objective of our model is to simulate a simplified yet informative portrayal of the COVID-19 progression, drawing inspiration from the Susceptible, Exposed, Infected, Recovered, Discharged (SEIRD)-based epidemic behavior observed in traditional models. Our model enriches this representation by incorporating geospatial characteristics of the campus, enabling us to assess and evaluate georeferenced strategies to prevent COVID-19 transmission effectively within the campus environment. We implemented a comprehensive experimental design that includes essential statistical analyses, such as ANOVA tables and confidence interval assessments. Additionally, in this study, we employ Reinforcement Learning (RL) as a potent tool to optimize control policies within our ABMS of a university campus during a pandemic. RL allows us to establish an adaptive learning mechanism wherein agents within the model acquire the ability to make decisions based on their interactions with the environment. Specifically, RL enables us to identify and implement control policies that encourage individuals on campus to adhere to social distancing and other preventive measures. RL agents learn from the consequences of their actions, such as maintaining physical distance, wearing masks, or modifying their behavior in response to the evolving pandemic conditions. Over time, the RL agents fine-tune their strategies to maximize the objective, often focused on minimizing infections or overcrowding. This adaptive learning process is critical for optimizing control policies because it accounts for the dynamic nature of the pandemic and individual behavior. RL agents can adapt their responses based on changing circumstances, thereby improving the effectiveness of control measures. Through RL, our aim is to provide a clear demonstration of how specific control policies can be identified and applied in real-world scenarios, offering valuable insights into pandemic control within university campuses and similar semi-enclosed environments. Our research yields significant results; we establish that RL is a robust and practical approach for effectively modeling agent behavior within the complex dynamics of a university campus during a pandemic. Furthermore, our research uncovers specific temporal patterns related to overcrowding violations, offering insights into the nuanced nature of human behavior within semi-enclosed communities. Lastly, although our adaptive learning mechanism successfully mitigated campus crowding, its impact on altering the course of the epidemic was limited, underscoring the need for comprehensive pandemic control strategies that account for individual decision-making and emphasizing the importance of targeted interventions. The remainder of the paper is organized as follows. First, it presents the materials and methods, beginning with a thorough model description that outlines the core aspects of our research approach. Following that, we delve into the input analysis, providing insights into how we prepared and handled the data for our study. Then, we provide details on the model implementation, explaining the technical aspects of how ABMS and adaptive learning techniques were integrated.