## 3 Results
### Adaptive Learning Integration with ABMS
This approach considers spatial factors and individual behaviors, leading to more precise and context-specific predictions. It is essential to emphasize that the complexity of our ABMS is a deliberate choice made to ensure a faithful representation of real-world dynamics. The detailed elements of the model are indispensable for capturing the subtle and location-specific behaviors of individuals within a university campus during a pandemic. The geospatial dimension is intrinsically linked with human behavior, a central component of our ABM. As individuals traverse the campus, their behaviors are influenced by various factors, such as crowd density, proximity to others, and personal experiences. Our model incorporates these aspects by empowering agents to make decisions specific to their locations based on their perceptions of the environment and the evolving pandemic conditions. In the context of the COVID-19 pandemic, simpler models like the SEIR model may not adequately account for the impacts of interventions such as social distancing and mask-wearing. These effects can vary significantly depending on the local context and individual behaviors. Our ABMS, with its explicit modeling of these factors, serves as a more effective tool for predicting disease spread and assessing the consequences of various interventions. The comprehensive structure of our ABMS is indispensable for capturing the variabilities in individual behaviors, the environment, and the disease transmission process, all of which significantly influence the dynamics of the epidemic. These intricacies are pivotal for comprehending disease spread and devising effective intervention strategies. Geolocalized policies are fundamental for informed decision-making. University administrators and public health officials require detailed insights into the density of people within specific campus areas to implement targeted interventions effectively. By considering the geospatial dimension and individual behaviors, our model provides a comprehensive outlook that enables policymakers to make more precise and context-aware decisions. The dynamic nature of the pandemic calls for a dynamic approach to policy implementation. Geolocalized policies account for the evolving situation, allowing authorities to adapt to changing conditions swiftly. Our ABMS demonstrates the usefulness of geospatially informed strategies, which can help mitigate the risk of disease transmission while minimizing disruptions to the normal functioning of the university. Our findings demonstrate that implementing an adaptive learning mechanism, mainly through Reinforcement Learning, is feasible. Our results align with previous research that has successfully applied adaptive learning techniques to optimize control policies during epidemics. This highlights the adaptability and effectiveness of Reinforcement Learning in representing how people make goal-oriented decisions in complex, semi-enclosed environments, such as a university campus during an epidemic. However, the results also provide insights into the subtle aspects of the impact of adaptive learning. While the adaptive learning mechanism led to meaningful reductions in campus crowding, the extent of this effect varied across scenarios. In some cases, agents maintained a considerable distance from one another, significantly exceeding the recommended physical distancing policy. However, in other instances, this effect was less pronounced. These variations emphasize the intricacies of human behavior and the challenges of precisely predicting outcomes within semi-enclosed communities like campuses. Moreover, our study revealed a temporal aspect of crowding violations. We observed that these violations predominantly occurred on Tuesdays during lunch hours. This temporal pattern underscores the necessity for targeted interventions on specific days and times to ensure adherence to social distancing measures. It is important to note that while our study successfully reduced campus crowding, it did not substantially alter the epidemic's course.